Journal Sciences News
World Science and Technology
June 2018
Permutation flowshop scheduling with time lag constraints and makespan criterion
Publication date: June 2018
Source:Computers & Industrial Engineering, Volume 120 Author(s): Bailin Wang, Kai Huang, Tieke Li A permutation flowshop with time lag constraints requires that the time lag between consecutive operations of a job must be in the given interval. In this study, the scheduling of such flowshop with makespan minimization objective is investigated. We prove that this problem has the reversibility property, and present a two-stage constructive heuristic with time complexity O(n 2 m), where n and m are the numbers of jobs and machines, respectively. The first stage generates a rank of jobs based on an equivalent formulation of the makespan, and the second stage constructs a schedule through an insertion mechanism. We apply the reversibility property to reduce the time complexity of insertion procedures in both two stages, and find a better solution by solving both the original problem and its reversed problem. To verify the effectiveness and efficiency of this heuristic, we compare its performance to the mixed integer linear programming (MILP) formulation of this problem for small-scale problems, and conduct a comprehensive computational study on the Taillard’s benchmark.
June 2018
Dynamic control of intelligent parking guidance using neural network predictive control
Publication date: June 2018
Source:Computers & Industrial Engineering, Volume 120 Author(s): Jong-Ho Shin, Hong-Bae Jun, Jae-Gon Kim The parking problem is a very important issue in city life because many citizens waste a large amount of energy and time trying to find suitable parking lots. To resolve this problem, various intelligent parking guidance systems have been introduced. However, the method of operating an intelligent parking guidance system remains in the infant stage. For successful operation, it is important to develop an effective method that assesses and selects the best parking lot in a real-time environment. In this vein, this study proposes a neural network-based predictive control approach that finds suitable weights for multiple factors dynamically so that the best performance of the intelligent parking guidance system can be achieved. The proposed method can enhance the performance of an intelligent parking guidance system via dynamic control in selecting the best parking lot. To evaluate the proposed approach, simulation tests and comparison with a traditional model have been conducted. As a result, the proposed approach provides a robust solution in an efficient manner under diverse parking environments. With the proposed approach, from the public interest viewpoint, the car parking problem can be approached more effectively.
June 2018
A No Speeds and Coefficients PSO approach to reliability optimization problems
Publication date: June 2018
Source:Computers & Industrial Engineering, Volume 120 Author(s): George Anescu, Anatoli Paul Ulmeanu Nowadays we assist to the global extension of reliability optimization problems from the design phase of systems and sub-systems to the design and operational phases, not only of systems and sub-systems, but also of complex industrial plants. Essentially the reliability optimization problems are dealing with the fine trade-off between two contradictory requirements, the maximization of system’s reliability and the minimization of system’s cost. The paper is investigating the suitability of the No Speeds and Coefficients Particle Swarm Optimization (NSC-PSO) method for solving reliability optimization problems by approaching a set of test problems comprising two known Redundancy Allocation Problem (RAP) case studies, one Fault Tree Optimization (FTO) case study and one Event Tree Optimization (ETO) case study. The FTO and ETO case studies have only illustrative purposes, while the RAP case studies are used for comparison between the numerical results produced by NSC-PSO method and the numerical results reported by other optimization methods published in the literature. The comparisons prove that NSC-PSO is a competitive optimization method for solving reliability optimization problems. The NSC-PSO algorithm is one of the robust optimization methodologies in the procedure of solving the reliability optimization problems.
June 2018
The value of information in supply chain decisions: A review of the literature and research agenda
Publication date: June 2018
Source:Computers & Industrial Engineering, Volume 120 Author(s): Nguyen Quoc Viet, Behzad Behdani, Jacqueline Bloemhof The purpose of this paper is to provide a structured overview of the value of information in different supply chain decisions and to identify a research agenda based on the current state of research on the topic. The paper uses the systematic literature review methodology to review journal articles published in the 12-year period from 2006 to 2017. Each selected study is analyzed using a rigorous review framework of four primary dimensions, including “supply chain decisions”, “information”, “modelling approach”, and “context”. The review of articles shows that the current literature is rich in assessing the value of information in inventory decisions, yet insufficient in other supply chain areas such as facility, transportation, sourcing, and pricing. In addition, the value of information is subject to contextual supply chain parameters and varies in accordance with the characteristics of the information (such as accuracy, timeliness, and completeness). Furthermore, the focus of the existing literature is on “information availability” in supply chain decisions, and the impact of important information characteristics on the value of information has not been studied extensively. The research on information timeliness and its influence on supply chain performance is especially limited. Based on the discussion and results of our review, a research agenda is offered and sample research questions are discussed.
May 2018
A two-stage stochastic optimization model for warehouse configuration and inventory policy of deteriorating items
Publication date: June 2018
Source:Computers & Industrial Engineering, Volume 120 Author(s): Yu-Siang Lin, Kung-Jeng Wang The optimal decision of global warehouse/hub deployment is critical to firm profitability. This study resolves the problems of deciding the optimal warehouse location for multiple markets and determining warehouse configuration design against stochastic demands. An appropriate inventory policy with owned and rented warehouses for deteriorating items is further designed. The proposed model maximizes the profit under rent warehouse incentives decreasing over time and price-sensitive demands. Furthermore, we propose a solution algorithm to solve the problem effectively. Sensitivity analysis is conducted to examine the effects of the parameters for the model of the algorithm.
May 2018
Novel methods for resource allocation in humanitarian logistics considering human suffering
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Lina Yu, Canrong Zhang, Huasheng Yang, Lixin Miao Deprivation cost is often used as a key economic metric of human suffering associated with emergency logistics. An improved approach is proposed for effective and equitable critical resource allocation within emergency logistics that considers human suffering by using this economic representation. A dynamic programming model is constructed for a multi-period resource allocation dispatch problem extracted to represent the disaster response phase, with special attention paid to the human suffering resulting from the delivery delay. Extensive numerical experiments are conducted to validate the computational performance and solution quality of the dynamic programming method. Based on the best solutions, an optimal delivery pattern with a cyclically sequenced feature is identified and its sufficient condition is provided as well. Furthermore, a piecewise linear method is proposed to accommodate large-scale instances that cannot be applied to the “Cyclic Delivery Approach”.
