Abstract
Under the current volatile business environment, the requirement of flexible production is becoming increasingly urgent. As an innovative production mode, seru-system with reconfigurability can overcome the lack of flexibility in traditional flow lines. Compared with pure seru-system, the hybrid seru-system composed of both serus and production lines is more practical for adapting to many production processes. This paper addresses a specific hybrid seru-system scheduling optimization problem (HSSOP), which includes three strongly coupled sub-problems, i.e., hybrid seru formation, seru scheduling and flow line scheduling. To minimize the makespan of the whole hybrid seru-system, we propose an efficient cooperative coevolution algorithm (CCA). To tackle three sub-problems, specific sub-algorithms are designed based on the characteristic of each sub-problem, i.e., a sub-space exploitation algorithm for hybrid seru formation, an estimation of distribution algorithm for seru scheduling, and a first-arrive-first-process heuristic for flow line scheduling. Since three sub-problems are coupled, a cooperation coevolution mechanism is proposed for the integrated algorithm by information sharing. Moreover, a batch reassign rule is designed to overcome the mismatch of partial solutions during cooperative coevolution. To enhance the exploitation ability, problem-specific local search methods are designed and embedded in the CCA. In addition to the investigation about the effect of parameter setting, extensive computational tests and comparisons are carried out which demonstrate the effectiveness and efficiency of the CCA in solving the HSSOP.
Highlights
Under the background of Industry 4.0, demand volatility is a growing reality in current market due to the increasing demands of consumers as well as the rapid development of manufacturing technologies
To decode a batch priority string into a partial solutionSS, an earliest completion flow line (ECFL) rule shown in Algorithm 2 is designed to assign batches to seru according to the batch priority string
The problem is decomposed in three sub-problems: hybrid seru formation, seru scheduling and flow line scheduling
Summary
Under the background of Industry 4.0, demand volatility is a growing reality in current market due to the increasing demands of consumers as well as the rapid development of manufacturing technologies. A sub-space exploitation algorithm (SSEA) is developed to solve the hybrid seru formation sub-problem by exploiting the solution space with different number of serus; an estimation of distribution algorithm (EDA) is presented to solve the seru scheduling sub-problem by learning the distribution of high-quality batching processing sequences; and a first-arrive-first-process (FAFP) heuristic is designed to efficiently and effectively generate a flow line scheduling. The HSSOP can be divided into three sub-problems: hybrid seru formation (including worker allocation and seru formulation), seru scheduling and flow line scheduling. To decode a batch priority string into a partial solutionSS , an earliest completion flow line (ECFL) rule shown in Algorithm 2 is designed to assign batches to seru according to the batch priority string. Time incremental learning which is based on the historical information of the better individuals
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