Forthcoming articles

European Journal of Industrial Engineering

European Journal of Industrial Engineering (EJIE)

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European J. of Industrial Engineering (21 papers in press)

Regular Issues

  • The impact of online sales in centralized and decentralized dual-channel supply chains   Order a copy of this article
    by Subrata Saha 
    Abstract: This paper studies a supply chain structure featuring two different types of distribution channels through which manufacturers sell products. The centralised and decentralised distribution channels considered in this study are affected by online sales outside the structured channels. In the centralised distribution channel, two retail stores located in geographically distinct markets are operated by a single owner. In the decentralised distribution channel, two retailers independently operate two retail stores. In the non-cooperative scenario, the manufacturer always prefers the decentralised distribution channel irrespective of whether an online channel is used. To achieve channel coordination, a revenue-sharing contract is applied, but it can be used to coordinate only the decentralised distribution system. Therefore, a modified revenue-sharing contract is proposed to coordinate the centralised distribution system. The analytical study reveals that without coordination among the channel members, the manufacturer always earns maximum profit in decentralised distribution systems. However, if the supply chain is coordinated, then the manufacturer receives more benefits from using the centralised distribution systems under certain conditions. Propositions are presented to describe the characteristics of distribution structures, and to provide meaningful management guidelines for coordinating them. Extensive numerical investigations are also presented.
    Keywords: Supply chain management; Dual-channel supply chain; Revenue sharing contract; Pricing strategy; Stackelberg.
    DOI: 10.1504/EJIE.2018.10011153
     
  • A Resource Allocation Model to Choose the Best Portfolio of Economic Resilience Plans: A PossibilisticStochastic Programming Model   Order a copy of this article
    by Shima Pashapour, Ali Azadeh, Ali Bozorgi-Amiri, Abbas Keramati, Seyed Farid Ghaderi 
    Abstract: Economic resilience is defined as a tool capable of reducing the losses caused by disasters. It can be defined in two major concepts. Static economic resilience is the effective allocation of available resources and dynamic economic resilience refers to accelerating the recovery process through the repair and rebuilding of the capital stock. In this research, the performance of a petrochemical plant in the face of crisis is investigated. For this, a bi-objective mathematical model that considers cost and resilience capability as objective functions is developed to choose the best portfolio of static and dynamic plans. To solve the mathematical model, a weighted augmented -constraint method and a Multi-Stage Possibilistic Stochastic Programming approach (MSPSP) are employed. The numerical results showed that the proposed approach is effective in optimizing the performance of a petrochemical plant in facing crisis situations and in choosing the best portfolio of economic resilience plans.
    Keywords: Petrochemical Plant; Economic Resilience; Resilience Capability; Multi-Stage Possibilistic Stochastic Programming (MSPSP); Resource Allocation.

  • A multi-criteria spatial analysis using GIS to evaluate potential sites for a new border gate on Turkey's Syria frontier   Order a copy of this article
    by Mehmet Kabak, Eren Özceylan, Mehmet Erbaş, Cihan Çetinkaya 
    Abstract: After the internal disturbance in Syria in 2011, many Syrian refugees migrated to Turkey progressively, and the Turkish Government provided humanitarian aid to people in Syria. These incidents caused a huge amount of density on current border gates. Also increasing potential terrorist attacks and growing frontier infringements also create a need for a new border gate on Turkeys Syria frontier. Thus, a four-step hybrid solution approach is developed for this problem. This approach starts with determination of selection criteria; then the spatial database of these criteria is created by using a Geographical Information System. In the third step, the DEMATEL technique is applied to assign importance levels to the criteria. Lastly, MULTIMOORA technique is used to rank the potential sites. The results indicate that, recommended potential sites are more suitable than current border gates. This paper can serve as a scientific base while selecting the optimal site for border gates.
    Keywords: DEMATEL; border check-point; GIS; Multi-criteria decision making; MULTIMOORA; Site location.

  • A robust stochastic bi-objective model for blood inventory-distribution management in a blood supply chain   Order a copy of this article
    by Hadis Derikvand, Seyed Mohammad Hajimolana, Armin Jabbarzadeh, Seyed Esmaeil Najafi 
    Abstract: Providing blood units in a blood supply chain should be effective, appropriate and well-organized since it directly affects the health of individuals and, if not provided promptly, can even lead to the death of patients. This study presents a robust stochastic bi-objective programming model for an inventory-distribution problem in a blood supply chain, the first objective of which attempts to minimize the total number of shortages and wastages and the second objective maximizes the connection between two different types of hospitals. The blood supply chain under investigation includes one blood center, type-1 and type-2 hospitals, and patients. In doing so, the robust programming approach has been applied in order to minimize the expected value as well as the variance of the number of shortages and wastages in the whole blood supply chain and while simultaneously penalizing the solutions infeasibility due to the uncertain parameter(s) and maximizing the expected value and the variance of connection between two types of hospitals. Mathematical approximations are employed to remove the non-linear terms, and a hybrid solution approach, combining the -constraint and the Lagrangian relaxation method, is applied to solve the proposed bi-objective model. Finally, the proposed robust stochastic optimization model is implemented and analyzed using the data inspired by a real case study in Iran to show its potential applicability as to decisions concerning the blood inventory-distribution problem.
    Keywords: blood supply chain; blood inventory-distribution management; robust programming approach; ɛ-constraint; lagrangian relaxation approach.