May 2018
Optimal Bayesian control policy for gear shaft fault detection using hidden semi-Markov model
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Xin Li, Viliam Makis, Hongfu Zuo, Jing Cai A new optimal Bayesian control approach is presented to predict early fault of a partially observable gear shaft system subject to deterioration and random failure. The gear shaft system deterioration process is modeled as a three-state continuous time hidden semi-Markov process. States 0 and 1 are unobservable and represent the good and warning system states, respectively. Only state 2 is assumed to be observable and represents the failure state. The general Erlang distribution is considered for modeling the sojourn time in each of the hidden states, which is closer to the actual deterioration process modeling of the gear shaft system than the exponential sojourn time distribution assumed in a hidden Markov model (HMM). The optimal maintenance policy represented by a multivariate Bayesian control scheme based on a hidden semi-Markov model (HSMM) is developed. The objective is to maximize the long-run expected average availability per unit time. An effective computational algorithm is designed in the semi-Markov decision process (SMDP) framework to obtain the optimal control limit and the optimal average availability. Using multidimensional data obtained from condition monitoring, the proposed approach can not only predict early fault occurrence of the gear shaft, but also update the remaining useful life (RUL) at each sampling epoch. A comparison with other maintenance policies is given, which illustrates the effectiveness of the proposed approach.
May 2018
Trade credit financing and coordination for an emission-dependent supply chain
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Erbao Cao, Man Yu Under carbon cap-and-trade mechanism, we investigate the interaction of financial decision and operational decision in an emission-dependent supply chain consisting of one manufacturer and one capital constrained retailer who funds her business by trade credit from the manufacturer. We find that the optimal ordering quantity is irrelevant to carbon emission cap under centralized decision. While under decentralized decision, the retailer purchases more products with less internal working capital and smaller carbon cap. We also find that the manufacturer prefers to cooperate with a medium rich retailer to earn more profit. Furthermore, a general contract (including quantity discount contract, revenue sharing contract and buyback contract) can coordinate the emission-dependent supply chain with a capital constrained retailer. When coordinating by a revenue sharing contract, the capital constrained retailer can earn more profit than the well-funded retailer. We further investigate the scenario when the retailer is loss aversion, and find that the retailer will order fewer products. Besides, under the revenue sharing contract, a higher loss aversion degree accompanies with a smaller profit sharing proportion, which implies that a manufacturer can earn more profit when cooperating with a loss aversion retailer.
May 2018
Synchronizing vehicles for multi-vehicle and one-cargo transportation
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Zhi-Hua Hu, Chen Wei In ship manufacturing and big-size cargo transportation, several flat vehicles are usually cooperated to transport one big-size ship segment or cargo, namely “multi-vehicle and one-cargo transportation” (MVOC). It is distinctly different from general transportation scenarios where a vehicle is used to load and transport several cargos, namely “one-vehicle and multi-cargo transportation” (OVMC). The MVOC generates the difficulty in synchronizing several vehicles for transporting a cargo. A mathematical program that can be solved by existing mixed-integer linear program solver is formulated, by considering the synchronization constraint among the unit-load flat vehicles under the minimization of makespan. To improve the computation performance of the solution methods, a sequential insert algorithm is developed as the basic procedure for a greedy insert algorithm and as the decoding scheme of a proposed genetic algorithm. These three methods (the mathematical program, the greedy insert algorithm and the genetic algorithm) are compared by numerical studies considering the effects of instance scales and synchronization complexity on the optimality and computation performances. The genetic algorithm performed better for solving small- and medium-scale instances with lower complexity, and the greedy insert algorithm is very fast and suitable for solving large-scale and complex instances.
May 2018
Pricing policies for a dual-channel retailer with cross-channel returns
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Mohannad Radhi, Guoqing Zhang Many retailers are adopting a dual-channel retailing strategy (DCRS) in which products are offered through two channels: physical stores and online stores. Due to regulations or competitive measures, such a strategy allows customers who find a purchase unsatisfactory to obtain a full refund through a same-channel return or a cross-channel return. No papers have collectively studied the aforementioned types of customer returns in a dual-channel context. This paper studies optimal pricing policies for a centralized and decentralized dual-channel retailer (DCR) with same- and cross-channel returns. How dual-channel pricing behavior is impacted by customer preference and rates of customer returns is discussed. It is found, through sensitivity analysis, that when a channel with significant customer preference faces a high rate of returns, decentralized channels generate a greater system profit for retailers than coordinated channels that have a unified pricing strategy. A DCR with a Stackelberg scheme has the proclivity to be more profitable when under the leadership of a channel with a high rate of returns and significant customer preference.
May 2018
Capacitated and multiple cross-docked vehicle routing problem with pickup, delivery, and time windows
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Azadeh Ahkamiraad, Yong Wang The vehicle routing problem (VRP) in cross-docked distribution networks is an important research topic in the supply chain management literature. This paper formulates a mixed integer linear programming model of a special type of capacitated and multiple cross-docked VRP with pickup, delivery, and time windows. The problem is to design a set of routes for vehicle fleets servicing pickup and delivery nodes with defined demands and time windows to achieve the minimum transportation and fixed costs. We propose a hybrid of the genetic algorithm and particle swarm optimization (HGP) to solve the formulated NP-hard problem. Small-size problems are solved by HGP and then compared with the exact method using CPLEX to validate the effectiveness of the proposed hybrid algorithm. Extensive experiments have been conducted for medium and large-size problems and the results show the proposed HGP provides better solutions in the allocated time compared to CPLEX.
May 2018
Integrated production-distribution planning problem in a competition-based four-echelon supply chain
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Hamed Rafiei, Fatemeh Safaei, Masoud Rabbani Integrated production-distribution (P-D) planning reduces costs, enhances the efficiency of the supply chain, and reduces additional proceedings because of its integrated parts and activities. P-D planning facilitates simultaneous planning of supply chain constituents in order to save more time in planning within a supply chain. Therefore, it plays an important role in supply chain management (SCM). Besides, since the introduction of competition into SCM, it has attracted the attention of many researchers and practitioners. The present work was conducted to investigate an integrated P-D planning problem within a four-echelon supply chain with two main objective functions: minimizing total chain cost and maximizing service level. The considered problem is modeled under two circumstances; no competition and competitive market. In the latter, three kinds of competitions are taken into account: Cournot, Stackelberg, and Quality competitions. To tackle the problem, two mixed integer linear programs (MILPs) are developed, which are proved to be unimodular. Moreover, elastic constraint method is used to solve the two multiobjective models. Finally, numerical experiments are conducted. The results showed that although competition improves chain performance in terms of quality of the delivered products, it might also raise the cost of the chain.
