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Journal of Intelligent Manufacturing

Erschienen in:

24.09.2014

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

verfasst von: Sasan Khalifehzadeh, Mehdi Seifbarghy, Bahman Naderi

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2017

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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.

Vorheriger Artikel Buffer allocation and preventive maintenance optimization in unreliable production lines

Nächster Artikel A RFID-based storage assignment system for enhancing the efficiency of order picking

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Titel: Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches
verfasst von: Sasan Khalifehzadeh
Mehdi Seifbarghy
Bahman Naderi
Publikationsdatum: 24.09.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 1/2017
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-014-0964-x

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