Application of Fuzzy Optimization to Production-Distribution Planning in Supply Chain Management (original) (raw)
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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.
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.
A Fuzzy Optimization Model for Supply Chain Production Planning with Total Aspect of Decision Making
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This paper models supply chain uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-stage with different methods of manufacturing in each stage, multi-distribution centre and multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to achieve the best use of the available resources and the best method of manufacturing at each stage for a product along the time horizon so that customer demands are met at a minimum cost. The fuzzy model provides the decision maker with alternative decision plans with different degrees of satisfaction.
2010
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.
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.
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.
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.
Nowadays, the role of economics in different fields, such as automobile manufacturing industries, is developing. Fuzzy supply chain management is a new approach which leads to decrease in system costs, decrease in wastes, decrease in losses in transportation and decrease in stock supply. Management of fixed costs such as warehouse construction, and changing costs such as transportation, maintenance, shortage, human force, will lead to car manufacturing waste, etc. structure of queuing model used in this research follows M/G/1 queuing, which has Arrival rate of the Poisson probability distribution function and serving rate of general probability distribution function. In this research, fuzzy supply chain is analyzed in 3 levels. Suppliers are placed on the first level, ware houses and distribution centers are placed on the second level, and local warehouses (consumers) are placed on the third level. The structure of objective function of optimized model which is presented, is based on profit maximization, so that profit resulting from product sales is obtained from difference in total incomes and total costs. Determination of transfer rate from suppliers to middle warehouses (distribution centers), and also determination of transfer rate from middle warehouses to local warehouses, are two good results which can be obtained through model analysis. Model is processed by using Maple 12 software, and at the end suitable results of the model are expressed. This approach result in suitable market management, increase in customer satisfaction, and increase in efficiency of activities in supply chain, product distribution in car manufacturing industries.
2015
Today, the role of the economy in various spheres among food is on the rise. Fuzzy supply chain management is one of the modern attitudes that lead to reduce system costs, food spoilage, losses in shipping products and to reduce inventory levels. Management of fixed costs including the cost of distribution centers construction and variable costs such as transportation costs, maintenance, shortages, manpower, deterioration of food products and others will be concluded. Queue model structure which used in this study obeys from M/G/1 queue that arrival rate Poisson probability distribution function and the service rate have the general probability distribution function. In this study, fuzzy supply chain has been analyzed in three levels. The suppliers are in first level, the distribution centers are in second level and the local warehouse (consumer) is located in third level. The structure of objective function of the presented optimization model is based on the profit maximization whi...
This paper considers a multi-objective integrated production-distribution problem (IPDP) for multi-product and multi-production facility with limited capacity vehicles over a multi-period horizon in a two-level supply chain. In order to consider uncertainty of a real supply chain, some fuzzy parameters are considered for costumer demands, machine and labor levels of each manufacturer. The proposed model minimizes total production, inventory and distribution costs and total delivery time simultaneously, and the performance of the proposed model is evaluated on several randomly generated instances. The results show that integrating production and distribution decisions is more efficient than making these two decisions, separately.