May 2018
Green logistics under imperfect production system: A Rough age based Multi-Objective Genetic Algorithm approach
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Manoranjan De, Barun Das, Manoranjan Maiti Imperfect economic production lot size (EPL) models are considered with time dependent defective rate. Here, defective production starts after the passage of some time from production commencement. Produced defective units are partially reworked and sold as fresh units. Under the environmental regulation, a cost (carbon tax) is charged by the government to mitigate global warming by reducing carbon emission (CE). Management also uses carbon trading when upper limit of carbon emission is given by the government. These costs bring a contradiction to a production manager. For more profit, if more production is decided, then CE and tax due to that are more. The models are formulated as single- and multi-objective profit maximization problems and solved using Rough age based single- and multi-objective Genetic Algorithms (RMOGAs). Numerical experiments are performed and graphical presentation of the results are depicted to illustrate the models. An algorithm with example for a firm management to achieve the maximum profit is also presented.
May 2018
Greedy heuristic algorithm for packing equal circles into a circular container
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Mao Chen, Xiangyang Tang, Ting Song, Zhizhong Zeng, Xicheng Peng, Sanya Liu This paper presents a greedy heuristic algorithm for solving the circle packing problem whose objective is to pack a set of unit circles into the smallest circular container. The proposed algorithm can be divided into two stages. In the first stage, a greedy packing procedure is introduced to determine whether the given set of circles can be packed into a fixed container. According to the greedy packing procedure, the circles are packed into the container one by one and each circle is packed into the container by a corner-occupying placement with maximal global benefit. In the second stage, the greedy packing procedure is embedded in a heuristic enumeration strategy to find the smallest container to accommodate all given circles. Tested on two sets of 20 public benchmark instances, the proposed algorithm achieves competitive results compared with existing algorithms in the literature. Furthermore, the effects of important parameter setting and essential components of the proposed algorithm are analyzed.
May 2018
A hybrid TLBO-TS algorithm for integrated selection and scheduling of projects
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Manish Kumar, M.L. Mittal, Gunjan Soni, Dheeraj Joshi Organizations dealing with projects often face the challenge of choosing right mix of projects for implementation and scheduling. The two steps have, however, traditionally been performed in sequential manner often resulting into problems such as scope changes/dropping/replacement/delayed completion of the projects. This paper deals with the problem of simultaneous selection and scheduling of the projects with maximization of total expected benefit of the portfolio as an objective. Expected benefit of the project is considered to be time sensitive. Further, the projects have a variety of interdependencies between them. Two types of interdependencies: mutual exclusiveness and complementariness have been considered. A zero-one integer programming model is proposed for the problem. Three meta-heuristics: TLBO, TS and hybrid TLBO-TS have been developed and compared with the existing algorithms in the literature. The performance of the algorithms is evaluated on the four different types of data sets. Performance of the hybrid TLBO-TS algorithm has been found to be quite promising in terms of solution quality and convergence.
May 2018
Emission reduction decision and coordination of a make-to-order supply chain with two products under cap-and-trade regulation
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Qingguo Bai, Jianteng Xu, Yingyu Zhang This paper incorporates carbon emission reduction into a make-to-order (MTO) supply chain with one upstream supplier that provides two types of raw materials and one downstream manufacturer that produces and sells two products. Emissions from the supply chain are the result of production activities, and the manufacturer invests in emission reduction technology to curb carbon emissions under cap-and-trade regulation. We first analyze the optimal operational decisions of the supplier and the manufacturer in the decentralized scenario and then test the supply chain’s performance by comparing its profit and carbon emissions with those in the centralized scenario. The results show that the supply chain’s profit in the decentralized scenario can be improved and that carbon emissions can be reduced. We propose a revenue- and investment-sharing contract to coordinate the supply chain and derive several conditions under which the contract is accepted by both members of the supply chain. A numerical study is conducted, and the theoretical results and impacts of several key carbon parameters on supply chain coordination are presented to inform management decisions.
May 2018
Efficient heuristics for minimizing weighted sum of squared tardiness on identical parallel machines
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Jeffrey Schaller, Jorge M.S. Valente Scheduling jobs on a set of identical parallel machines using efficient heuristics when the objective is to minimize total weighted squared tardiness is considered. Two efficient heuristics and an improvement procedure are presented for the problem. These heuristics and other heuristics are tested using problem sets that represent a variety of conditions. The results show that one of the heuristics consistently performs better than the other heuristics tested. It is also shown how these heuristics can be incorporated into other procedures such as the existing Lagrangian relaxation procedure or meta-heuristics to obtain improved solutions for medium sized problems.
May 2018
A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Zhengyang Hu, Guiping Hu A stochastic lot-sizing and scheduling problem with demand uncertainty is studied in this paper. Lot-sizing determines the batch size for each product and scheduling decides the sequence of production. A multi-stage stochastic programming model is developed to minimize overall system costs including production cost, setup cost, inventory cost and backlog cost. We aim to find the optimal production sequence and resource allocation decisions. Demand uncertainty is represented by scenario trees using moment matching technique. Scenario reduction is used to select scenarios with the best representation of original set. A case study based on a manufacturing company has been conducted to illustrate and verify the model. We compared the two-stage stochastic programming model to the multi-stage stochastic programming model. The major motivation to adopt multi-stage stochastic programming models is that it extends the two-stage stochastic programming models by allowing revised decision at each period based on the previous realizations of uncertainty as well as decisions. Stability test and weak out-of-sample test are applied to find an appropriate scenario sample size. By using the multi-stage stochastic programming model, we improved the quality of solution by 10–13%.
