International Journal of Industrial Engineering & Production Research

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Currently, due to increased competition in the services and manufacturing, many companies are trying to lower price and good quality products offer to the market. In this paper, the multi-criteria decision-making techniques to evaluate and select the best supplier from among the existing suppliers. The first, hierarchical structure for selecting suppliers of raw materials used and the analytic hierarchy process to obtain the relative importance of quantitative and qualitative criteria related to green supply chain is applied.  Then, a fuzzy TOPSIS technique any raw material suppliers is ranked according to the relevant criteria. Finally, with regard to the weight of suppliers and demand of raw material and resource constraints by a multi-objective mathematical model, optimum order is determined. The objectives are to minimize the total cost, maximize amount of purchases of desirable suppliers and minimize of raw materials required are not provide. The proposed method in a case study used Food Company and the relevant results are expressed.

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In the recent decade, special attention is paid to reverse logistic due to economic benefits of recovery and recycling of used products as well as environmental legislation and social concerns. On the other hand، many researches claim that separately and sequential planning of forward and reverse logistic causes sub-optimality. Effective transport activities are also one of the most important components of a logistic system and it needs an accurate planning. In this study, a mixed integer linear programming model is proposed for integrated forward / reverse supply chain as well as vehicles routing. Logistic network which is used in this paper is a multi-echelon integrated forward /reverse logistic network which is comprised capacitated facility, common facilities of production/recovery and distribution/collection, disposal facilities and customers. The proposed model is multi-period and multi-product with the ability to consider several facilities in each level. Various types of vehicle routing models are also included such as multi-period routing, multi-depot, multi-products, routing with simultaneous delivery and pick-up, flexible depot assignment and split delivery. The model results present the product flow between the various facilities in forward and reverse direction throughout the planning horizon with the objective minimization of total cost. Numerical example for solving the model using GAMS shows that the proposed model could reach the optimal solution in reasonable time for small and medium real world’s problems.  

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The eternal need for humans' blood as a critical commodity makes the healthcare systems attempt to provide efficient blood supply chains (BSCs) by which the requirements are satisfied at the maximum level. To have an efficient supply of blood, an appropriate planning for blood supply chain is a challenge which requires more attention. In this paper, we address a mixed integer linear programming model for blood supply chain network design (BSCND) with the need for making both strategic and tactical decisions throughout a multiple planning periods. A robust programming approach is devised to deal with inherent randomness in parameters data of the model. To illustrate the usefulness of the model as well as its solution approach, it is tested into a set of numerical examples, and the sensitivity analyses are conducted. Finally, we employ two criteria: the mean and standard deviation of constraint violations under a number of random realizations to evaluate the performance of both the proposed robust and deterministic models. The results imply the domination of robust approach over the deterministic one.

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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, sustainable supply chain management (SSCM) has become one of the important subjects in the industry and academia. Supplier selection, as a strategic decision, plays a significant role in SSCM. Researchers use different multi-criteria decision making (MCDM) methods to evaluate and select sustainable suppliers. In the previous studies, evaluation is solely based on the desirable features of suppliers and their risks are neglected. Therefore, current research uses failure mode and effects analysis (FMEA) as a risk analysis technique to consider supplier's risk in combination with the MCDM method. Practically, this study operated in two main stages. In the first stage, the score of the suppliers obtains by integration Fuzzy MOORA and FMEA. In the second stage, the output of the previous stage used as input parameters in developed mix-integer linear programming to select suppliers and order optimum quantity. Finally, to demonstrate the effectiveness of the proposed approach, a case study in a chemical industry and sensitivity analysis is presented.  

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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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Nowadays, with a growing body of features and technologies, supply chain management is being widely used to coordinate and optimize key processes such as increasing customer satisfaction, facilitating the processes, and enhancing product quality. In recent years, the emergence of IT and new business environments has led to the development of electronic supply chains. In order to use and benefit from the privileges of e-supply chains, organizations must identify the key factors in the implementation of e-supply chain management so that they can monitor the organization's current and future activities and take action to identify and modify and fix any bugs. The present study aimed at identifying these factors. Based on the available theoretical foundations and expert opinions, the factors affecting the implementation of electronic supply chain management were identified in seven factors with 31 indicators. To determine the weight of the identified factors considering the lack of independence between them, an integrated type-2 fuzzy AHP and type-2 fuzzy DEMATEL approach was used. Results showed that computer-based technology, infrastructure, inter-organizational relationships, and information are the most important factors.

