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HYBRIDIZATION OF GENETIC ALGORITHM & VARIABLE NEIGHBORHOOD SEARCH (VNGA) APPROACH TO CRACK THE FLEXIBLE JOB SHOP SCHEDULING PROBLEM: A FRAMEWORK

Abstract

It is well recognized that for finding the finest or precised solution of a flexible job shop scheduling problem (FJSSP) which is NP-hard in nature, one needs to connect different aspects of many optimization approaches. This paper proposes differential evolution algorithm (DE) to show hybrid combination of genetic algorithm & variable neighborhood search (VNGA) approach for solving FJSSP and assess the effect of flexibility with an objective to minimize makespan. DE algorithm is a latest population based evolutionary meta-heuristic. Simulation provides a meaningful understanding of real phenomenon of a system nature. A regeneration scheme as suggested in literature is also embedded into the framework. For solving the FJSSP an algorithm and its main representation techniques (GA & VNS) are presented in order to have optimized output.

RAJAN AND VINEET KUMAR

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