Journal Sciences News
World Science and Technology
September 2018
Associated immediate probability intuitionistic fuzzy aggregations in MCDM
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Gia Sirbiladze, Irina Khutsishvili, Bidzina Midodashvili In this article, the Associated Immediate Probability Intuitionistic Fuzzy Order Weighted Averaging (As-IP-IFOWA) and the Associated Immediate Probability Intuitionistic Fuzzy Order Weighted Geometric (As-IP-IFOWG) operators are constructed. Associated probability distributions in the role of uncertainty measure are used. Arguments of the new aggregation operators are presented in the intuitionistic fuzzy values. Some properties of the constructed operators are presented. The conjugate intuitionistic fuzzy operator is defined. The conjugate connections between the constructed operators are shown. Several variants of the new operators for the decision making problem regarding assessment of the software development risks are used.
September 2018
A hybrid multiple criteria decision making approach for measuring comprehensive performance of reverse logistics enterprises
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Mohammed Najeeb Shaik, Walid Abdul-Kader The area of reverse logistics has recently received considerable attention and is an important business policy. The performance measurement of reverse logistics is seldom studied due to the complexity and uncertainty of its operations. This paper provides a multi-criteria performance measurement model to assess the reverse logistics enterprise’s performance by considering performance attributes such as product lifecycle stages, strategies, processes, capabilities, and perspectives and measures. In developing the performance measurement model, a hybrid multi-criteria approach combining DEMATEL, fuzzy ANP and AHP methods are applied. Furthermore, the relative importance of these attributes and their criteria with respect to each other and their contribution to the overall performance are affected by the competitive outlook considered by the reverse logistics enterprises. The performance evaluation model developed in this paper incorporates relevant attributes and achieves a more realistic representation of the enterprise’s performance by calculating the overall comprehensive performance index. This study provides decision makers a basis for improving the reverse logistics enterprise performance.
September 2018
When to invest in carbon capture and storage: A perspective of supply chain
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Xiping Wang, Shaoyuan Qie The purpose of this study is to investigate the investment threshold of carbon capture and storage (CCS) project from the perspective of supply chain, overcoming the limitations of previous works regarding this topic mainly from a single investor’s perspective. An analytical real options model was firstly presented for the scenario of centralized decision making, then the model was extended by integrating the real options theory with the game theory to examine the CCS investment threshold for the scenario of dual-echelon supply chain. An interesting finding is that CCS investment requires a much higher threshold under the dual-echelon supply chain than that under the centralized scenario, and this finding is consolidated by a numerical example simulation. Furthermore, the results of the numerical simulation indicated that the CCS investment threshold is positively affected by carbon price volatility, CO2 capture rate and the transfer payments coefficients, while negatively affected by capital subsidy. These conclusions can provide theoretical foundation for decision-making of CCS investment and related policy-making.
September 2018
A fuzzy programming approach for the multi-objective patient appointment scheduling problem under uncertainty in a large hospital
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): M. Susana Moreno, An
September 2018
A modified Genetic Algorithm approach to minimize total weighted tardiness with stochastic rework and reprocessing times
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Venkatesh Arasanipalai Raghavan, Sang Won Yoon, Krishnaswami Srihari Scheduling challenges are typical with electronics manufacturing services (EMS) providers. The rework and reprocessing of failed electronics components consume more time in the production line, causing jobs to miss their due dates. A mathematical model and a Modified Shortest Total Estimated Processing Time (MSTEPT) Algorithm to minimize the Total Weighted Tardiness (TWT) are proposed in this research. This research then develops a novel modified Genetic Algorithm approach to solve the scheduling problem with stochastic rework and reprocessing time. While the Genetic Algorithm as a methodology to solve scheduling problems has been developed in earlier research articles, the existing set of genes in the chromosomes of a regular Genetic Algorithm would not be able of handle jobs waiting to undergo reprocessing. The modified Genetic Algorithm in this research introduces the concept of priority genes, specifically encoded to handle jobs waiting to be reprocessed after they have been reworked. Experimental results indicate that the proposed modified GA outperforms the best of different commonly used dispatch rules, in terms of solution quality. For small-to-medium-sized job shops, the proposed algorithm outperforms optimal results from CPLEX® optimal solver, as well as those from the MSTEPT algorithm.
September 2018
A scatter simulated annealing algorithm for the bi-objective scheduling problem for the wet station of semiconductor manufacturing
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Jihong Pang, Hongming Zhou, Ya-Chih Tsai, Fuh-Der Chou This paper studies a single-machine scheduling problem where flexible maintenance is required in the planning horizon. The problem observed is in the clean operation of semiconductor manufacturing, where jobs are associated with release times, processing times, due dates, penalty weight of tardy jobs and the amount of dirt left on the machine during processing. The machine, named the wet station, should be stopped for cleaning or maintenance before a maximum amount of dirt has accumulated; the cleaning time is fixed and is known in advance. However, the starting point of each maintenance activity is a decision variable. Preemptive operation is not allowed. The bi-objective of minimizing total weighted tardiness and total completion time is considered in this paper. An intuitive threshold method and a dynamic programming approach are proposed for scheduling jobs and PMs under a given job sequence. A mixed-integer programming formulation is developed to obtain all efficient solutions for the small-size problems. In addition, this paper also develops a scatter simulated annealing (SSA) algorithm in which a scatter-search mechanism leads SSA to explore more potential solutions. Computational experiments are conducted to examine the efficiency of the SSA algorithm. For the small-size problems, SSA could obtain all non-dominated solutions except for a tiny fraction of the instances. For the large-size problems, SSA performs considerably well compared with SMOSA and NSGA-II, demonstrating its potential to efficiently solve bi-objective single problems with flexible maintenance activities.
