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Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches

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Abstract

This paper studies a multi-objective production–distribution system. The objectives are to minimize total costs and maximize the reliability of transportations system. Each transportation system is assumed to be of unique reliability. In the real world, some parameters may be of vagueness; therefore, some tools such as fuzzy logic is applied to tackle with. The problem is formulated using a mixed integer programming model. Commercial software can optimally solve small sized instances. We propose two novel HEURISTICS called ranking genetic algorithm (RGA) and concessive variable neighborhood search (CVNS) in order to solve the large sized instances. RGA utilizes various crossover operators and compares their performances so that better crossover operators are used during the solution process. CVNS applies several neighborhood search structures with a novel learning procedure. The heuristics can recognize which neighborhood structure performs well and applies those more than the others. The results indicated that RGA is of higher performance.

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Authors and Affiliations

  1. Young Researchers Club, Karaj Branch, Islamic Azad University, Karaj, Iran

    Sasan Khalifehzadeh

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

    Mehdi Seifbarghy

  3. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Karaj, Iran

    Bahman Naderi

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  1. Sasan Khalifehzadeh
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  2. Mehdi Seifbarghy
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  3. Bahman Naderi
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Correspondence to Mehdi Seifbarghy.

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Khalifehzadeh, S., Seifbarghy, M. & Naderi, B. Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches. J Intell Manuf 28, 95–109 (2017). https://doi.org/10.1007/s10845-014-0964-x

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