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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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This paper considers the advertising, pricing, and service decisions simultaneously to coordinate the supply chain with a manufacturer and a retailer. The amount of market demand is influenced by advertising, pricing and service decisions. In this paper, three well-known approaches to the game theory, including the Nash, the Stackelberg-retailer, and the cooperative game are exploited to study the effects of these policies on the supply chain. Using these approaches, we identify optimal strategies in each case for the manufacturer and the retailer. Then, we will compare the outcomes of each strategy thus developed. The results show that, compared with the Nash game, the Stackelberg-retailer game yields higher profits for the retailer, the manufacturer, and the whole system. The cooperative game yields the highest profits. Finally, the Nash bargaining model will be presented and explored to investigate the possibilities for profit sharing.

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In this paper, a comprehensive mathematical model for designing an electric power supply chain network via considering preventive maintenance under risk of network failures is proposed. The risk of capacity disruption of the distribution network is handled via using a two-stage stochastic programming as a framework for modeling the optimization problem. An applied method of planning for the network design and power generation and transmission system via considering failures scenarios, as well as network preventive maintenance schedule, is presented. The aim of the proposed model is to minimize the expected total cost consisting of power plants set-up, power generation and the maintenance activities. The proposed mathematical model is solved by an efficient new accelerated Benders decomposition algorithm. The proposed accelerated Benders decomposition algorithm uses an efficient acceleration mechanism based on the priority method which uses a heuristic algorithm to efficiently cope with computational complexities. A large number of considered scenarios are reduced via using a k-means clustering algorithm to decrease the computational effort for solving the proposed two-stage stochastic programming model. The efficiencies of the proposed model and solution algorithm are examined using data from the Tehran Regional Electric Company. The obtained results indicate that solutions of the stochastic programming are more robust than the obtained solutions provided by a deterministic model.

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In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms. 

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In this paper, the fuzzy multi-depot vehicle routing problem with simultaneous pickup and delivery (FMDVRP-SPD) is investigated. The FMDVRP-SPD is the problem of allocating customers to several depots, so that the optimal set of routes is determined simultaneously to serve the pickup and the delivery demands of each customer within scattered depots. In the problem, both pickup and delivery demands of customers are fuzzy variables. The objective of FMDVRP-SPD is to minimize the total cost of a distribution system including vehicle traveling cost and vehicle fixed cost. To model the problem, a fuzzy chance-constrained programming model is proposed based on the fuzzy credibility theory. A heuristic algorithm combining K-means clustering algorithm and ant colony optimization is developed for solving the problem. To achieve an appropriate threshold value of parameters of the model, named “vehicle indexes”, and to analyze their influences on the final solution, numerical experiments are carried out.

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This study considers pricing, production and transportation decisions in a Stackelberg game between three-stage, multi-product, multi-source and single-period supply chains called leader and follower. These chains consist of; manufacturers, distribution centers (DCs) and retailers. Competition type is horizontal and SC vs. SC. The retailers in two chains try to maximize their profit through pricing of products in different markets and regarding the transportation and production costs. A bi-level nonlinear programming model is formulated in order to represent the Stackelberg game. Pricing decisions are based on discrimination pricing rules, where we can put different prices in different markets. After that the model is reduced to single-level nonlinear programming model by replacing Karush-Kuhn-Tucker conditions for the lower level (follower) problem. Finally, a numerical example is solved in order to analyze the sensitivity of effective parameters on price and profit.

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Managing income is a considerable dimension in supply chain management in current economic atmosphere. Real world situation makes it inevitable not to design or redesign supply chain. Redesign will take place as costs increase or new services for customers’ new demands should be provided. Pricing is an important fragment of Supply chain due to two reasons: first, represents revenue based each product and second, based on supply-demand relations enables Supply chain to provide demands by making suitable changes in facilities and their capacities. In this study, Benders decomposition approach used to solve multi-product, multi-echelon and multi-period supply chain network redesign including price-sensitive customers.

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Logistics planning in disaster response phase involves dispatching commodities such as medical materials, personnel, food, etc. to affected areas as soon as possible to accelerate the relief operations. Since transportation vehicles in disaster situations can be considered as scarce resources, thus, the efficient usage of them is substantially important. In this study, we provide a dynamic vehicle routing model for emergency logistics operations in the occurrence of natural disasters. The aim of the model is to find optimal routes for a fleet of vehicles to give emergency commodities to a set of affected areas by considering the existence of more than one arc between each two nodes in the network (multi-graph network). Proposed model considers FIFO property and focused on minimization of waiting time and total number of vehicles. Various problem instances have been provided to indicate the efficiency of the model. Finally, a brief sensitivity analysis is presented to investigate the impact of different parameters on the obtained solutions.