September 2018
A modified particle swarm optimization algorithm for a batch-processing machine scheduling problem with arbitrary release times and non-identical job sizes
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Hongming Zhou, Jihong Pang, Ping-Kuo Chen, Fuh-Der Chou This paper presents a modified particle swarm optimization (MPSO) algorithm to minimize the maximum lateness for the single batch-processing machine problem with non-identical job sizes and release dates. The MPSO algorithm incorporated a diversification and a local search strategy into a basic particle swarm optimization algorithm. This incorporation enables the proposed algorithm to have a good balance between exploration and exploitation that yields high search efficiency. Additionally, a dynamic programming method is proposed to calculate a relevant value for each particle. The MPSO algorithm was tested in problems from the literature without release dates and newly generated problems with release dates. Computational results show the advantages of combining the diversification strategy, and local search methods. The performance of the proposed MPSO is competitive. For the problems without release dates, the MPSO algorithm could find 80 optimal solutions and improve 68 solutions for all benchmark instances. For the problems with job release dates, the MPSO algorithm also significantly outperformed the other two algorithms with respect to solution quality within the same computational time.
September 2018
The gardener problem with reservation policy and discount
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Seyedeh Sara Sadralsharifi, Seyed Hamid Reza Pasandideh, Seyed Taghi Akhavan Niaki, Mohammad Hossein Nahavandian The Newsboy problem has always been an important issue in inventory management. The multi-product newsboy problem with random yield and budget constraint named as the Gardener Problem is one of the novels and popular extensions of the newsboy problem. Different from the existing studies, this paper presents a multi-product gardener problem with reservation policy. Moreover, a discount rate is offered to those customers who are willing to make reservations. In addition to the demand from the original customers, extra demand is included in the model due to the motivation received by the discount rate. A solution algorithm namely the multi-product gardener problem with reservation policy (MGPRA) is proposed to obtain the optimal order quantities and discount rate in order to maximize the expected profit when the yield and demand are uniformly distributed. This solution algorithm is based on Newton’s method and Lagrangian multipliers and solves the problem in unconstrained, constrained and tightly constraint cases. Examples are given to show not only can the MGPRA solve the problem under the constraint budget, but also it is able to solve the problem under tightly budget constraint, efficiently. In addition to the examples provided, the application of the multi-product gardener problem with reservation policy can obtain greater expected profit than the multi-product gardener problem without a reservation policy.

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August 2018
Mathematical models for flight-to-gate reassignment with passenger flows: State-of-the-art comparative analysis, formulation improvement, and a new multidimensional assignment model
Publication date: September 2018
Source:Computers & Industrial Engineering, Volume 123 Author(s): Moschoula Pternea, Ali Haghani This study explores the mathematical programming formulation of Flight-to-Gate Reassignment with passenger connections, one of the most critical problems in airport and airline recovery operations. Motivated by the intractable increase in problem size when passenger flows are considered, combined with the need for low solution time, we perform three main tasks: (a) We compare and analyze both theoretically and experimentally the different types of state-of-art formulations, and identify the limitations of each one. (b) We improve the performance of existing models by modifying their formulations and introducing valid inequalities. (c) We propose a novel mathematical formulation that accounts for passenger connections considering the layout of the airport and the available time between connecting flights. For the purpose of our experiments, we generate a number of cases of various sizes and schedule scenarios, as well as a set based on a real European airport. We then use our results to identify the most efficient formulations under different objective functions and problem assumptions. We expect that our work can provide researchers with a valuable tool for formulating efficient models that can be embedded in mathematical programming-based heuristics.
August 2018
Editorial Board
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122

August 2018
A multi-stage stochastic programming approach for blood supply chain planning
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): B. Zahiri, S. Ali Torabi, M. Mohammadi, M. Aghabegloo Perishability of blood products and uncertainty in donation and demand sizes complicate the blood supply chain planning. This paper presents a novel bi-objective mixed-integer model for integrated collection, production/screening, distribution and routing planning of blood products, and seeks to simultaneously optimize the total cost and freshness of transported blood products to hospitals. To cope with inherent uncertainty of input data, a multi-stage stochastic programming approach with a combined scenario tree is presented. Due to the high complexity of the problem, a novel hybrid multi-objective self-adaptive differential evolution algorithm is developed, which benefits from the variable neighborhood search with fuzzy dominance sorting (hereafter it is briefly called MSDV). MSDV is validated through comparing its performance with two of the most common multi-objective evolutionary algorithms (i.e. MOICA and NSGA-II). Applicability of the proposed decision model is also tested through a real case study. Our results show that the solution efficiency of a network can be balanced with its effectiveness through customer satisfaction. Further, several sensitivity analyses are carried out to provide valuable managerial insights.
August 2018
Unmanned aerial vehicle scheduling problem for traffic monitoring
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Miao Li, Lu Zhen, Shuaian Wang, Wenya Lv, Xiaobo Qu For more accurate multiple-period real-time monitoring of road traffic, this paper investigates the unmanned aerial vehicle scheduling problem with uncertain demands. A mixed integer programming model is designed for this problem by combining the capacitated arc routing problem with the inventory routing problem. A local branching based solution method is developed to solve the model. A case study which applies this model to the road traffic in Shanghai is performed. In addition, numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.
