A New Trade-Off Model for Fuzzy Supply Chain Network Design and Optimization (original) (raw)
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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.
Design of supply chain in fuzzy environment
Journal of Industrial Engineering International, 2013
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An efficient integration of production and distribution plans into a unified framework is critical to achieving competitive advantage. This paper addresses the production and distribution planning problem in a supply chain system that involves the allocation of production volumes among the different production lines in the manufacturing plants, and the delivery of the products to the distribution centers. An integrated optimization model for production and distribution planning is proposed, with the aimed of optimally coordinating important and interrelated logistics decisions. However, a real supply chain operates in a highly dynamic and uncertain environment. Therefore, this model is transformed into fuzzy models taking into account the fuzziness in the capacity constraints, and the aspiration level of costs using different aggregation operators. The applicability and flexibility of the proposed models are illustrated through a case study in consumer goods industry.
The International Journal of Advanced Manufacturing Technology, 2014
Supply chain (SC) network design problems are complex problems with multi-layer levels and dynamic relationships which involve a considerable amount of uncertainty concerning customer demand, facility capacity, or lead times, among others. A large number of optimization methods (i.e., fuzzy mathematical programming, stochastic programming, and interval mathematical programming) have been proposed to cope with the uncertainties in SC network design problems. We propose a fuzzy bi-objective mixed-integer linear programming (MILP) model to enhance the material flow in dual-channel, multi-item, and multi-objective SCs with multiple echelons under both ambiguous and vague conditions, concurrently. We use a computationally efficient ranking method to resolve the ambiguity of the parameters and propose two methods for resolving the vagueness of the objective functions in the proposed fuzzy MILP model. The preferences of the decision makers (DMs) on the priority of the fuzzy goals are represented with crisp importance weights in the first method and fuzzy preference relations in the second method. The fuzzy preference relations in the second method present a unique practical application of type-II fuzzy sets. The performance of the two methods is compared using comprehensive statistical analysis. The results show the perspicuous dominance of the method which uses fuzzy preference relations (i.e., type-II fuzzy sets). We present a case study in the food industry to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms. To the best of our knowledge, a concurrent interpretation of both ambiguous and vague uncertainties, which is applicable to many real-life problems, is novel and has not been reported in the literature.
Application of Fuzzy Optimization to Production-Distribution Planning in Supply Chain Management
Mathematical Problems in Engineering, 2014
A production-distribution model has been developed that not only allocates the limited available resources and equipment to produce the products over the time periods, but also determines the economical distributors for dispatching the products to the distribution centers or retailers. The model minimizes production, inventory holding, backordering, and transportation cost while considering the time value of money. Since uncertainty is an inevitable issue of any real-world production system, then to provide a realistic model, the concept of fuzzy sets has been applied in the proposed mathematical modeling. To illustrate and show the feasibility and validity of the model, a real case analysis, which is pertaining to a mineral water bottling production factory, has been used. The case has been solved using a three-step solution approach developed in this study. The results show the feasibility and validity of the mathematical model, and also the solution procedure. © 2014 S. Ariafar et al. http://www.hindawi.com/journals/mpe/2014/218132/abs/
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.
2011 IEEE International Conference on Industrial Engineering and Engineering Management, 2011
This paper presented an application of Fuzzy mathematical programming model to solve network design problems for supply chains via considering aggregate production planning (APP). APP goals to minimize all costs through optimal levels of production, subcontracting, inventory, backorder and work levels over a time period to meet the demand. Fuzzy logic was applied to solve the uncertain production/distribution/subcontracting costs and capacities. However, most of the existing models deal the APP problems without integrating supply chain networks. In our model, APP and supply chain design problem were considered within a single plan horizon to get better managerial results. A supply chain network which includes suppliers, manufacturers, subcontracts, retailers and customers, was developed to illustrate the performance of the proposed model. A numerical example was presented to clarify the features proposed approach. In applying the model, decision makers should find a potential to represent their human resources policies regarding the overtime and subcontract production under material requirements constraints.
Supply Chain Network Design under Uncertainty
This paper proposes a fuzzy programming model and a hybrid intelligent algorithm to design supply chain network. Existing researches on these problems are either restricted on deterministic environment or only address stochastic parameters. In this paper, we consider supply chain network design problem in fuzzy environment. In practise, there is generally a predetermined cost which decision-maker can accept, the objective of this paper is to maximize the degree of credibility of satisfying the event that the total cost is less than that given cost. Moreover, a genetic algorithm based on fuzzy simulation is developed to solve the proposed fuzzy models.
Supply chain modelling using fuzzy sets
International Journal of Production Economics, 1999
This paper considers a production supply chain (SC) with all facilities in a serial connection. The SC includes inventories and production facilities between them. It is assumed that the SC operates in an uncertain environment. Uncertainty is associated with: (1) customer demand, (2) supply deliveries along the SC and (3) external or market supply. Uncertainties are described by vague and imprecise phrases that are interpreted and represented by fuzzy sets. The SC fuzzy model described in this paper is developed to determine the order quantities for each inventory in the SC in the presence of uncertainties, that give an acceptable service level of the SC at reasonable total cost. Two control concepts of the SC are treated: (1) decentralised control of each inventory and (2) partial coordination in the inventories control. A special purpose simulator has been developed for examining the dynamics and performance of all the parts of the SC and the SC as a whole. Various simulation tests have been carried out to assess particularly the effects of uncertain external supply on the SC service level. Different approaches to improve SC performance in an uncertain environment have been simulated and analysed.
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.