International Journal of Industrial Engineering & Production Research

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Hub covering location problem, Network design,   Single machine scheduling, Genetic algorithm,   Shuffled frog leaping algorithm

Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc.) through special facilities or transshipment points called hubs. In this paper, we consider a central mine and a number of hubs (e.g., factories) connected to a number of nodes (e.g., shops or customers) in a network. First, the hub network is designed, then, a raw materials transportation from a central mine to the hubs (i.e., factories) is scheduled. In this case, we consider only one transportation system regarded as single machine scheduling. Furthermore, we use this hub network to solve the scheduling model. In this paper, we consider the capacitated single allocation hub covering location problem (CSAHCLP) and then present the mixed-integer programming (MIP) model. Due to the computational complexity of the resulted models, we also propose two improved meta-heuristic algorithms, namely a genetic algorithm and a shuffled frog leaping algorithm in order to find a near-optimal solution of the given problem. The performance of the solutions found by the foregoing proposed algorithms is compared with exact solutions of the mathematical programming model .

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The Network Design Problem (NDP) is one of the important problems in combinatorial optimization. Among the network design problems, the Multicommodity Capacitated Network Design (MCND) problem has numerous applications in transportation, logistics, telecommunication, and production systems. The MCND problems with splittable flow variables are NP-hard, which means they require exponential time to be solved in optimality. With binary flow variables or unsplittable MCND, the complexity of the problem is increased significantly. With growing complexity and scale of real world capacitated network design applications, metaheuristics must be developed to solve these problems. This paper presents a simulated annealing approach with innovative representation and neighborhood structure for unsplittable MCND problem. The parameters of the proposed algorithms are tuned using Design of Experiments (DOE) method and the Design-Expert statistical software. The performance of the proposed algorithm is evaluated by solving instances with different dimensions from OR-Library. The results of the proposed algorithm are compared with the solutions of CPLEX solver.  The results show that the proposed SA can find near optimal solution in much less time than exact algorithm.

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Network location models comprise one of the main categories of location models. These models have various applications in regional and urban planning as well as in transportation, distribution, and energy management. In a network location problem, nodes represent demand points and candidate locations to locate the facilities. If the links network is unchangeably determined, the problem will be an FLP (Facility Location Problem). However, if links can be added to the network at a reasonable cost, the problem will then be a combination of facility location and NDP (Network Design Problem) hence, called FLNDP (Facility Location Network Design Problem), a more general variant of FLP. In previous studies of this problem, capacity of facilities was considered to be a constraint while capacity of links was not considered at all. The proposed MIP model considers capacity of facilities and links as decision variables. This approach increases the utilization of facilities and links, and prevents the construction of links and location of facilities with low utilization. Furthermore, facility location cost (link construction cost) in the proposed model is supposed to be a function of the associated facility (link) capacity. Computational experiments as well as sensitivity analyses performed indicate the efficiency of the model.

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In this study, an optimization model is proposed to design a Global Supply Chain (GSC) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. Therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of Economic Cooperation Organization trade agreement (ECOTA) is considered. This competition condition is affected by disruption occurrence. The aim of the proposed model is to maximize the expected net after-tax profit of GSC under disruption and normal situation at the same time. To effectively deal with disruption, some practical strategies are adopted in the design of GSC network. The uncertainty of the business environment is modeled using the robust optimization technique based on the concept of uncertainty budget. To tackle the proposed Mixed-Integer Nonlinear Programming (MINLP) model, a hybrid Taguchi-based Memetic Algorithm (MA) with an adaptive population size is developed that incorporates a customized Adaptive Large Neighborhood Search (ALNS) as its local search heuristic. A fitness landscape analysis is used to improve the systematic procedure of neighborhood selection in the proposed ALNS. A numerical example and computational results illustrate the efficiency of the proposed model and algorithm in dealing with global disruptions under uncertainty and competition pressure.

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Flexible and dynamic supply chain network design problem has been studied by many researchers during past years. Since integration of short-term and long-term decisions in strategic planning leads to more reliable plans, in this paper a multi-objective model for a sustainable closed-loop supply chain network design problem is proposed. The planning horizon of this model contains multiple strategic periods so that the structure of supply chain can be changed dynamically during the planning horizon. Furthermore, in order to have an integrated design, several short-term decisions are considered besides strategic network design decision. One of these short-term decisions is determining selling price and buying price in the forward and reverse logistics of supply chain, respectively. Finally, an augmented e-constraint method is used to transform the problem to a single-objective model and an imperialist competitive algorithm is presented to solve large scale problems. The results’ analysis indicates the efficiency of the proposed model for the integrated and dynamic supply chain network design problem. 

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Recently, urbanization has been expanded rapidly in the world and a number of metropolitan areas have been appeared with a population of more than 10 million people. Because of dense population in metropolitan and consequently increasing the delivery of goods and services, there has been a lot of problems including traffic congestion, air pollution, accidents and high energy consumption. This made some complexities in distribution of urban goods; Therefore, it is essential to provide creative solutions to overcome these complexities. City logistics models can be effective in solving these complexities.

In this paper, concepts and definitions related to city logistics are explained to provide a mathematical model in order to design city logistics distribution network aim at minimizing response times. This objective is effective for goods and emergency services, especially in times of crisis and also for goods that are delivered as soon as possible. This is a three-level network and has been used in modeling of queuing theory. To validate the model, a numerical example has been established and results of the model have been explained using BARON solver in Gams software. Finally, conclusions and recommendations for future research are presented.

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A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.

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One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered.
This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages.  At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.

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Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.