A clustering based genetic algorithm approach to flexible job shop scheduling problem

Nowadays ۱ in competitive industrial environment, necessity to production in high volume and with high quality causes conditions in which companies have to implement new production methods such as Just in time or lean manufacturing systems. However flexible job shop problem is a modified version of classical job shop problem. It has been proved that this problem is strongly N-P hard. So solving this problem in reasonably computational time by implementing the exact solution methods is almost intractable. So in recent years many heuristic and meta-heuristic approaches have been introduced and developed to solve this problem. Hurink et al (۱۹۹۴) [۱] have presented a tabu search method for this problem. Also Dauzere-Peres and Pauli (۱۹۹۷) [۲]have implemented a tabu search method extending the disjunctive graph representation for the classic job shop problem by consideration the assignment of different operations to available machines. Mastrolilli and Gambardella (۲۰۰۰) [۳] proposed a tabu search procedure with effective neighborhood search method for solving the problem. In recent years many various techniques have been introduced to solve the Flexible job shop problem. For instance, Brandimarte (۱۹۹۳) [۴] has proposed a heuristic approach. In his heuristic, sequencing of operations is determined firstly and then assignment of operations to machines using dispatching rules is done. Also a genetic approach has been developed to solve the problem. Many hybrid genetic algorithms are proposed in last decade, Such as Kacem et al (۲۰۰۲) [۵], Jia et al. (۲۰۰۳) [۶], Ho and Tay (۲۰۰۴) [۷], Pezzella et al(۲۰۰۸) [۸],Gao et al (۲۰۰۸) [۹] which have proposed the hybrid genetic and variable neighborhood descent algorithm for this problem. The purpose of Research proposed by Piroozfard et al [۱۰] is to present a multi-objective flexible job shop scheduling problem with the objectives of minimizing total carbon footprint and total late work criterion, simultaneously, as sustainability-based and classical-based objective functions, respectively.