Meta-heuristic Algorithms for an Integrated Production-Distribution Planning Problem in a Multi-Objective Supply Chain

سال انتشار: 1392
محل انتشار: دوفصلنامه بهینه سازی در مهندسی صنایع، دوره: 6، شماره: 12
کد COI اختصاصی: JR_JOIE-6-12_006
زبان مقاله: انگلیسی
تعداد مشاهده: 527

نویسندگان

Assistant Professor, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Fatemeh Kangi

MSc, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Maghsoud Amiri

Associate Professor, Department of Industrial Management, Allameh Tabatabaei University, Tehran, Iran

چکیده

In today’s global marketplace, an effective integration of production and distribution plans into a unified framework is crucial for attainingcompetitive advantages. This paper, therefore, addresses an integrated multi-product and multi-time period production-distributionplanning problem for a two-echelon supply chain subject to the real-world constraints. It is assumed that all transportations are outsourcedto third-party logistics providers and all-unit quantity discounts on transportation costs are taken into consideration. The problem isformulated as a multi-objective mixed-integer linear programming model which attempts to simultaneously minimize the total deliverytime and total transportation costs. Due to the complexity of the considered problem, the genetic algorithm (GA) and particle swarmoptimization (PSO) algorithm are developed within the LP-metric method and desirability function framework for solving the real-sizedproblems in a reasonable computational time. As the performance of meta-heuristic algorithms is significantly influenced by the calibrationof their parameters, Taguchi methodology is used to tune the parameters of the developed algorithms. Finally, the efficiency andapplicability of the proposed model and solution methodologies are demonstrated through several problems of different sizes.

کلیدواژه ها

Supply chain; Production-distribution planning; Multi-objective optimization; Meta-heuristic algorithms; Transportation costdiscount

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