May 2018
Nonlinear optimization problem subjected to fuzzy relational equations defined by Dubois-Prade family of t-norms
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Amin Ghodousian, Marjan Naeeimi, Ali Babalhavaeji In fuzzy set theory, triangular norms (t-norm for short) and triangular co-norms (t-conorm for short) play a key role by providing generic models for intersection and union operations on fuzzy sets. Various continuous and discontinuous t-norms have been proposed by many authors. Despite variation in the t-norms, most of the well-known continuous t-norms are Archimedean (for example, Frank, Yager, Hamacher, Sugeno-Weber and Schweizer-Sklar family). An interesting family of non-Archimedean continuous t-norms was introduced by Dubois and Prade. This paper is an attemp to study a nonlinear optimization problem whose constraints are formed as a special system of fuzzy relational equations (FRE). In this type of constraint, FREs are defined with max-Dubois-Prade composition. Firstly, we investigate the resolution of the feasible solutions set. Then, some necessary and sufficient conditions are presented to determine the feasibility or infeasibility of the solutions set. Also, some procedures are introduced for simplifying the problem. Since the feasible solutions sets of FREs are non-convex, conventional nonlinear programming methods may not be directly employed to solve the problem. Therefore, in order to overcome this difficulty, a genetic algorithm (GA) is designed based on some theoretical properties of the problem. It is shown that the proposed algorithm preserves the feasibility of new generated solutions. Moreover, a method is presented to generate feasible max-Dubois-Prade FREs as test problems. These test problems are used to evaluate the performance of our algorithm. Finally, the algorithm are compared with some related works. The obtained results confirm the high performance of the proposed algorithm in solving such nonlinear problems.
May 2018
Data envelopment analysis models for probabilistic classification
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Parag C. Pendharkar We propose and test three different probabilistic classification techniques using data envelopment analysis (DEA). The first two techniques assume parametric exponential and half-normal inefficiency probability distributions. The third technique uses a hybrid DEA and probabilistic neural network approach. We test the proposed methods using simulated and real-world datasets. We compare them with cost-sensitive support vector machines and traditional probabilistic classifiers that minimize Bayesian misclassification cost risk. The results of our experiments indicate that the hybrid approach performs as well as or better than other techniques when misclassification costs are asymmetric. The performance of exponential inefficiency distribution DEA classifiers is similar or better than that of traditional probabilistic neural networks. We illustrate that there are certain classification problems where probabilistic DEA based classifiers may provide superior performance compared to competing classification techniques.
May 2018
Maintenance policies for energy systems subject to complex failure processes and power purchasing agreement
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Qingan Qiu, Lirong Cui, Li Yang Power purchase agreement (PPA) has emerged as a new support contracting approach and received much attention in recent years. In this paper, a novel maintenance model under PPA is developed for a complex energy generation system, whose objective is to maximize the expected net revenue of energy suppliers. The energy system undergoes two competing and dependent failure processes, namely, soft failure process and hard failure process. The former reduces the production rate of the system, whereas the latter immediately causes the stoppage of the system. The dependence is characterized by the random increment of soft/hard hazard rate due to the occurrence of hard/soft failures. The system is minimally repaired upon a hard failure or the detection of a soft failure. Additionally, a preventive replacement is immediate when the system operational age attains the pre-determined age. The target of this article is to maximize the expected net revenue of energy suppliers via the optimization of the inspection interval and number. A case study on wind turbine system (WTS) is provided to validate the effectiveness of the adopted profit-centric approach.
May 2018
Procurement mechanism for dual sourcing and emergency production under capacity constraint
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): He Huang, Nengmin Zeng, Hongyan Xu This study investigates the optimal procurement mechanism of a buyer who faces two potential suppliers with capacity constraint and private information on costs. The buyer places regular orders to the capacitated suppliers before demand realization and has the option to place an expensive emergency order depending on remaining capacity after that. We observe that the buyer solely exercises emergency option when the virtual cost of the first supplier (with low cost) is greater than emergency production cost. When this virtual cost is not too large, the buyer chooses dual sourcing solely, dual sourcing with emergency option, and single sourcing with emergency option when the capacity is small, moderate, and large, respectively. Compared with symmetric information, information asymmetry weakens capacity constraint for the buyer in terms of order quantity in regular sourcing. However, in terms of order quantity in emergency sourcing, information asymmetry either strengthens capacity constraint when total capacity is greater than expected demand, or weakens capacity constraint when the opposite is the case. Furthermore, the capacity constraint of the first supplier causes a greater buyer’s profit loss than the second supplier (with high cost) does under symmetric information, and information asymmetry further amplifies this asymmetric effect. We also show that the dual sourcing mechanism can be extended to the case of multiple sourcing without loss of the main properties.
May 2018
A spatial data pre-processing tool to improve the quality of the analysis and to reduce preparation duration
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Gautier Daras, Bruno Agard, Bernard Penz Spatial data analysis allows for a better understanding of environmental effects on the performance of an organization’s activities. One of the first steps required to process such an analysis is to gather all of the spatialized data corresponding to the elements that might influence the activities. Then, a series of treatments must be processed on those datasets to make them ready to be used in classical data mining tools. Those pre-processing steps are complex and time consuming tasks that may require advanced Geographic Information System (GIS) skills. Moreover, the choices involved in this process influence the quality of analysis results. With the aim of addressing those issues, we developed a tool that automatizes several steps of spatial data pre-processing tasks. To allow for reproducibility, the specifications of our approach, tools, architectures and techniques required are presented in detail. To support the effectiveness of our approach, a case study is presented that focuses on an evaluation of the processing time that is saved and the improvement of the quality of analysis.
May 2018
Location-routing problem in multimodal transportation network with time windows and fuzzy demands: Presenting a two-part genetic algorithm
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Saeed Fazayeli, Alireza Eydi, Isa Nakhai Kamalabadi Distribution of products throughout a supply chain could be managed via multimodal transportation networks. This will be more likely in long-haul transportation where a decision-maker has to determine the transportation modes and mode changing nodes to optimize the underlying distribution problem. On the other hand, a corresponding location-routing problem arises in any distribution system involving the plan for depots establishment in customer regions. Combination of the mentioned problems is missing in the literature and this study aims to combine the multimodal routing and location-routing problems. In addition, time window constraints were imposed upon the problem to maximize customer satisfaction. These constraints are in accordance with products that should be delivered within predetermined time intervals. Moreover, demands were represented by fuzzy numbers enabling the problem formulation to be well approximated to the real-world situation. Presenting the mentioned problem with time windows and fuzzy demands is the main contribution of this study. A mixed-integer mathematical fuzzy model was presented for the proposed problem. This model simultaneously determined the locations for establishing depots, multimodal terminals (for provision of mode-changing facilities), multimodal routes to deliver products to depots and tours for products delivery to customers which can be helpful to achieve better solutions for distribution systems. Other contribution of this study included presentation of a two-part genetic algorithm for solving the proposed mathematical model. Finally, numerical examples with different problem sizes and scenarios were used and solved by GAMS software and proposed algorithm to demonstrate the performance of the proposed model and algorithm in different situations. The results showed that time windows and fuzzy demands imposed more difficulty to the problem and increased overall cost and time. Also comparing GAMS and algorithm values and solution time indicated proper performance of the proposed algorithm.