August 2018
Heterogeneous multigranulation fuzzy rough set-based multiple attribute group decision making with heterogeneous preference information
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Bingzhen Sun, Weimin Ma, Xiangtang Chen, Xiaonan Li This study proposes a heterogeneous attribute multigranulation fuzzy rough set approach to the problem of multiple attribute group decision-making (MAGDM) under uncertainty. We first present the definition of heterogeneous attribute multi-source information systems and then construct the heterogeneous multigranulation approximation space using the arbitrary binary relation classes generated by different categories attribute. We then give the rough approximation of a fuzzy decision-making object with respect to heterogeneous multigranulation approximation space, i.e., the heterogeneous attribute multigranulation fuzzy rough set model over multi-source information system. Meanwhile, we investigate some interesting properties and conclusions for the proposed new model and also discuss the interrelationship between the proposed heterogeneous attribute multigranulation fuzzy rough set model and the existing generalized rough set models. After that, we construct a new approach to MAGDM problems based on heterogeneous attribute multigranulation fuzzy rough set theory. The decision-making procedure and the methodology as well as the algorithm of the proposed method are given and a detailed comparison of the traditional methods to MAGDM problems illustrates the advantages and limitations. Finally, an example of handling MAGDM problem of evaluation of emergency plans for unconventional emergency events illustrates this approach.
August 2018
The reliable design of a hierarchical multi-modes transportation hub location problems (HMMTHLP) under dynamic network disruption (DND)
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Sara Sadat Torkestani, Seyed Mohammad Seyedhosseini, Ahmad Makui, Kamran Shahanaghi In ground and air transportation systems, services are operated as hub-and-spoke networks. For a given discrete planning horizon, disrupted hub nodes and edges can change the location of hub nodes and routes of transferring (allocation) at each level of hierarchy, so that the system can be converted to a flexible network capable of expanding or contracting in the long term. Because of the probability of hub nodes’, hub edge’ disruption and the periodic changes demands, A novel mixed-integer mathematical programming formulation that is effective for a hierarchical multi-modes transportation hub location problem (HMMTHLP) is presented. The main issue is how to tackle the hub nodes’ and edges’ disruption in a dynamic system. Various instances from the two different sets of data such as CAB dataset and Airport-Railway Network real-life case study (ARWN) are provided for computational analysis. The value of these tests in a multi-period solution with managerial insights of the two different cases are presented. A Monte Carlo simulation is used in a two-stage heuristic algorithm; this methodology provides a strategy with which to compare and discuss the locations-allocations of a dynamic network with or without any disruption.
August 2018
The construction of a modified sampling scheme by variables inspection based on the one-sided capability index
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Amy H.I. Lee, Chien-Wei Wu, Zih-Huei Wang Several process capability indices have been applied to develop variables single sampling plans (SSPs) and repetitive group sampling plans (RGSPs) for numerical measurements of quality characteristics. Both types of plans are simple to administer and easy to implement, but neither of them considers the available information of previous collected samples. In this study, we develop a modified variables repetitive group sampling plan (Modified-VRGSP) to overcome the disadvantages of the SSP and the RGSP by considering the quality history of preceding lots based on the one-sided capability index. By minimizing the average sample number while satisfying the quality levels demanded by both the producer and the consumer, the plan parameters can be obtained for product acceptance determination. The advantages of the proposed plan over existing variables sampling plans are presented.
August 2018
Green-blood supply chain network design: Robust optimization, bounded objective function &amp; Lagrangian relaxation
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Hassan Heidari-Fathian, Seyed Hamid Reza Pasandideh The aim of this research is to consider the issue of sustainability in designing a blood supply chain network by presenting a multi objective mixed integer mathematical programming model that aims to simultaneously minimize the total cost of the supply chain network and the total environmental impacts of the activities of the supply chain network. As the nature of supplying the blood by the donors and also demand for the blood product are uncertain, a robust optimization approach is applied in the model to deal with this type of uncertainty. To convert the proposed multi objective model into a single objective one, the bounded objective function method is used. Then, as the presented mathematical model is a complicated mixed integer linear programming model, an algorithm based on the Lagrangian relaxation approach is proposed to solve the model. At the end, a computational study is done to present the competency of the proposed Lagrangian relaxation algorithm.
August 2018
Decision data model in virtual product development
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Frank Sch
August 2018
Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Simona Vasilica Oprea, Adela B
August 2018
A large neighborhood search based matheuristic for the tourist cruises itinerary planning
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Simona Mancini, Giuseppe Stecca The planning of itineraries for tourist cruises is a complex process where several features, such as vessel selection, port services, and requirements for point of interest to be inserted in each tour, must be addressed. The present work models the tour planning problem as a variant of vehicle routing problem considering specific constraints such as: fixed number of tours, not mandatory visits of all nodes, multiple time windows, possibility to choose among different travel speed values. The resulting mathematical formulation lead to a complex model for which commercial solvers fail to solve large instances in a reasonable time. To overcome this issue we propose a Large Neighborhood Search based matheuristic, in which an over-constrained version of the mathematical model is used to exhaustively and efficiently explore large neighborhoods. Test results performed on a real case instances demonstrate effectiveness of the proposed approach.
