Abstract
We aim at solving the cyclic scheduling problem with a single robot and flexible processing times in a robotic flow shop, which is a well-known optimization problem in advanced manufacturing systems. The objective of the problem is to find an optimal robot move sequence such that the throughput rate is maximized. We propose a hybrid algorithm based on the Quantum-Inspired Evolutionary Algorithm (QEA) and genetic operators for solving the problem. The algorithm integrates three different decoding strategies to convert quantum individuals into robot move sequences. The Q-gate is applied to update the states of Q-bits in each individual. Besides, crossover and mutation operators with adaptive probabilities are used to increase the population diversity. A repairing procedure is proposed to deal with infeasible individuals. Comparison results on both benchmark and randomly generated instances demonstrate that the proposed algorithm is more effective in solving the studied problem in terms of solution quality and computational time.
Highlights
Since the 1970s, with the development of the robotics and automation technologies, computer-controlled robots instead of workers have been gradually used in many manufacturing industries to perform high frequency or dangerous transportation jobs
We propose a new scheduling algorithm based on Quantum-Inspired Evolutionary Algorithm (QEA) and genetic operators for the single robot cyclic scheduling problem with flexible processing times in a robotic flow shop
For evaluating the quality of the solution obtained with our HQEA, the problem was formulated by the Mixed Integer Programming (MIP) approach and solved by the ILOG CPLEX (Version 12.4)
Summary
Since the 1970s, with the development of the robotics and automation technologies, computer-controlled robots instead of workers have been gradually used in many manufacturing industries to perform high frequency or dangerous transportation jobs. Lei and Wang [13] and Zhou and Liu [14] proposed various heuristic algorithms based on zone-partition approach for solving different kinds of multiple robots cyclic scheduling problems. Since a higher degree of cyclic schedule would generally improve the production rate, different MIP approaches were suggested for the single robot cyclic scheduling problem with 2 degrees, which means that each machine is emptied exactly 2 times during a cycle [20]. Elmi and Topaloglu [23] developed a metaheuristic algorithm based on ant colony optimization for solving the robotic flow shop scheduling problem with multidegree. We propose a new scheduling algorithm based on QEA and genetic operators for the single robot cyclic scheduling problem with flexible processing times in a robotic flow shop.
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