May 2018
Modelling intervention policies of government in price-energy saving competition of green supply chains
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Ashkan Hafezalkotob Energy saving efforts decrease the demand of energy services, and it can yield improvement in the environmental protection, national security, financial benefits, and social welfare. In this research, we investigate the effects of various governmental regulation policies on competition of green supply chains. We consider six regulation policies of deregulation, direct tariff, direct limitation, government certificate, government permit, cooperative energy saving as well as two decision making structures of centralized and decentralized green supply chains. We formulate twelve mathematical programming models using Stackelberg game between government and supply chains. A comprehensive analysis of brick production supply chains reveals some managerial insights. We find that all intervention policies are advantageous because they result in more social utilities than deregulation policy; however, the policy should be chosen regarding the effects on consumers, green supply chains, and the environment. In particular, cooperative energy saving policy yields the highest social utility and energy saving level; meanwhile, it involves the highest government investment. Moreover, we know than profit seeking behaviour of government in all policies causes the decrease in social utility.
May 2018
Value of on-site rework in a coordinated two-stage supply chain with supply imperfection
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Chung-Chi Hsieh, Chih-Chung Chiu This paper considers a decentralized supply chain with one supplier with supply imperfection and one manufacturer. The supplier performs outbound inspection to ensure that its components comply with the quality specification (QS), and makes efforts to improve inspection reliability. Once receiving the supplier’s components, the manufacturer begins the assembly production process, requiring that every component meet the quality requirement (QR). The components that do not meet the QS or QR are reworked by the supplier. The location at which the latter group of the components is reworked becomes a strategic choice, provided that the option of on-site rework, i.e. rework performed at the manufacturer’s production site, is available. We show that on-site rework may be beneficial to the supply chain even if it is more costly than in-house rework, i.e. rework performed in the supplier’s in-house repair center. In addition, coordinating the decentralized supply chain with the option of on-site rework yields maximum supply chain performance over a certain cost range of on-site rework. Finally, we show the similarities and differences of the effects of QS and QR on the coordinated results.
May 2018
Efficient simulation and analysis of mid-sized networks
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Luis E. Castro, Xu Dong, Nazrul I. Shaikh There is growing interest in developing the abilities to simulate realistic social networks and analyze data generated from existing online social networks such as Facebook and Twitter. Amongst other things, researchers and practitioners need these abilities to study how opinions and information diffuse over networks and identify the influential agents in networks. However, the sizes of the social networks that need to be simulated and the amount of user generated data that needs to be analyzed is growing at a faster rate than the computational power of most of the modern day computers. This paper presents a memory efficient network representation and computational resource allocation algorithm that yields a scale-up of about 400; thus, given a constraint on the availability of computational resources, researchers can now use the proposed algorithm to simulate and analyze networks that are more than 100 times larger than what they could simulate otherwise. The proposed network representation is conducive to multi-core processing and random node sampling. Algorithms for computationally efficient execution of three random-node-sampling-based methods to estimate network metrics such as the network diameter and average path length are also presented in the paper. These algorithms yield a speed-up of about 40 even when the researcher requires a precision of more than 98%. The scale-up and speed-up numbers are based on a detailed performance analysis of the proposed algorithms that was conducted on synthetic networks of sizes ranging from 1000 to 1,000,000 nodes. The observed scale-up and speed-up performance of the proposed algorithms has been validated against the algorithms used in igraph and statnet-two popular network data analysis software package, and these results are also presented in this paper.
May 2018
Planning the transport of loads to oil platforms considering the arrangement of the loads on the ship's deck
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Gustavo de Luna Pinto, Lirielly Ruela Vitorugo, Rodrigo de Alvarenga Rosa, Bianca Passos Arpini, Lucas Arrevabene Caprini Oil exploration in Brazil is done mainly by offshore oil platforms located far from the coast and ships transport all supplies for them. This paper proposes a method based on a Hybrid Simulated Annealing with Ship’s Balance (HSA-SB) metaheuristic to plan the transport of loads to offshore platforms considering the arrangement of the loads on the ship’s deck and the ship's balance about its keel. The main objective of HSA-SB is to minimize the ships affreightment costs by reducing the number of ships used and the distance sailed. It also arranges the loads on the deck, aiming to reduce ship’s imbalance. HSA-SB was tested on real instances from a major Brazilian oil company and the results showed possible financial benefits.
May 2018
Cooperative maximal covering models for humanitarian relief chain management
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Xueping Li, Mohammad Ramshani, Yu Huang Observing the growing risks of disasters and challenges in humanitarian relief chain management, this study examines the general structure in humanitarian relief chain logistics, and focuses on developing a maximal cooperative covering model with budget considerations to maximize the benefits to the affected population in disastrous regions. Disasters affect regions depending on their severity, and the demand of the disastrous regions cannot be exactly predicted. We incorporate demand uncertainty to capture the uncertain nature of disasters. We include financial efficiency and appeal coverage as two key performance indicators to evaluate humanitarian relief chain management, and preform extensive sensitivity and robustness analysis. We analyze the impact of availability of items through distribution centers on relief chain management, and compare the performance of the proposed model under cooperative and non-cooperative scenarios.
May 2018
Joint control of dynamic maintenance and production in a failure-prone manufacturing system subjected to deterioration
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Kaican Kang, Velusamy Subramaniam This paper addresses the integrated control of dynamic maintenance and production in a deteriorating manufacturing system. The previous literature on the integrated control of production and maintenance usually ignores maintenance opportunities that may arise during machines’ failures. Therefore, this paper proposes a dynamic maintenance policy which comprises of corrective, preventive and opportunistic maintenance. Opportunistic maintenance uses the downtime of machines as potential opportunities to perform maintenance on other machines. In this paper, control of dynamic maintenance is integrated with production to achieve the minimal total production cost, which includes costs of inventory, backlog, repair, preventive and opportunistic maintenance. By applying an approximation technique and the value iteration algorithm, the authors obtain a near optimal control policy for the deteriorating manufacturing system. In addition, the authors compare the performance of the obtained control policy with that of a previous control policy reported in the literature. The sensitivity analysis is also conducted to examine the effect of some system parameters on the configuration of the control policy. With the proposed control policy, industry practitioners are able to avoid unnecessary preventive maintenance and fully utilize the opportunistic time window to improve system reliability while achieving minimal total production cost.