  • Two-machine chain-reentrant ow shop with the no-wait constraint
    by Karim Amrouche, Mourad Boudhar, Nazim Sami 
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  • Maximizing Reward from a Team of Surveillance Drones: a Simheuristic Approach to the Stochastic Team Orienteering Problem
    by Javier Panadero, Angel A. Juan, Christine Currie 
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  • Project Management under Uncertainty: using flexible resource management to exploit schedule flexibility
    by João Faria, Madalena Araújo, Erik Demeulemeester, Anabela Tereso 
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  • Efficient matheuristic for the generalized multiple knapsack problem with setup
    by Yassine Adouani, Bassem Jarboui, Malek Masmoudi 
    Abstract: This paper introduces a new variant of the knapsack problem with setup (KPS). We refer to it as the generalized multiple knapsack problem with setup (GMKPS). GMKPS originates from industrial production problems where the items are divided into classes and processed in multiple periods. We refer to the particular case where items from the same class cannot be processed in more than one period as the multiple knapsack problem with setup (MKPS). First, we provide mathematical formulations of GMKPS and MKPS and provide an upper bound expression for the knapsack problem. We then propose a matheuristic that combines variable neighborhood descent (VND) with integer programming (IP). We consider local search techniques to assign classes to knapsacks and apply the IP to select the items in each knapsack. Computational experiments on randomly generated instances show the efficiency of our matheuristic in comparison to the direct use of a commercial solver.
    Keywords: Knapsack problems; Setup; matheuristic; Variable neighborhood descent; Integer programming.

  • Design of facility location-allocation network with an emergency backup supply system
    by Jae-Dong Hong, Ki-Young Jeong 
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  • A hybrid genetic tabu search algorithm for minimizing total completion time in a flexible job shop scheduling problem
    by Asma Fekih, Hatem Hadda, Imed Kacem, Atidel B. Hadj-Alouane 
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  • A possibilistic model for production planning with uncertain demand
    by Maria Laura Cunico, Aldo Vecchietti 
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  • A Stochastic Maritime Transportation-Inventory Problem with Gamma, Exponential, and Uniform Demand Distributions
    by Hossein. M. Soroush, Salem M. Al-Yakoob, Fatemah A. Alqallaf 
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  • A Systematic Literature Review of the Design of Intermodal Freight Transportation Networks Addressing Location-Allocation Decisions
    by Anny Del Mar Agamez-Arias, José P. García-Sabater, Angel Ruiz, José Moyano-Fuentes 
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  • Two-phase differential evolution for solving emergency response supplies optimization problem
    by Qi Cao, K.M. Leung, Wenhua Hou 
    Abstract: A material supply model is constructed for serious disasters in which a large number of supply centers and disaster areas are involved. We introduce a new method referred to as two-phase differential evolution (TPDE) to solve this kind of complex nonlinear programming problem. In constraint handling phase, the goal is to explore the parameter space to identify a feasible solution quickly. In optimum seeking phase, the aim is to gradually improve the quality of current best solution. Different differential evolution schemes and special handling techniques are utilized in the two phases. Extensive numerical optimization experiments are conducted where TPDE is compared with results obtained from using commercial software and three evolutionary optimization methods. We determine that TPDE is always able to find a feasible solution with fewer generations and the optimal solution almost always ranks as the best. This work is beneficial to address large-scale nonlinear optimization problems with constraints. A material supply model is constructed for serious disasters in which a large number of supply centers and disaster areas are involved. We introduce a new method referred to as two-phase differential evolution (TPDE) to solve this kind of complex nonlinear programming problem. In constraint handling phase, the goal is to explore the parameter space to identify a feasible solution quickly. In optimum seeking phase, the aim is to gradually improve the quality of current best solution. Different differential evolution schemes and special handling techniques are utilized in the two phases. Extensive numerical optimization experiments are conducted where TPDE is compared with results obtained from using commercial software and three evolutionary optimization methods. We determine that TPDE is always able to find a feasible solution with fewer generations and the optimal solution almost always ranks as the best. This work is beneficial to address large-scale nonlinear optimization problems with constraints.
    Keywords: evolutionary computation; large-scale optimization; emergency logistics; differential evolution.

  • A Multi-Objective Approach in Expanding the Pre-positioning Warehouse Networks in Humanitarian Logistics
    by Ertan Yakıcı, Mumtaz Karatas, Serhan Duran 
    Abstract: In this study, we focus on the structure of the pre-positioning warehouse networks which have a great effect on the response to a disaster. We determine the pre-positioning warehouse network configuration of CARE International with a multi-objective approach using the recent decade data. In addition to the minimization of the average response time of an item, we also consider the maximum response time and maximum water delivery time as additional objectives. After analysis of the non-dominated solutions, we conclude that CARE International should open a warehouse in Kenya and pre-position 39-44% of all relief items other than tents to this location while starting to operate the Denmark warehouse instead of the Dubai warehouse.
    Keywords: Pre-positioning; Humanitarian Relief Logistics; Warehouse Location; Network Expansion; Multi-Objective.

