A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem
Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear progra...
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| Veröffentlicht in: | Central European journal of operations research 2011-12, Vol.19 (4), p.547-569 |
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| creator | Jolai, F. Razmi, J. Rostami, N. K. M. |
| description | Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear programming problem for integrating production–distribution, which considers various simultaneously conflicting objectives, is developed. The decision maker’s imprecise aspiration levels of goals are incorporated into the model using a fuzzy goal programming approach. Due to complexity of the considered problem we propose three meta-heuristics to tackle the problem. A simple genetic algorithm and a particle swarm optimization (PSO) algorithm with a new fitness function, and an improved hybrid genetic algorithm are developed. In order to show the efficiency of the proposed methods, two classes of problems are considered and their instances are solved using all methods. The obtained results show that the improved hybrid genetic algorithm gives us the best solutions in a reasonable computational time. |
| doi_str_mv | 10.1007/s10100-010-0144-9 |
| format | Article |
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K. M.</creatorcontrib><title>A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem</title><title>Central European journal of operations research</title><addtitle>Cent Eur J Oper Res</addtitle><description>Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear programming problem for integrating production–distribution, which considers various simultaneously conflicting objectives, is developed. The decision maker’s imprecise aspiration levels of goals are incorporated into the model using a fuzzy goal programming approach. Due to complexity of the considered problem we propose three meta-heuristics to tackle the problem. A simple genetic algorithm and a particle swarm optimization (PSO) algorithm with a new fitness function, and an improved hybrid genetic algorithm are developed. In order to show the efficiency of the proposed methods, two classes of problems are considered and their instances are solved using all methods. 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K. M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem</atitle><jtitle>Central European journal of operations research</jtitle><stitle>Cent Eur J Oper Res</stitle><date>2011-12-01</date><risdate>2011</risdate><volume>19</volume><issue>4</issue><spage>547</spage><epage>569</epage><pages>547-569</pages><issn>1435-246X</issn><eissn>1613-9178</eissn><abstract>Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear programming problem for integrating production–distribution, which considers various simultaneously conflicting objectives, is developed. The decision maker’s imprecise aspiration levels of goals are incorporated into the model using a fuzzy goal programming approach. Due to complexity of the considered problem we propose three meta-heuristics to tackle the problem. A simple genetic algorithm and a particle swarm optimization (PSO) algorithm with a new fitness function, and an improved hybrid genetic algorithm are developed. In order to show the efficiency of the proposed methods, two classes of problems are considered and their instances are solved using all methods. The obtained results show that the improved hybrid genetic algorithm gives us the best solutions in a reasonable computational time.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s10100-010-0144-9</doi><tpages>23</tpages></addata></record> |
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| ispartof | Central European journal of operations research, 2011-12, Vol.19 (4), p.547-569 |
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| source | SpringerLink Journals; Business Source Complete |
| subjects | Algorithms Analysis Business and Management Collaboration Decision making Design Fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets Genetic algorithms Goal programming Heuristic Heuristic methods Integer programming Inventory Linear programming Literature reviews Logistics Mathematical models Mathematical optimization Mathematical programming Objectives Operations research Operations Research/Decision Theory Optimization Original Paper Planning Production capacity Production planning Retail stores Suppliers Supply chain management Supply chains Systems design |
| title | A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem |
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