May 2018
Product development network modelling extensions to the cycle elimination method
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Omar Abou Kasm, Ali Yassine This paper considers Product Development (PD) project networks, which are characterized by stochastic activity durations and activity rework or iteration (i.e., potential to repeat some activities several times during PD execution). The Cycle Elimination (CE) approach presented in Nasr et al. (2016) reduces the computational complexity of analyzing iterative PD project networks by considering an approximate network with no iteration. We build on the CE approach to investigate practical scenarios which arise in real world PD projects which are not accounted for by the CE approach. These scenarios include: (i) forward probabilities, (ii) dynamic rework probabilities and proportions, (iii) multiple dependency relationships between activities, and (iv) different rework through indirect connections. We demonstrate these extensions using two case studies. The first case study considers a software development process, where we collected the data by interviewing the managers of the company. The second case study involves a hardware development process (adapted from Pinkett (1998)), where the results show that the proposed method outperformed three existing techniques from the literature. Both cases were solved using the proposed modification to the CE approach, and then simulated to gauge the accuracy of the proposed method showing very promising results.
May 2018
A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Zhuo Dai, Faisal Aqlan, Xiaoting Zheng, Kuo Gao Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS’s solution is higher than that of HGA’s solution, whereas HGA is faster than HHS.
May 2018
Joint order (1, T) policy for a two-echelon, single-item, multi-retailer inventory system with Poisson demand
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Hamed Tayebi, Rasoul Haji, Babak Ghalebsaz Jeddi This study considers a two-echelon inventory system with one central warehouse and a number of non-identical retailers with Poisson demands, constant lead times, and lost sales for a single item. The warehouse works as a cross-docking terminal with no inventory and coordinates shipments to retailers. We apply the Joint order (1, T) policy in such an inventory system. In the standard (1, T) policy, the warehouse sends one unit of the item to each retailer in a fixed cycle time, which is calculated by considering each retailer separately. In the joint order (1, T) policy, the warehouse decides to replenish one unit of the item for each retailer in a cycle time which is adjusted to be an integer multiple of a base cycle time. Thus, under such an ordering policy, the warehouse can better adjust shipments to retailers so that they can be batched with other retailers’ orders, which results in overall savings in ordering, shipping and stock out costs for the entire system on average. We calculate the total cost of the system, and present a procedure to obtain near optimal values of the base cycle time and the integer multiples of the base cycle in which retailers should be served.
May 2018
A consistency-based approach to multiple attribute decision making with preference information on alternatives
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Haiming Liang, Qiang Gong, Yucheng Dong, Zhaogang Ding The methodology to solve the multiple attribute decision making (MADM) with preference information on alternatives has been systematically investigated. However, the inconsistency issue between the two rankings respectively derived from the preference information and the decision matrix is seldom considered. In order to investigate this issue, this paper proposes a consistency-based approach to MADM with preference information on alternatives. Based on the classical idea of the geometric consistency index in the preference relation, we define a geometric consistency index in MADM. Then, we propose an algorithm to adjust the preference information and the decision matrix simultaneously to improve the geometric consistency index in MADM. Next, some simulations experiments are designed to discuss the properties of the proposed approach. Finally, through illustrative examples and a comparative analysis, we demonstrate the effectiveness of the proposed approach.
May 2018
Simultaneous balancing, sequencing, and workstation planning for a mixed model manual assembly line using hybrid genetic algorithm
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Fantahun M. Defersha, Fatemeh Mohebalizadehgashti Balancing and sequencing are two important challenging problems in designing mixed-model assembly lines. A large number of studies have addressed these two problems both independently and simultaneously. However, several important aspects such as assignment of common tasks between models to different workstations, and minimizing the number and length of workstations are not addressed in an integrated manner. In this paper, we proposed a mixed integer linear programming mathematical model by considering the above aspects simultaneously for a continuously moving conveyor. The objective function of the model is to minimize the length and number of workstations, costs of workstations and task duplications. Since the proposed model cannot be efficiently solved using commercially available packages, a multi-phased linear programming embedded genetic algorithm is developed. In the proposed algorithm, binary variables are determined using genetic search whereas continuous variables corresponding to the binary variables are determined by solving linear programming sub-problem using simplex algorithm. Several numerical examples with different sizes are presented to illustrate features of the proposed model and computational efficiency of the proposed hybrid genetic algorithm. A comparative study of genetic algorithm and simulated annealing is also conducted.
May 2018
A two-stage network data envelopment analysis approach for measuring and decomposing environmental efficiency
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Lei Chen, Fujun Lai, Ying-Ming Wang, Yan Huang, Fei-Mei Wu To scientifically evaluate the environmental efficiency (EE) of economic development, this study develops a new two-stage network structure with undesirable intermediate elements to describe the actual production process. According to different relationships between two stages, non-cooperative and cooperative data envelopment analysis (DEA) measure methods are constructed to evaluate the EE of decision-making units (DMUs). An EE decomposition model is then proposed as the second goal to improve the coordination efficiency of DMU. Compared to conventional DEA methods, these EE measure methods not only consider the internal structure of DMUs with undesirable intermediate elements, but also can be used to obtain relatively unique and fair evaluation results for achieving different decision objectives. An empirical study on the Chinese industrial water system is taken as an example to illustrate the effectiveness of these EE measures. The results show that non-cooperative relationships may hinder the sustainable development of the economy and environment, so promoting mutual cooperation between two stages is more in line with the interests of decision makers.
