Zahra Habibi Ahzoon - Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj,Iran

This paper deals with hybrid flow shop scheduling problems considering time lags and sequence-dependent setup times which have wide application in real-world. Most of the researches on operations scheduling problems have ignored time lags. A mathematical model is presented which is capable of solving the small size of the considered problem in a reasonable time.problems.Lots of researches about manufacturing scheduling have neglected group preparing time and time lags and or have considered machine setup times independent of job sequence. This article is about complex job shop flow group scheduling problems with preparing time which depends on sequence and time lags among stations targeting simultaneously minimization of total weighted latency and maximum completion time. This type of manufacturing systems could be found in metallurgy industry. Increasing complexity of industrial problems make traditional scheduling techniques inefficient. Meta-heuristic algorithms, calculation inspired from biology and other soft computing can be used to solve high complexity problems and produce a reasonable manufacturing program at an acceptable time. Genetic algorithm is an intelligently technique to solve scheduling problems. This algorithm is based on genetic science and natural selection to optimize and search among choices to choose the best answer. In such problems we have a group of people to born and grow in a condition to maximize their worthiness or minimize a social related cost. Also Artificial Immune System AIS algorithm is a computing system which is inspired by immune system principles, functionalities and mechanisms. This two type of meta-heurists are used to resolve complex job shop flow scheduling problems with sequence dependent setup times and time lags targeting minimization of total weighted latency and total completion time. Also in this study performance of two algorithms were evaluated in different sizes and results shows that the NSGAII is an efficient and effective algorithm to solve complex job shop flow group scheduling problems with sequence dependent setup time and time lags.

Scheduling Problems, Group Setup Times, Total Latency, Maximum Completion Time