Abstract
In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.
Highlights
The flexible job shop scheduling problem (FJSP) is an extension of the classical job shop scheduling problem (JSP) [1].FJSP can be divided into two subproblems [2]: One is to determine operation sequences, and the other one is to select corresponding machine for these operations
For partial flexible job shop scheduling problem (P-FJSP), when the machine number is greater than the total number of the available machines by this crossover operation, one machine is randomly selected for processing the corresponding operation
If the machine number obtained by this method is greater than the total number of the available machines by this crossover operation, a machine is selected in the machine set to process the corresponding operation in a random manner
Summary
In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. We propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. An improved genetic algorithm (GA) is proposed for minimizing makespan.
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