August 2018
Lower bound development in a flow shop electronic assembly problem with carryover sequence-dependent setup time
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): M.T. Yazdani Sabouni, Rasaratnam Logendran A flow shop group-scheduling problem in the assembly of printed circuit boards (PCBs) is addressed in this paper. We have developed search algorithms yet their quality cannot be attested to when optimal solutions or lower bounds are unavailable. A very effective and efficient lower-bounding mechanism based on the underlying concepts of column generation and branch-and-price is developed. The problem remains so complex even after decomposition. Thus, optimal properties and strategies are developed to facilitate efficiently solving the sub problems. Accompanied by an experimental design and statistical analysis, comprehensive computational tests for a wide range of problems are carried out. The findings suggest that the lower bound and search algorithms are very effective even for large-size problem instances.
August 2018
Unsupervised classification of multichannel profile data using PCA: An application to an emission control system
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Massimo Pacella Modern sensing technologies have facilitated real-time data collection for process monitoring and fault diagnosis in several research fields of industrial engineering. The challenges associated with diagnosis of multichannel (multiple) profiles are yet to be addressed in the literature. Motivated by an application of fault diagnosis of an emission control system, this paper proposes an approach for efficient and interpretable modeling of multichannel profile data in high-dimensional spaces. The method is based on unsupervised classification of multichannel profile data provided by several sensors related to a fault event. The final goal is to isolate fault events in a restricted number of clusters (scenarios), each one described by a reference pattern. This can provide practitioners with useful information to support the diagnosis and to find root cause. Two multilinear extensions of principal component analysis (PCA), which can analyze the multichannel profiles without unfolding the original data set, are investigated and compared to regular PCA applied to vectors generated by unfolding the original data set. The effectiveness of multilinear extensions of PCA is demonstrated using an experimental campaign carried out on an emission control system. Results of unsupervised classification show that the multilinear extension of PCA may lead to a classification with better compactness and separation of clusters.
August 2018
A new approach to overall performance evaluation based on multiple contexts: An application to the logistics of China
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Jin-Xiao Chen Data envelopment analysis (DEA) is a useful method for evaluating the performance of decision making units (DMUs). In this paper, we propose a new approach to overall performance evaluation of DMUs based on multiple contexts in the framework of DEA. For a given set of DMUs, an algorithm is performed to identify frontiers of different efficiency levels as evaluation context. An ideal case is supposed in which all evaluation contexts lead to a consistent report on the performance of DMUs. Shannon entropy is employed to measure the entropy deviations of evaluation results from the real case to the ideal case. A constrained optimization model is constructed to integrate the results against multiple evaluation contexts into an overall performance score for each DMU. The proposed approach is applied to evaluate the logistics performance of China. Its comparisons to some previous methods are also illustrated using the empirical application. It is shown that the proposed approach is robust and provides a more comprehensive evaluation for logistics performance.
August 2018
A non-time segmented modeling for air-traffic flow management problem with speed dependent fuel consumption formulation
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Ali Akgunduz, Helia Kazerooni Aircraft en-route flight planning is one of the major challenges for Air Traffic Control operations. Poor planning results in undesirable congestion in the air-traffic network, causing major economic losses for both airline companies and the public. Furthermore, heavy congestion generates flight safety risks due to increased possibility of mid-air conflict. To address these problems, this paper introduces a non-time segmented en-route flight plan formulation with rerouting options for aircrafts in a 3-dimensional (3D) capacitated airspace. Novelty of the proposed mathematical model is the non-time segmented formulation that captures exact arrival and departure times to/from each air-sector. The proposed formulation also incorporates sector capacity changes due to changing weather conditions during planning horizon. Moreover, the speed dependent fuel consumption rate is introduced as a factor in the zone-based air traffic flow management problem. In order to handle the problem sizes similar to those in real-world cases, we proposed a sequential solution heuristics. The performance of the sequential solution method is demonstrated through various test cases.
August 2018
Fuzzy QFD approach for managing SCOR performance indicators
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Piyanee Akkawuttiwanich, Pisal Yenradee The Supply Chain Operations Reference (SCOR) KPIs are widely used to measure supply chain performances by industrial practitioners. However, it is still difficult to determine what actions should be carried out to improve the KPIs. This paper proposes a new fuzzy QFD approach to manage the SCOR KPIs. The SCOR KPIs are specified as “Whats” and the technical improvement actions (TIs) are specified as “Hows”. The proposed fuzzy QFD approach will prioritize the TIs to be implemented to achieve the target SCOR KPIs. A case study of bottled water manufacturing is used to demonstrate the application of the proposed approach. This paper is the first attempt to develop the fuzzy QFD approach to manage SCOR KPIs with a real industrial case study.
August 2018
Relative power in supply chains – Impact on channel efficiency &amp; contract design
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Abhishek Chakraborty, Arqum Mateen, Ashis Kumar Chatterjee, Nivedita Haldar The performance of a supply chain is often characterized by the power of decision making of the partners involved. Various decisions taken by different partners influence the overall profit of the chain and hence affect the channel efficiency. In this paper we have considered a two-echelon supply chain where the final demand depends upon both the retail price and the marketing expenses borne by the partners. Both the manufacturer and the retailer have been examined as the Stackelberg leader alternately. Profitability increases for the follower, while the leader suffers on account of being the Stackelberg leader. We have also shown that under these circumstances, both the wholesale price as well as the revenue sharing contract fails to coordinate the supply chain. We develop a hybrid revenue and cost sharing contract that coordinates the supply chain thereby making the chain fully efficient.