May 2018
Fleet-level selective maintenance problem under a phased mission scheme with short breaks: A heuristic sequential game approach
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Dezhen Yang, Haochen Wang, Qiang Feng, Yi Ren, Bo Sun, Zili Wang Selective maintenance is the most widely used strategy for identifying and performing the maintenance actions necessary for fleet mission success. A fleet of equipment is usually required to perform phased missions with short scheduled breaks. In this case, a selective maintenance model should be extended for frequency-based maintenance optimization. We research the problem considering the application of condition-based maintenance (CBM). The problem is formulated with the objective of reducing the repair frequency and cost. The constraint is the reliability of the phased mission, and the variables are the remaining useful lifetimes (RUL) of all the key subsystems. The equipment can be classified into three echelons based on the health status before each wave of a mission, and a heuristic game framework with state backtracking is proposed for the three echelons to solve the problem. The flowchart and heuristic rules of the game framework are given, and the game algorithms for the second and third echelons are presented. The second echelon algorithm aims to select the dispatched equipment for the current wave and minimize maintenance, and the third echelon algorithm aims to ensure that sufficient equipment is available for the next wave by performing necessary maintenance. Finally, we present two types of strategy adjustment methods with state backtracking to turn infeasible solutions into feasible solutions and to optimize feasible solutions. To verify the capacity of the proposed method, a case involving a fleet of 12 aircraft is analyzed for a three-mission scheme, and the aircraft repair times and costs are reduced by the method.
May 2018
Beta regression control chart for monitoring fractions and proportions
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): F
May 2018
A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Sameera Mufazzal, S.M. Muzakkir The identification of best alternative from amongst the available choices is a complex task dependent upon the user priorities that needs to be graded on a rating scale requiring careful consideration of all influencing characteristic features of individual alternative. Several multi criterion decision making techniques are available to facilitate the decision maker arrive at a best alternative by ranking the alternatives in an order of preference. However, it has been observed that with the addition of new alternatives or deletion of existing alternatives, the ranks of the available alternatives, indicating their suitability to a particular set of requirements, is not maintained. This is often described as rank reversal phenomenon by many researchers. The commonly used MCDM methods are particularly incapable of preventing this rank reversal phenomenon. Thus, addition of new alternative/s or deletion of existing alternative/s creates a modified order of preference which may, sometimes, lead to erroneous decisions/results. In the present research work, an effort has been made to critically examine the rank reversal phenomenon with an aim to propose a new method to obviate this problem. In order to establish the feasibility and effectiveness of the proposed method in preventing the rank reversal phenomenon, several case studies covering different technological specializations from the reported literature work have been considered. The results indicate that the rank reversal issue was found to be minimal with the use of proposed method and a good correlation was found to exist between the rankings obtained by the proposed method and the other commonly used MCDM methods. The proposed method is thus capable of preventing the rank reversal phenomenon, arising out of change in available alternatives.
May 2018
Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Xindong Peng, Harish Garg This paper aims to give deeper insights into decision making problem based on interval-valued fuzzy soft set (IVFSS). Firstly, a new score function for interval-valued fuzzy number is proposed for tackling the comparison problem. Subsequently, the formulae of information measures (distance measure, similarity measure and entropy) are introduced and their transformation relations are pioneered. Then, the objective weights of various parameters are determined via new entropy method, meanwhile, we develop the combined weights, which can show both the subjective information and the objective information. Moreover, we propose three algorithms to solve interval-valued fuzzy soft decision making problem by Weighted Distance Based Approximation (WDBA), COmbinative Distance-based ASsessment (CODAS) and similarity measure. Finally, the effectiveness and feasibility of approaches are demonstrated by a mine emergency decision making problem. The salient features of the proposed methods, compared to the existing interval-valued fuzzy soft decision making methods, are (1) it can obtain the optimal alternative without counterintuitive phenomena; (2) it has a great power in distinguishing the optimal alternative; and (3) it can avoid the parameter selection problems.
Available online 22 April 2018
A genetic algorithm based heuristic for dynamic lot sizing problem with returns and hybrid products
Publication date: May 2018
Source:Computers & Industrial Engineering, Volume 119 Author(s): Pakayse Koken, Venkatesh Arasanipalai Raghavan, Sang Won Yoon For a hybrid system with manufacturing and remanufacturing, a variant of dynamic lot sizing problem is addressed in this study. In the system, manufactured and remanufactured products are produced on separate lines and sold in segmented markets. In addition to these two types of products, there are also hybrid products produced in the system. Hybrids are used to meet the excess manufactured product demand and integrate the two distinct lines. Therefore, this study investigates the profitability conditions for producing the hybrid products. Using a variant of dynamic lot sizing problem, called dynamic lot sizing problem with returns and hybrids (DLSPRH), which is a constrained mixed-integer nonlinear programming problem, the performance of the system with hybrids is compared to the same system with no hybrids. The DLSPRH is a NP-hard problem. A Genetic Algorithm based heuristic (GA_H) has been proposed to solve the DLSPRH and its capacitated version from the literature. The performance of the algorithm is tested by comparing its results with Simulated Annealing (SA), Variable Neighborhood Search (VNS) and Simulated Annealing with Neighborhood List (SA_NL). Numerical experiments show that GA_H significantly outperforms the other metaheuristic algorithms. On average, GA_H performs 2.51%, 2.24% and 2.06% better than SA, VNS and SA_NL algorithms, respectively. Another finding is that the system with hybrids performs well at medium–high holding cost environments especially when remanufacturing demand is low. Additional managerial insights are also presented.
Available online 22 April 2018
Iterated local search algorithm with ejection chains for the open vehicle routing problem with time windows
Publication date: Available online 22 April 2018
Source:Computers & Industrial Engineering Author(s): Jos
Available online 21 April 2018
The Unmanned Aerial Vehicle Routing and Trajectory Optimisation Problem, a taxonomic review
Publication date: Available online 22 April 2018
Source:Computers & Industrial Engineering Author(s): Walton Pereira Coutinho, Maria Battarra, J
Available online 21 April 2018
Integrated planning of project scheduling and material procurement considering the environmental impacts
Publication date: Available online 21 April 2018
Source:Computers & Industrial Engineering Author(s): Babak H. Tabrizi Concurrent planning of project scheduling and material procurement has been addressed in this paper aiming to facilitate more efficient execution of projects. Both all-unit and incremental discount strategies, as the most commonly purchase incentives suggested by vendors, are taken into consideration to procure the materials from. On the other hand, it is advisable to deal with the side effects of a civil project, in particular, as the societies have been concentrating on the environmental issues, as well as the execution methods planning within the last years. Thus, a mixed-integer programming model is proposed to minimize the project costs, in addition to the environmental impacts. Two distinctive meta-heuristics, calibrated by the Taguchi method, are considered to solve the problem with different sizes. Finally, the applicability and efficiency of the solution methods is tested in terms of appropriate multi-objective comparison measures.
