A Restart Scheme Based Meta Heuristic for Flexible Job Shop Scheduling

Flexible job shop scheduling problem (F.J.S.S.P) is very important in the fields of production management and it appears under the category of N.P hard combinatorial optimization problem. It is an expansion of traditional job shop scheduling problem (J.S.S.P), though, in many organizations, schedules are mandatory with the existence of various diverse sudden interferences. That's why, it's very complicated to have best possible results within reasonable time. Genetic algorithm system (G.A.S) can reduce combinatorial complexity by task breakdown & real time allotment methods. Genetic algorithm system (G.A.S) and human immune system (H.I.S) are analogous in genetic structure and negotiation strategies. Moreover, structure and negotiation strategies of G.A.S are inspired by H.I.S and are much reliable with the negotiation strategies of H.I.S.. The purpose of this paper is to optimize F.J.S.S.P using G.A.S. In total, a case study has been considered to access the performance of F.J.S.S.P with an objective to reduce make span (Cmax). A restart scheme is entrenched into regular G.A.S for avoiding premature convergence and hence improvement in the fitness value. Randomly selected process plan results into improved shop performance.