Solving multi-objective supplier selection and quota allocation problem under disruption using a scenario-based approach

Nowadays, experts believe there are abundant sources of risks in a supply chain. An important group of risks against a supply chain is the disruption risks group, which disturbs the flow of material in the chain and may lead to inefficiency in providing the final product in the supply chain. The aim of this article is to investigate the control of costs of disruption in a supply chain by considering the possibility of disruption. In fact, this research focuses on determining the best combination of suppliers and quota allocation with regards to disruption in suppliers. The proposed multi-objective mathematical model in this paper is a mixed-integer programming (MIP) model with objective functions to minimize transaction costs of suppliers, expected costs of purchasing goods, expected percentages of delayed products, expected returned products, and to maximize expected evaluation scores of the selected suppliers. Due to the uncertainty of demand and supplier disruption in the real world, their values are also considered uncertain; the proposed multi-objective model is studied by using a scenario-based stochastic programming (SP) method. In this method, all possible predictions for demand and disruption values are simultaneously included in the model; objective function results have more optimal value than a separate solution of the model for each predicted value

Developing dynamic maximal covering location problem considering capacitated facilities and solving it using hill climbing and genetic algorithm

The maximal covering location problem maximizes the total number of demands served within a maximal service distance given a fixed number of facilities or budget constraints. Most research papers have considered this maximal covering location problem in only one period of time. In a dynamic version of maximal covering location problems, finding an optimal location of P facilities in T periods is the main concern. In this paper, by considering the constraints on the minimum or maximum number of facilities in each period and imposing the capacity constraint, a dynamic maximal covering location problem is developed and two related models (A, B) are proposed. Thirty sample problems are generated randomly for testing each model. In addition, Lingo 8.0 is used to find exact solutions, and heuristic and meta-heuristic approaches, such as hill climbing and genetic algorithms, are employed to solve the proposed models. Lingo is able to determine the solution in a reasonable time only for small-size problems. In both models, hill climbing has a good ability to find the objective bound. In model A, the genetic algorithm is superior to hill climbing in terms of computational time. In model B, compared to the genetic algorithm, hill climbing achieves better results in a shorter time

Maximal Benefit Location Problem for A Congested System

Some servers are to be located at nodes of a network. Demand for services of these servers is located at each node and a subset of the nodes is to be chosen to locate one server in each. Each customer selects a server with a probability dependent on distance and a certain amount of benefit is achieved after giving service to the customer. Customers may waive receiving service with a known probability. The objective is to maximize the total benefit. In this paper, the problem is formulated, three solution algorithms are developed and applied to some numerical examples to analyze the results

Integration of supplier selection and resilient closed loop supply chain design and ranking based on fuzzy-MOORA-reference point method

Closed loop supply chain network design is of great important due to the legal requirements and economic benefits. Raw material suppliers are of the most important players of this supply chain. Most of the previous researchers studied the design problem separated from the supplier assessment. Some other criteria except for price, like the production process, the features of parts and reliability of supply have long term effects on the performance of supply chains. In this research a general closed loop supply chain network including production centres, disassembly, refurbishing, and disposal sites is considered. An integrated two-phase model is given so that in the first phase, the proposed fuzzy-MOORA-reference point method is applied to suppliers’ assessment and the results from this phase is used in the second phase. In the second phase, a three-objective mixed integer linear programming model is proposed in order to determine the eligible suppliers, the locations of refurbishing sites and the material flows between the supply chain members. The objective functions are maximizing profit, suppliers’ evaluation and resiliency scores. Unsatisfied demand of customers is lost. The numerical results show the validity of the model and the role of stockout option in reaching better solutions based on the LP-Metric method

A Supplier Selection Model for Social Responsible Supply Chain

Due to the importance of supplier selection issue in supply chain management (SCM) and ,also,  the increasing tendency of organizations to their social responsibilities, In this paper, we survey the supplier selection issue as a multi objective problem while considering the factor of corporate social responsibility (CSR) as a mathematical parameter. The purpose of this paper is to design a model so that suppliers are selected and quota is allocated to them while raising their social responsibility to the maximum expected extent. Supplier selection objectives such as cost minimization, quality maximization and on-time delivery maximization have already been surveyed. In this paper, we add objectives such as CSR maximization, maximization of advantages of domestic supplier selection and minimization of sum total distance to suppliers, to the prior objective functions while considering the quality and on time delivery constraints. Observance of CSR is lineally related to quality and on-time delivery and will lead to their increase. The model is presented in linear and integer programming in two states, single product and multi product, then it is solved by Multi Objective Decision Making (MODM) methods (Utility Function, STEM and Goal Programming) and answers are obtained and compared.</p

Maximal Benefit location problem for a congested system

Abstract Some servers are to be located at nodes of a network. Demand for services of these servers is located at each node and a subset of the nodes is to be chosen to locate one server in each. Each customer selects a server with a probability dependent on distance and a certain amount of benefit is achieved after giving service to the customer. Customers may waive receiving service with a known probability. The objective is to maximize the total benefit. In this paper, the problem is formulated, three solution algorithms are developed and applied to some numerical examples to analyze the results

Determination of Material Flows in a Multi-echelon Assembly Supply Chain

This study aims to minimize the total cost of a four-echelon supply chain including suppliers, an assembler, distributers, and retailers. The total cost consists of purchasing raw materials from the suppliers by the assembler, assembling the final product, materials transportation from the suppliers to the assembler, product transportation from the assembler to the distributors, product transportation from the distributors to the retailers, and product holding and stock-out in the distribution centers. To this end, having modeled the problem addressed, a numerical example including ten suppliers, an assembler, three distributors and eight retailers in the chain is solved for four periods of time. Then the model is solved by a simulated annealing-based heuristic and LINGO. Finally, a set of 30 numerical problems of small and large sizes are developed and solved. The results indicate that simulated annealing-based heuristic provides near optimal solutions

Hub Covering Location Problem Considering Queuing and Capacity Constraints

In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in placeswhere the entrance flows are more than a predetermined value.A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network.On modeling, a nonlinear mathematical program is presented.Then, the nonlinear constraints are convertedto linear ones.Due to the computational complexity of the problem,genetic algorithm (GA),particle swarm optimization (PSO)based heuristics, and improved hybrid PSO are developedto solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is appliedin order to tune the parameters. Finally,the efficiency ofthe proposed heuristicsis studied while designing a number of test problems with different sizes.The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the proble

An Integrated Production-Inventory Model with Single-Vendor and Multi-Buyer Considering Shortage and Unequal Batch Sizes

This paper investigates a joint economic lot sizing model for a two echelon supply chain consisting of a single-vendor and multiple buyers. Recent studies have focused on the problem with equal batch sizes while this study proposed a mixed integer nonlinear programming model considering unequal batch sizes and shortage to minimize supply chain total cost. A heuristic procedure is proposed to find the solution and finally we show that unequal batch sizes delivers a more cost efficient solution