Abstract
This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs' transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on Teaching Learning Based Optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations' assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.
Highlights
Intelligent production equipment has been gradually applied in recent manufacturing industry
This study proposes a new hybrid multi-objective metaheuristic which combines the Teaching Learning Based Optimization (TLBO) algorithm with a powerful local search technique, the experimental results show that our algorithm obtains better non-dominated solutions than others
EXPERIMENTAL SETUP To evaluate the performance of the proposed improved multi-objective teacher learning-based optimization algorithm (IMOTLBO) algorithm, experimental evaluation and comparison with other methods were performed
Summary
Intelligent production equipment has been gradually applied in recent manufacturing industry. Moslehi and Mahnam [18] present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem.
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