August 2018
Minimizing total completion time in the assembly scheduling problem
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Ik Sun Lee This paper studies a two-stage assembly problem to minimize the total completion time. The two-stage assembly system consists of multiple machines in the first stage, and an assembly machine in the second stage. Each job consists of multiple components. In the first stage each component is processed on the dedicated machine. In the second stage, the processed components of each job are shipped and assembled into a product on the assembly machine. This system is a generalization of flowshop, which has practical applications in assembly-driven manufacturing. The objective is to establish an efficient schedule minimizing the total completion time. Six lower bounds are proposed and evaluated in a branch-and-bound algorithm. Also, four efficient heuristic algorithms are developed to generate near-optimal schedules. The computational results show that the derived B&B and heuristic algorithms perform very well within reasonable time.
August 2018
A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): MME Alemany, A. Ortiz, Vicente S. Fuertes-Miquel Order promising in manufacturing systems that produce non-uniform units of the same finished good becomes a more complex process when customer orders need to be served with homogeneous units. To facilitate this task, we propose a mathematical model-based decision tool to support the order promising process according to product homogeneity requirements in hybrid Make-To-Stock (MTS) and Make-To-Order (MTO) contexts. In these manufacturing environments, the comparison of Available-To-Promise (ATP) and/or Capable-To-Promise (CTP) quantities with homogeneous ones ordered by customers is necessary during the order commitment. To properly deal with customers’ product uniformity requirements, different ATP consumption rules are implemented by defining a novel objective function. CTP modelling in these systems also entails having to address new aspects, such as estimating future homogeneous quantities in additional lots to the master plan, accomplishing minimum lot sizes and saving in setups when programming new lots. By including CTP in the order promising model, a closer integration with the master production schedule is achieved. The resulting mathematical model was applied to a ceramic tile company in different supply scenarios and execution modes, and at several availability levels (ATP and ATP&CTP). The results validate model performance and provide insights into the impact of ATP consumption rules on the profits made from committed customer orders in different scenarios for the specific ceramic tile company.
August 2018
An Improved Artificial Bee Colony algorithm for real-world hybrid flowshop rescheduling in Steelmaking-refining-Continuous Casting process
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Kunkun Peng, Quan-Ke Pan, Liang Gao, Biao Zhang, Xinfu Pang Realistic production systems usually encounter various of unexpected disruptions, which may invalidate the original schedules, and thus rescheduling becomes essential. This paper studies a real-world hybrid flowshop rescheduling problem in Steelmaking-refining-Continuous Casting (SCC) process, where machine breakdown is considered as the disruption, and controllable processing times in the last stage of SCC process is considered. An Improved Artificial Bee Colony (IABC) algorithm is developed to solve the problem. In the IABC, novel encoding and decoding strategies are devised to represent the solutions effectively, where a charge left-shifting strategy is designed to decrease cast break. A population initialization heuristic is devised to generate solutions with a high level of quality and diversity. Meanwhile, a variable neighboring operator, which can balance the exploration and exploitation abilities, is proposed to generate new solutions with high quality for the employed bee and onlooker bee phases. Moreover, a worst solution replacement strategy is developed to further enhance the exploitation ability. To demonstrate the performance of the IABC, comprehensive computational comparisons against several state-of-the-art algorithms and statistical analysis are conducted, which discloses the strength and availability of the IABC. Moreover, key features of the IABC are analyzed, confirming their critical roles to the success of the IABC.
August 2018
A new product development concept selection approach based on cumulative prospect theory and hybrid-information MADM
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Cheng-shuo Ying, Yan-Lai Li, Kwai-Sang Chin, Hong-Tai Yang, Jie Xu New product development (NPD) concept selection is one of the foremost phases in the early stage of NPD to identify the best alternative concept for an enterprise. In practice, not all attributes of NPD concept can be estimated precisely considering inevitable uncertainty associated with the NPD processes. Thus, the NPD concept selection is a hybrid-information multiple attribute decision making (HI-MADM) problem, in which attribute values are represented in various formats (e.g., crisp numbers, interval numbers, and linguistic terms). In the concept selection, psychological behaviors of the NPD team (NPDT) have a non-negligible influence on the accuracy of the final decision. Nevertheless, the decision behaviors are rarely considered in existing studies on the concept selection. In this paper, a risk HI-MADM method based on cumulative prospect theory (CPT) is proposed to select the NPD alternative concepts. Initially, decision information in various formats is normalized and the expectations of the NPDT are set as the corresponding reference points with considering the psychology of the NPDT. Subsequently, the gain and loss matrix relative to the reference points is constructed. Furthermore, the prospect values of concept attributes are calculated based on the value function of CPT. Then, by aggregating prospect values and attribute weights by the simple additive weighting (SAW) method, the comprehensive prospect values of alternative concepts are obtained, and then the ranking order of all concepts can be determined. Finally, a NPD case study of a new automatic dishwasher is used to illustrate the feasibility and validity of the proposed approach and meanwhile a sensitivity analysis and a comparison analysis are conducted.