  • SCHEDULING IN ROBOTIC CELLS WITH TIME WINDOW CONSTRAINTS
    by Wassim Zahrouni, Hichem Kamoun 
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  • K-means clustering combined with principa component analysis for material proling in automotive supply chains
    by JoãoN.C. Gonçalves, Paulo Cortez, M. .Sameiro Carvalho 
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  • A HYBRID MODEL FOR MIX-BANK BUFFER CONTENT DETERMINATION IN AUTOMOBILE INDUSTRY   Order a copy of this article
    by Elif Elcin Gunay, Ufuk Kula 
    Abstract: In mixed-model automobile assembly lines, paint defective vehicles are the main reason for unintentional sequence alteration that stirs up the production sequence so that resequencing is required. This study aims to decide optimal spare vehicles that are held in the mix-bank buffer to be replaced instead of defective vehicles in order to regain the production sequence. We develop a hybrid solution methodology in which optimal spare vehicle content is determined by genetic algorithm (GA) and the releasing order of the vehicles to final assembly (FA) are decided by stochastic mixed-integer-programming (MIP) model. In addition to discussing the efficiency of the hybrid model, the following insights were gained. (i) Not only FA but also paint shop constraints can be considered in production sequence determination when mix-bank buffer is efficiently used. (ii) The decrease in defect rate improves sequence restoration linearly. (iii) Effect of an additional lane on SSAR increase is diminishing.
    Keywords: Mixed-model assembly lines; genetic algorithm; sample average approximation; mixed-integer programming; mix-bank buffer.
    DOI: 10.1504/EJIE.2020.10029433
     
  • A Stochastic Queueing - Inventory System with Renewal Demands and Positive Lead Time   Order a copy of this article
    by Sivakumar B, Saranya Navaneethan, Keerthana Murugan 
    Abstract: In this article, we analyze a stochastic inventory system with a service facility. The customer arrives according to a renewal process and demanded item is delivered to the customer after performing an exponentially distributed service time. An (s; S) type ordering policy is adopted with exponentially distributed lead times. We derive the stationary probability distribution for the number of customers in the system and inventory level at arrival epoch and at an arbitrary time point. We derive some system performance measures in the steady state and using these system performance measures we have calculated the long-run expected cost rate. Since the long run expected cost rate is highly complex, we use mixed integer distributed ant colony optimization (ACO) to obtain the optimal values. We also conduct a sensitivity analysis to illustrate the effects of parameters and cost on the optimal values.
    Keywords: Queueing-Inventory System; (s; S) policy; MIDACO Algorithm; Infi nite waiting hall.
    DOI: 10.1504/EJIE.2020.10029435
     
  • A Bayesian Networks Approach to Fleet Availability Analysis Considering Managerial and Complex Causal Factors   Order a copy of this article
    by Sharareh Taghipour, Abdollah Abdi 
    Abstract: Availability analysis of a fleet of assets requires modelling uncertainty sources that affect equipment reliability and maintainability. These uncertainties include complex, managerial causalities and risks which have been seldom examined in the asset management literature. The objective of this study is to measure the reliability, maintainability and availability of a fleet, considering the effect of common causal factors and extremely rare or previously unobserved events. We develop a fully probabilistic availability analysis model using hybrid Bayesian networks (BNs), to capture managerial, organizational and environmental causal factors that influence failure or repair rate, as well as those that affect both failure and repair rates simultaneously. We have demonstrated the application of the model using a fleet of excavators located in Toronto, Ontario. The prediction accuracy of the proposed model is evaluated by use of a measure of prediction error.
    Keywords: Fleet; Availability; Failure rate; Repair rate; Causal factors; Bayesian networks.
    DOI: 10.1504/EJIE.2020.10029681
     
  • Multi-Type Electric Vehicle Relocation Problem Considering Required Battery-Charging Time   Order a copy of this article
    by Byung-In Kim, Ivan Kristianto Singgih 
    Abstract: This research discusses an electric vehicle (EV) relocation problem, wherein multiple types of EVs are transported using heterogeneous trucks. The initial position, battery level of the EVs, and the required number of EVs and empty parking slots at each station are provided as inputs. Relocations are performed during the night, while no EVs are used. Before the end of the relocation planning horizon, each EV must be charged to a certain battery level. The charging process can only be performed when the EV is not being transported. The objectives are to minimise the total transportation costs, the total truck fixed costs, and the total unsatisfied empty parking slot requirements while ensuring that all EV demands are satisfied. A mixed-integer linear programming (MILP) model and construction and improvement heuristic approaches are proposed. The results of the computational experiments indicate that the proposed approaches perform well.
    Keywords: Electric Vehicle Relocation; Battery Charging; Heterogeneous Truck; Heuristic; Adaptive Neighbourhood Search; Mixed Integer Linear Programming.
    DOI: 10.1504/EJIE.2020.10029682