Available online 21 April 2018
A Mathematical Definition and Basic Structures for Supply Chain Reliability: A Procurement Capability Perspective
Publication date: Available online 21 April 2018
Source:Computers & Industrial Engineering Author(s): Chunghun Ha, Hong-Bae Jun, Changsoo Ok Supply chain reliability has been receiving increasing attention in recent years, as it might provide a theoretical background for quantifying supply chain risks and uncertainties. However, most previous researches on supply chain reliability only focus on some reliability issue for limited supply chain structure without any general definition of supply chain reliability. This limitation makes it difficult to apply the theoretically well-established reliability engineering methodologies to various assessment and optimization problems related to supply chain reliability and risk. To tackle the issue, this paper provides a mathematical definition on supply chain reliability and relevant functions based on the traditional reliability theory, and subsequently, the basic structural reliability models for various types of supply chains. This paper also verifies that the proposed functions and structural reliability models are applicable to various types of supply chain with a case study of a computer assembly company.
Available online 20 April 2018
A Decision Support System based on Ontology and Data mining to Improve Design using Warranty Data
Publication date: Available online 21 April 2018
Source:Computers & Industrial Engineering Author(s): Mohammed Alkahtani, Alok Choudhary, Arijit De, Jenny Harding Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability and reduce costs in areas, including product development processes, advanced product design, and manufacturing. However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost optimization for identifying manufacturing faults, mapping them to design information and finally optimizing design parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases, which collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e., sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas where improvements have resulted in the greatest reliability for the lowest cost.
Available online 19 April 2018
Modeling learning and forgetting processes with the corresponding impacts on human behaviors in infectious disease epidemics
Publication date: Available online 20 April 2018
Source:Computers & Industrial Engineering Author(s): Kaiming Bi, Yuyang Chen, Songnian Zhao, David Ben-Arieh, Chih-Hang (John) Wu This article presents two new mathematical models, an information forgetting curve (IFC) model and a memory reception fading and cumulating (MRFC) model, to examine forgetting and learning behaviors of individuals during an infectious disease epidemic. Both models consider how epidemic prevalence and community behavior-change information may affect agent emotions and subsequently influence an individual's behavior changes during an epidemic. The IFC model utilizes a forgetting curve to process epidemic information, and the MRFC model formulates disease information variations using the It
Available online 19 April 2018
Multi-period Incentive Contract Design in the Agent Emergency Supplies Reservation Strategy with Asymmetric Information
Publication date: Available online 19 April 2018
Source:Computers & Industrial Engineering Author(s): Gao Xiao-ning, Tian Jun Agent reserve strategy have experienced increased popularity in recent decades. In such a reservation method, the amount of effort exerted by an enterprise has significant influence on the value and supply capacity of emergency materials. When the interests of the government and the enterprise are not aligned, combined with poor information sharing, the members of the enterprise are more likely to suffer from emotional burnout, especially when considering long-term contract periods. Thus, the enterprise usually cannot consciously act in accordance with the government’s wishes. We extend the one-period incentive contract model to multi-periods to constrain the enterprise’s behavior and stimulate it to exert more effort. We conduct a numerical experiment to illustrate the changes in the two parties’ benefits over the contract period using the MATLAB experimental platform. The results show that both parties can benefit, and the incentive power of a multi-period incentive contract is better than that of a one-period contract. Furthermore, we analyze and determine the optimal cooperation period and the optimal joint profit. The practical purpose of using a multi-period incentive contract is steady improvement in emergency supplies’ capabilities and a strengthening of the enterprise’s effort, which lead to long-term benefits for both the government and the enterprise and valuable suggestions for the government’s decision-making.
Available online 18 April 2018
Economic evaluation of the trilateral FTA among China, Japan, and South Korea with big data analytics
Publication date: Available online 19 April 2018
Source:Computers & Industrial Engineering Author(s): Lianbiao Cui, Malin Song, Lei Zhu This study quantitatively analyzes the impact of a free trade agreement (FTA) among China, Japan, and South Korea using big data analytics. Using game theory and the computable general equilibrium approach, it proposes a compromise between the two countries for agricultural protection, to reduce possible divergences and confrontations. The findings show that game results differ among the three countries as per interest indexes. Compared to full tariff exemption, an FTA with agricultural protection not only stimulates economic growth in the three countries but also reduces Japan and South Korea’s agricultural concerns and impact on employment. We also evaluate the impacts of the trilateral FTA on manufacturing and services industries. The results show that China will increase imports of energy-intensive products from Japan and South Korea, which may reduce domestic output and generate environmental benefits. Implementing the trilateral FTA with agricultural protection may reduce carbon emission in Northeast Asia by 6.53 million tons. This study can promote economic integration in Northeast Asia and coping with climate change. The analysis also highlights the importance of lifecycle management of energy-intensive industries in China, Japan, and South Korea.

Integrated Decisions for Supplier Selection and Lot-Sizing Considering Different Carbon Emission Regulations in Big Data Environment
Publication date: Available online 18 April 2018
Source:Computers & Industrial Engineering Author(s): Kuldeep Lamba, Surya Prakash Singh, Nishikant Mishra The rising concerns about carbon emissions due to drastic environmental changes globally has increased awareness of customers regarding the carbon footprint of the products they are consuming. Thus, compelled supply chain managers to reformulate strategies for controlling the carbon emissions. The various activities contributing to carbon emissions in a supply chain are procurement, transportation, ordering and holding of inventory. Operational decisions like selection of the right supplier of right lot-sizes can play a vital role in reducing the overall carbon footprint of a supply chain. This paper proposes a mixed-integer nonlinear program (MINLP) for supplier selection along with determining the right lot-sizes in a dynamic setting having multi-periods, multi-products and multi-suppliers with a view of overall reduction in the supply chain cost as well as associated cost of carbon emissions. The model requires a range of real time parameters from both the buyer’s and supplier’s perspectives such as costs, capacities and carbon caps. These parameters have been mapped with the different dimensions of Big Data viz. volume, velocity and variety. The model provides an optimal supplier selection and lot-sizing policy along with the carbon emissions. For the purpose of evaluating the carbon emissions, three different carbon regulating policies viz., carbon cap-and-trade, strict cap on carbon emission and carbon tax on emissions, have been considered and insights are drawn. The validation of the proposed MINLP has been done using a randomly generated dataset having the essential parameters of Big Data, i.e. volume, velocity, and variety.
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