August 2018
Large group decision-making (LGDM) with the participators from multiple subgroups of stakeholders: A method considering both the collective evaluation and the fairness of the alternative
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Yang Liu, Zhi-Ping Fan, Tian-Hui You, Wei-Yu Zhang Large group decision-making (LGDM) with the participators from multiple subgroups of stakeholders is a valuable research topic with many practical backgrounds. In the LGDM analysis, not only the collective evaluation but also the fairness of the alternative among the stakeholders from different subgroups needs to be considered, whereas the fairness of the alternative is neglected in the existing studies. This paper proposes a method for LGDM considering both the collective evaluation and the fairness of the alternative. In the method, according to the Pauta criterion (3
August 2018
The Shewhart attribute chart with alternated charting statistics to monitor bivariate and trivariate mean vectors
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Roberto Campos Leoni, Antonio Fernando Branco Costa In this article, we combined the Alternated Charting Statistic (ACS) scheme with the traditional attribute np chart to control mean vectors of bivariate and trivariate normal processes. With the bivariate ACS scheme in use (the trivariate scheme is similar), the two quality characteristics (X, Y) are controlled in an alternating fashion. If the current sample point is the number of disapproved items with respect to the X discriminating limits, then the next sample point will be the number of disapproved items with respect to the Y discriminating limits. The strategy of using the X discriminating limits to classify the items of one sample and the Y discriminating limits to classify the items of the next sample instead of using jointly the X and Y discriminating limits to classify the items of all samples might be compensated with the adoption of larger samples. In other words, the proposed bivariate (trivariate) ACS chart might work with samples as large as 2n (3n); n is the sample size of the competing Hotelling and Max D charts. The proposed chart resembles an np chart with alternated charting statistic; because of that, it is called the ACS mp chart. The ACS mp chart always outperforms the Max D chart and, in comparison with the standard T 2 chart and with the combined Max D
August 2018
Preventive maintenance scheduling for serial multi-station manufacturing systems with interaction between station reliability and product quality
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Xiaojun Zhou, Biao Lu Station reliability and product quality usually interact with each other stochastically in the multi-station manufacturing systems, which brings much trouble for the preventive maintenance (PM) scheduling of the system. In this paper, a station reliability evaluation method is developed firstly based on the introduced bidirectional interaction mechanism between station reliability and product quality, in which the focus is on the derivation of the failure rate of the stations. Then, a dynamic opportunistic PM (OM) policy is presented for the series multi-station systems with such quality integrated station reliability. The OM model is built up based on an extended cost saving method, and the optimal PM scheme of the system is obtained by maximizing the short-term cost savings which not only come from the stations conducting PM but also come from the downstream stations of those PM stations. Finally, a numerical example is given to illustrate how this PM scheduling approach works, and a numerical comparison on different OM policies is also given to show the effectiveness of the proposed OM policy under such interaction between station reliability and product quality.
August 2018
A preemptive fuzzy goal programming model for generalized supplier selection and order allocation with incremental discount
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Hossein Mirzaee, B. Naderi, S.H.R. Pasandideh This paper generalizes the problem of supplier selection and order allocation with multi-period, multi-product, multi-supplier, multi-objective cases as well as quantity discount subject to budget and capacity limitations for both buyers and suppliers. The objectives are total inventory cost (i.e., delay, holding and shortage, ordering, discounted purchase costs) and value of purchasing. The problem is mathematically formulated by a mixed integer linear programming model. This model is then solved by a preemptive fuzzy goal programming approach. Using a numerical experiment, the proposed model is evaluated for performance against weighted fuzzy goal programming, max-min programming, and classical goal programming approaches. The results show that the proposed model outperforms the others.
August 2018
Profit optimisation for deterministic inventory systems with linear cost
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): B. O'Neill, S. Sanni A well-designed inventory system is critical to the success of any business organisation. One of the major challenges of inventory managers is to determine an inventory optimisation strategy that ensures the right balance between keeping enough inventory on hand to meet customer demand and optimising costs related to holding inventory. This paper focuses on providing a general inventory optimisation strategy to support business organisations. We examine a general deterministic model of inventory in which the rate of demand is determined by price and the rate of decay can change over the cycle time. For this general model we examine the profit function arising when the costs are linear with respect to the number of items purchased in the inventory cycle and the total item-time of holding. This framework encompasses a wide range of deterministic models that have appeared in the literature and are useful in practice. Within this framework we derive optimisation results for the cycle time and price. We show how these results apply for particular deterioration functions and demand functions. This allows us to extend present inventory literature to give the solution to a more generalised problem. Our results are analytically and numerically compared with existing specific results in the inventory literature.
July 2018
Seru system balancing: Definition, formulation, and exact solution
Publication date: August 2018
Source:Computers & Industrial Engineering, Volume 122 Author(s): Yang Yu, Junwei Wang, Ke Ma, Wei Sun Seru production can reduce makespan, labor hours and manpower by improving workers’ workload balance based on the reconfiguration of workers. Therefore, this study focuses on the fundamental principles of seru system balancing. For a seru, we define and formulate seru balance (SB) to describe workloads balance of the workers in the seru. For a seru system containing more serus, the seru system balance (SSB) needs to be evaluated from workloads balance of all workers and workloads balance among all serus. Consequently, we define and formulate intra-seru system balancing (Intra-SSB) and inter-seru system balancing (Inter-SSB) to evaluate the two perspectives respectively. We theoretically develop the lower and upper bounds of Intra-SSB and Inter-SSB respectively. In addition, we define and formulate the seru system balancing problem (SSBP) as a bi-objective model with maximizing Intra-SSB and Inter-SSB simultaneously. The property of solution space for SSBP is analyzed. Finally, we develop an improved algorithm based on
July 2018
Editorial Board
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121

July 2018
A support vector machine for model selection in demand forecasting applications
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Marco A. Villegas, Diego J. Pedregal, Juan R. Trapero Time series forecasting has been an active research area for decades, receiving considerable attention from very different domains, such as econometrics, statistics, engineering, mathematics, medicine and social sciences. Moreover, with the emergence of the big data era, the automatic identification with the appropriate techniques remains an intermediate compulsory stage of any big data implementation with predictive analytics purposes. Extensive research on model selection and combination has revealed the benefits of such techniques in terms of forecast accuracy and reliability. Several criteria for model selection have been proposed and used for decades with very good results. Akaike information criterion and Schwarz Bayesian criterion are two of the most popular criteria. However, research on the combination of several criteria along with other sources of information in a unified methodology remains scarce. This study proposes a new model selection approach that combines different criteria using a support vector machine (SVM). Given a set of candidate models, rather than considering any individual criterion, an SVM is trained at each forecasting origin to select the best model. This methodology will be particularly interesting for scenarios with highly volatile demand because it allows changing the model when it does not fit the data sufficiently well, thereby reducing the risk of misusing modeling techniques in the automatic processing of large datasets. The effects of the proposed approach are empirically explored using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care manufacturer in the UK. Our findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.
