Part of #Multi-objective group hybrid flow shop scheduling problem with time lags and sequence-dependent setup times# :
Publishing year : 2016
Conference : International Conference on Science and Engineering
Number of pages : 28
Abstract: This paper deals with the time lags and sequence-dependent setup times that have wide application in real-world. Most of the researches on operations scheduling problems have ignored time lags. A lot of researches about manufacturing scheduling have neglected group preparing time and time lags and / or considered the machine setup times independent of job sequences. This is a series of scheduling problems, which depends on the sequence and time lags among stations targeting simultaneously minimizing total weighted latency and maximum completion time. This type of manufacturing systems could be found in the metallurgy industry. Increasing the complexity of industrial problems makes traditional scheduling techniques inefficient. Meta-heuristic algorithms, calculations inspired by biology and other soft computing can be used to solve high-complexity problems and produce a reasonable production program at an acceptable time. Genetic algorithm is an intelligent technique for solving 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 born and grow in a condition to maximize their worthiness or minimize social costs. Also, the Artificial Immune System AIS algorithm is a computing system that is inspired by the immune system principles, functions and mechanisms. These two types of meta-heurists are used to solve a complex task-flow scheduling problem with a sequence-dependent set-up of time and time-lags targeting the minimization of total weighted latency and total completion time. The NSGAII is an efficient and effective algorithm to solve complex work shop flow group scheduling problems with sequence dependent setup time and time lags.