A fuzzy bi-objective mixed-integer programming method for solving supply chain network design problems under ambiguous and vague conditions (original) (raw)
Related papers
Pomorstvo, 2021
In this paper, we devoted a design under uncertainty of a four-echelon supply chain network including multiple suppliers, multiple plants, multiple distributors and multiple customers. The proposed model is a bi-objective mixed integer linear programming which considers several constraints and aims to minimize the total costs including the procurement, production, storage and distribution costs as well as to maximize on-time deliveries (OTD). To bring the model closer to real-world planning problems, the objective function coefficients (e.g. procurement cost, production cost, inventory holding and transport costs) and other parameters (e.g., demand, production capacity and safety stock level), are all considered triangular fuzzy numbers. Besides, a hybrid mathematical model-based on credibility approach is constructed for the problem, i.e., expected value and chance constrained models. Moreover, to build the crisp equivalent model, we use different property of the credibility measure. The resulted crisp equivalent model is a bi-objective mixed integer linear programs (BOMILP). To transform this crisp BOMILP into a single objective mixed integer linear programs (MILP) model, we apply three different aggregation functions. Finally, numerical results are reported for a real case study to demonstrate the efficiency and applicability of the proposed model.
international journal of industrial engineering computations, 2012
In this paper, we study a supply chain problem where a whole seller/producer distributes goods among different retailers. Such problems are always faces with uncertainty with input data and we have to use various techniques to handle the uncertainty. The proposed model of this paper considers different input parameters such as demand, capacity and cost in trapezoid fuzzy forms and using two ranking methods, we handle the uncertainty. The results of the proposed model of this paper have been compared with the crisp and other existing fuzzy techniques using some randomly generated data. The preliminary results indicate that the proposed models of this paper provides better values for the objective function and do not increase the complexity of the resulted problem.
Journal of Manufacturing Systems, 2013
Nowadays, supply chains play an inevitable role in prompt handling of varying customers' needs. Administration of a successful supply chain depends on how efficiently the network design is accomplished. Therefore, a supply chain network design problem is considered in this paper. The network addresses an uncertain environment threatened by different risk sources in order to captivate the real world conditions. A mixed-integer non-linear mathematical model is developed in which the uncertainties are represented by the fuzzy set theory. Benders decomposition is then applied to solve the proposed problem; consequently, the model is transformed into a mixed-integer one. Moreover, an interactive resolution method is applied to provide the decision maker with alternative decision plans in regard to different satisfaction degrees. Finally, the accuracy of the proposed model is checked by sensitivity analysis test and its performance is considered by different numerical examples. (J. Razmi). affects the entire Supply Chain Network (SCN) configuration (e.g. the numbers, capacities, and locations) are considered by the strategic level and the issues corresponding to deciding on whatever affects the aggregate quantities (e.g. material handling, processing, and distribution) are considered by the tactical level . Therefore, designing an efficient SCN can guarantee the success of the whole chain as many underlying issues are involved in the given problem.
A hybrid fuzzy approach for the closed-loop supply chain network design under uncertainty
Journal of Intelligent & Fuzzy Systems, 2015
A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper a CLSC network is investigated that involves four echelons in a forward direction including suppliers, manufacturer, distribution center and demand market, and three echelons in a backward direction including disposal, rework and collection centers. This paper presents a bi-objective model in order to design a network of bi-directional facilities in logistics network under uncertainties. Its objectives are to minimize the total costs as well as the total defective rate, disposal rate and pollution production rate. To solve the model, a hybrid solution approach is applied that combines fuzzy possibilistic programming and fuzzy multi-objective programming. Furthermore, in order to illustrate the validity of the model and applicability of the proposed solution approach, numerical experiments and the related sensitivity analysis are provided. Finally, the conclusion is provided.
A New Trade-Off Model for Fuzzy Supply Chain Network Design and Optimization
2012
This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
Multi-Criteria Supply Chain Network Design Under Uncertainty
2012
This thesis contributes to the debate on how uncertainty and concepts of sustainable development can be put into modern supply chain network and focuses on issues associated with the design of multi-criteria supply chain network under uncertainty. First, we study the literature review , which is a review of the current state of the art of Supply Chain Network Design approaches and resolution methods. Second, we propose a new methodology for multi-criteria Supply Chain Network Design (SCND) as well as its application to real Supply Chain Network (SCN), in order to satisfy the customers demand and respect the environmental, social, legislative, and economical requirements. The methodology consists of two different steps. In the first step, we use Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) to buildthe model. Then, in the second step, we establish the optimal supply chain network using Mixed Integer Linear Programming model (MILP). Third, we extend the MILP...
2021
Supply Chain is a multi-objective decision-making problem with multiple conflicting objective functions related to each supply chain operation and its corresponding sub-criteria. The main focus of this paper is the development of a model that takes into account some important components of real-world supply chain planning. To do so, we proposed a supply chain model that involves multiple suppliers, multiple plants, multiple warehouses, and multiple distributors firms. This approach is designed to tackle a complex multi-site composite supply chain issue under uncertainty as a fuzzy multi-objective model with the primary objective to optimize the transportation cost and delivery time simultaneously. We have used neutrosophical set theory to tackle the ambiguity related to supply chain by using truth, indeterminacy and falsity membership functions and, finally neutrosophical compromise programming approach has been used for obtaining the desired solution. In order to demonstrate the ef...
Applied Mathematical Modelling
This study applies fuzzy sets to integrate the supply chain network of an edible vegetable oils manufacturer. The proposed fuzzy multi-objective linear programming model attempts to simultaneously minimize the total transportation costs. The first part of the total transportation costs is between suppliers and silos; and rest one is between manufacturer and warehouses. The approach incorporates all operating realities and actual flow patterns at production/distribution network with reference to demands of warehouses, capacities of tin and pet packaging lines. The model has been formulated as a multi objective linear programming model where data are modeled by triangular fuzzy numbers. Finally, the developed fuzzy model is applied for the case study, compiled the results and discussed.
Applied Soft Computing, 2013
Supply chain design problems have recently raised a lot of interest since the opportunity of an integrated management of the supply chain can reduce the propagation of undesirable events through the network and can affect decisively the profitability of the members. Often uncertainties may be associated with demand and relevant costs. In most of the existing models uncertainties are treated as randomness and are handled by appealing to probability theory. Here, we propose a fuzzy mathematical programming model for a supply chain which considers multiple depots, multiple vehicles, multiple products, multiple customers, and different time periods. In this work not only demand and cost but also decision variables are considered to be fuzzy. We apply two ranking functions for solving the model. The aim of the fuzzy mathematical program is to select the appropriate depots among candidate depots, the allocation of orders to depots and vehicles, also the allocation of the returning vehicles to depots, to minimize the total costs. To validate the model some numerical experiments are worked out and a comparative analysis is investigated. Also, a regression model is considered to analyze the applied fuzzy ranking methods.
This work applies fuzzy sets to integrating the distribution problem of a multi product, multi tiered closed loop flexible supply chain network (involves suppliers, factories, warehouses, distribution centers, retailers, end customers and collection, recovery, recycling centers) under fuzzy material requirement constraints. The proposed fuzzy multi-objective mixed integer linear programming model attempts to simultaneously minimize total transportation costs between all echelons and total fixed costs of manufacturers and distribution centers. The model has been formulated as a mixed-integer linear programming model where data are modeled by triangular fuzzy numbers. Finally, a numerical example is solved by a professional package program, compiled the results and discussed.