July 2018
Bi-objective dependent location quadratic assignment problem: Formulation and solution using a modified artificial bee colony algorithm
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Suman Samanta, Deepu Philip, Shankar Chakraborty Combinatorial optimization problems arise from various real life situations and the quadratic assignment problem (QAP) to model a facility layout problem or a plant location problem is such an example. While examining the facility layout of a semi-automated bus body manufacturing unit, a bi-objective facility layout optimization problem is identified in which the solution space of the second objective function depends and changes upon the feasible solutions of the first objective function. In this paper, the said problem is first defined in the form of a bi-objective quadratic dependent location assignment problem (bi-d-QAP), a heuristic solution approach is then provided, and finally, a modified artificial bee colony algorithm is proposed while combining both the genetic and neighborhood search algorithms to solve the considered bi-d-QAP. The data obtained from the above-mentioned manufacturing unit are utilized to show how the proposed algorithm performs better in comparison to some of the popular state-of-the-art optimization algorithms.
July 2018
A minimax linear programming model for dispatching rule selection
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Gholam R. Amin, Ahmed El-Bouri Dispatching rule selection is an important problem in production scheduling. This paper introduces a minimax linear programming (LP) model for dispatching rule selection in the presence of multiple criteria. The multi-criteria dispatching rule selection problem is first converted into a preference voting system, and a minimax LP model is then introduced for solving the corresponding problem. The advantage of this conversion is that it provides a way to identify dispatching rules that are moderately good in all criteria, rather than selecting dispatching rules that are good respect to only a few variables. An experimental study considering two different production priority settings is used to show the applicability of the proposed method.
July 2018
Information sharing and information concealment in the presence of a dominant retailer
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Ye Wang, Wansheng Tang, Ruiqing Zhao The dramatic development of large retail outlets has transferred the dominant power from suppliers to retailers, who possess more plentiful and more accurate information in order to take preemptive action. To better understand the issue of information dissemination in the presence of a dominant retailer, we model a market comprising such a dominant incumbent retailer, who knows the terminal market demand as private information and is entitled to dictate her wholesale price while competing in terms of quantity with a weaker entrant retailer. Both retailers purchase products sequentially from a common upstream supplier, who has the power to set the wholesale price only for the entrant. Our analyses of Stackelberg games corresponding to two mechanisms, i.e., information sharing and information concealment scenarios, reveal interesting insights regarding the impact of information dissemination. The analytic solutions confirm that the incumbent absolutely reigns supreme through the wholesale price and order quantity. Both the extent of stabilization of the terminal market demand and the value of accurate information influence the participants’ decisions and their relative profits. We unexpectedly discover that the incumbent does not always benefit from information concealment and that the supplier and entrant do not always prefer information sharing. These results are distinctive and explicable. Moreover, the incumbent, the supplier and the entrant could realize the splendid opportunity of a “double-win” or even a “triple-win” situation in certain circumstances.
July 2018
Genetic fuzzy schedules for charging electric vehicles
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Jorge Garc
July 2018
A design process for the adoption of composite materials and supply chain reconfiguration supported by a software tool
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Adrian E. Coronado Mondragon, Christian E. Coronado Mondragon, Paul J. Hogg, Nuria Rodr
July 2018
Minimizing makespan for no-wait flowshop scheduling problems with setup times
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Kuo-Ching Ying, Shih-Wei Lin This study investigates no-wait flowshop scheduling problems with sequence-independent and sequence-dependent setup times aimed at minimizing the makespan. We propose an efficient two-phase matheuristic, which can optimally solve all tested instances of three existing benchmark problem sets and a new generated large-sized test problem set, with up to 20-machine and 2000-job test instances, in acceptable computational times. This is a dramatic improvement over all previously known algorithms. In view of the strongly NP-complete nature of the two problems addressed herein, this study contributes an exact method that can find optimal solutions for solving these problems with the efficiency necessary to meet real-world scheduling requirements.