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This paper addresses supplier selection and order allocation problem while considering the losses arising from the risk of sanction in Iran’s Oil & Gas Drilling Industry. In the proposed study, two general classes of items and two different classes of suppliers are considered. AHP is first used to rank the potential suppliers. Then, a multi-objective linear programming model is proposed to determine the best suppliers and their allocated orders. A numerical example is presented to demonstrate the applicability of the proposed model.

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Nowadays, several methods in production management mainly focus on the different partners of supply chain management. In real world, the capacity of planes is limited. In addition, the recent decade has seen the rapid development of controlling the uncertainty in the production scheduling configurations along with proposing novel solution approaches. This paper proposes a new mathematical model via strong recent meta-heuristics planning. This study firstly develops and coordinates the integrated air transportation and production scheduling problem with time windows and due date time in Fuzzy environment to minimize the total cost. Since the problem is NP-hard, we use four meta-heuristics along with some new procedures and operators to solve the problem. The algorithms are divided into two groups: traditional and recent ones. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as traditional algorithms, also Keshtel Algorithm (KA) and Virus Colony Search (VCS) as the recent ones are utilized in this study. In addition, by using Taguchi experimental design, the algorithm parameters are tuned. Besides, to study the behavior of the algorithms, different problem sizes are generated and the results are compared and discussed.

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In this article, we propose a special case of two-echelon location-routing problem (2E-LRP) in cash-in-transit (CIT) sector. To tackle this realistic problem and to make the model applicable, a rich LRP considering several existing real-life variants and characteristics named BO-2E-PCLRPSD-TW including different objective functions, multiple echelons, multiple periods, capacitated vehicles, distribution centers and automated teller machines (ATMs), different type of vehicles in each echelon, single-depot with different time windows is presented. Since, routing plans in the CIT sector ought to be safe and efficient, we consider the minimization of total transportation risk and cost simultaneously as objective functions. Then, we formulate such complex problem in mathematical mixed integer linear programming (MMILP). To validate the presented model and the formulation and to solve the problem, the latest version of ε-constraint method namely AUGMECON2 is applied. This method is especially efficient for solving multi objective integer programing (MOIP) problems and provides the exact Pareto fronts. Results substantiate the suitability of the model and the formulation.
 

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Supplying of blood and blood products is one of the most challenging issues in the healthcare system since blood is as extremely perishable and vital good and donation of blood is a voluntary work. In this paper, we propose a two-stage stochastic selective-covering-inventory-routing (SCIR) model to supply whole blood under uncertainty. Here, set of discrete scenarios are used to display uncertainty in stochastic parameters. Both of the fixed blood center and bloodmobile facilities are considered in this study. We suppose that the number of bloodmobiles is indicated in the first stage before knowing which scenario is occurred. To verify the validation of the presented SCIR model to supply whole blood, we examine the impact of parameters variation on the model outputs and cost function using the CPLEX solver. Also the results of comparison between the stochastic approach and expected value approach are discussed.
 

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In today complex worldwide supply chains, intermediary organizations like Contract manufacturers and GPOs are mostly used. Well-known OEMs delegate their purchasing and procuring to these intermediaries. Because of their positive influence on supply chain efficiency, it is very important to investigate the role of intermediaries in today competitive supply chains. One important question arising about intermediaries is the conditions that the OEM controls his procurement or delegates this task to the intermediary organization?

To answer this question, this paper studies the equilibrium for component procurement strategies of two competing OEMs that produce substitutable products. Each OEM may either directly procure the input from the component supplier, or delegate the procurement task to the contract manufacturer. We analyze the OEMs’ procurement game under two contracting power schemes in such a supply chain: the supplier Stackelberg, where the component supplier acts as the Stackelberg leader, and the OEM Stackelberg, where the OEMs are the first movers.

We show that, the smaller OEM always prefers direct control of component procurement. This is because the OEM will receive a lower component price if the component supplier can price discriminate the OEMs. In contrast, the larger OEM’s preference depends on the contracting power scheme. Under the supplier Stackelberg, the larger OEM never prefers direct procurement; however, under the OEM Stackelberg, the larger OEM may have incentives to use direct procurement under reasonable conditions. This implies that a shift of the market power from the supplier to the OEMs may lead to more OEMs deviating from delegation to direct control.