This paper presents the idea of creating cells while reducing part-level sequence-dependent setup time in general cellular manufacturing systems (CMS). Setup time reduction in CMS has gained modest attention in the literature. This could be attributed to the fact that the fundamental problem in cell formation in CMS has been mainly related to material handling and machine utilization while setu...

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, neuron units weights are shared at each time step to reduce computational costs, limiting their ability learn time-varying relationships between model inputs outputs. In this context, paper proposes two methods characterize dynamic in real-world...

this paper explores the flexile flow lines where setup times are sequence- dependent. the optimization criterion is the minimization of total weighted completion time. we propose an iterated greedy algorithm (iga) to tackle the problem. an experimental evaluation is conducted to evaluate the proposed algorithm and, then, the obtained results of iga are compared against those of some other exist...

nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. in some of the applications of unrelated parallel mac...

this paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. the objective is to minimize the weighted sum of makespan and maximum tardiness. three meta-heuristic methods based on genetic algorithm (ga), imperialist competitive algorithm (ica) and a hybrid approach of ga a...