July 2018
ADOPT: Combining parameter tuning and Adaptive Operator Ordering for solving a class of Orienteering Problems
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Aldy Gunawan, Hoong Chuin Lau, Kun Lu Two fundamental challenges in local search based metaheuristics are how to determine parameter configurations and design the underlying Local Search (LS) procedure. In this paper, we propose a framework in order to handle both challenges, called ADaptive OPeraTor Ordering (ADOPT). In this paper, The ADOPT framework is applied to two metaheuristics, namely Iterated Local Search (ILS) and a hybridization of Simulated Annealing and ILS (SAILS) for solving two variants of the Orienteering Problem: the Team Dependent Orienteering Problem (TDOP) and the Team Orienteering Problem with Time Windows (TOPTW). This framework consists of two main processes. The Design of Experiment (DOE) process, which is based on a $2 k$ factorial design, determines important parameters to tune and the best configuration for those parameters. The ADOPT process accommodates a reinforcement learning mechanism (based on Learning Automata) that calculates the probability of selecting an operator of LS. The probability values would be used to generate a sequence/order of operators for the next LS iteration, based on three different ordering strategies: rank-based, random and fitness proportionate selections. Our computational results show the superiority of the ADOPT framework with the fitness proportionate selection strategy against other ordering strategies in solving benchmark instances. In general, SAILS with the fitness proportionate selection strategy is competitive and comparable to the state-of-the-art algorithms. The proposed framework is able to improve the performances of both ILS and SAILS by discovering 11 new best known solutions of the benchmark TOPTW instances.
July 2018
The two echelon open location routing problem: Mathematical model and hybrid heuristic
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Khosro Pichka, Amirsaman H. Bajgiran, Matthew E.H. Petering, Jaejin Jang, Xiaohang Yue Multi echelon distribution systems have become more common in recent years. This paper addresses the two echelon open location routing problem (2E-OLRP) which is a variant of the two echelon location routing problem (2E-LRP). This problem seeks to find a minimum-cost set of vehicle routes that do not return to the depot in the first echelon and do not return to satellites in the second echelon due to the presence of individual contractors and third party logistics (3PL) providers. In spite of the large amount of research on LRPs, the 2E-OLRP has received very little attention. Three flow-based mixed-integer linear programs and a hybrid heuristic algorithm are proposed to deal with this problem. Extensive experiments evaluate the effectiveness of these methods.
July 2018
A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Mahmood Pariazar, Mustafa Y. Sir We develop a multi-objective stochastic programming model to explore tradeoffs between costs and risk in the supply chain in the event a disruption occurs. We explicitly consider network configuration and operating cost under normal conditions, cost of unsatisfied demand, cost of shipping tainted products to a customer, and quality inspection cost as conflicting goals to be minimized simultaneously. We analyze different disruption scenarios to determine the best supplier selection and inspection strategies to mitigate the effect of disruptions on supply availability and quality. Even the single-objective version of this problem is NP-hard; thus, we propose a genetic algorithm-based search method to identify Pareto-optimal supply chain configurations. We use data envelopment analysis for calculating the fitness value of various supply chain configurations. The proposed approach efficiently yields high-quality supply chain designs, allowing the decision maker to determine an appropriate tradeoff between various costs.
July 2018
A game theory approach to online lead generation for oligopoly markets
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Aneesh Zutshi, Diogo Mota, Antonio Grilo, Marta Faias Digital marketing has received much attention from most firms recently. With the increasing competition and exigency, marketing managers’ need for reliable and scientifically supported decision systems to assist them has never been greater. This paper presents a management model for estimating the quantity of online leads they should generate in a given period of time in order to achieve their goal, measured in terms of contracts gained in the most effective and efficient way possible. Through the application of Game Theory, the strategies of the rival firms are taken into account to provide marketing managers with a set of reliable possible decisions that can provide a competitive advantage. Results show a clear improvement of the effectiveness and efficiency of the decisions. In certain scenarios, an increase in the quantity of online generated leads by a firm leads to a positive impact on the firm’s and the competitors’ sales. Results obtained from the Nash and Stackelberg equilibria show that the Stackelberg equilibrium is more efficient, given the higher expected profits it derives and the follower in the Stackelberg equilibrium yields a higher expected profit than the leader or first mover.
July 2018
A constraint programming approach for solving unrelated parallel machine scheduling problem
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Ridvan Gedik, Darshan Kalathia, Gokhan Egilmez, Emre Kirac This paper addresses the non-preemptive unrelated parallel machine scheduling problem (PMSP) with job sequence and machine dependent setup times. This is a widely seen NP-hard (non-deterministic polynomial-time) problem with the objective to minimize the makespan. This study provides a noval constraint programming (CP) model with two customized branching strategies that utilize CP’s global constraints, interval decision variables, and domain filtering algorithms. The performance of the CP model is evaluated against the state-of-art algorithms. In addition, we compare the performance of the default branching method in the CP solver against the two customized variants. In terms of average solution quality, the computational results indicate that the CP model slightly outperforms all of the state-of-art algorithms in solving small problem instances, is able to prove the optimality of 283 currently best-known solutions. It is also effective in finding good quality feasible solutions for the larger problem instances.
July 2018
Distribution product packaging to maximize net revenue
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Xiubin Bruce Wang, Yihua Li, Luca Quadrifoglio, Kai Yin This paper studies a problem in which a distribution center of a chain store packages a small subset of a large number of available products (e.g. DVDs) to distribute to its local stores. Only a limited number of different packages are allowed. We determine what products are in each package and what package to distribute to each store for revenue maximization. A column generation method is developed in which the sub-problem generates candidate packages and corresponding stores to serve while the master problem determines the final packages on the production lines. Two heuristic methods are proposed to generate the candidate packages for the sub-problem. Bounds are derived for both the optimal number of packages and the total revenue in order to expedite the solution. The algorithm shows great promise with operational data from a chain store that serves several thousand retail locations.

Balancing mixed-model assembly systems in the footwear industry with a variable neighbourhood descent method
Publication date: July 2018
Source:Computers & Industrial Engineering, Volume 121 Author(s): Parisa Sadeghi, Rui Diogo Rebelo, Jos
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