Dynamic flexible job shop scheduling method based on improved gene expression programming

Abstract

Dynamic scheduling is one of the most important key technologies in production and flexible job shop is widespread. Therefore, this paper considers a dynamic flexible job shop scheduling problem considering setup time and random job arrival. To solve this problem, a dynamic scheduling framework based on the improved gene expression programming algorithm is proposed to construct scheduling rules. In this framework, the variable neighborhood search using four efficient neighborhood structures is combined with gene expression programming algorithm. And, an adaptive method adjusting recombination rate and transposition rate in the evolutionary progress is proposed. The test results on 24 groups of instances with different scales show that the improved gene expression programming performs better than the standard gene expression programming, genetic programming, and scheduling rules.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

“…Equation (5) states that only one operation can be processed by one machine at the same time, and the start setup time of the next operation of the same machine is not less than the completion processing time of the previous operation. Equation (6) states that when a machine processes an operation of a job, the start processing time is equal to the completion setup time of the operation. Equation (7) states that the starting handling time after a process of a job is not less than the completion processing time of the operation.…”

“…Allahverdi et al [5] classified and summarized the workshop scheduling problem with the preparation time by studying the techniques and methods employed in the previous literature that considered preparation time. Zhang et al [6] considered the setup time before processing when studying the dynamic scheduling of flexible job shops, demonstrating that a proposed improved gene expression programming algorithm performs well on 24 groups of instances. Behnke and Geiger [7] proposed a new FJSP specification featuring centralized machines with similar functions within a work center and presented new test cases.…”

Scheduling Algorithms: Challenges Towards Smart Manufacturing

“…On the other hand, re-scheduling or pre-reaction scheduling is a way of scheduling again during the occurrence of new events [26]. It has two stages i.e., the pre-scheduling stage (generating scheduling for actual production) and the re-scheduling stage (re-configure the initial schedule to accommodate dynamic events).…”

“…Investigation of dynamic scheduling is introduced by Jackson in the 1950s [26]. The critical factors in a dynamic manufacturing environment are external disturbances such as failure and production scheduling plan [31].…”

Modeling and Solving for Multi-Satellite Cooperative Task Allocation Problem Based on Genetic Programming Method

“…Sabar [17] discussed the application of GEP to construct a super-heuristic solving framework, which achieved good results for a variety of combinatorial optimization problems. Zhang [18] studied the dynamic shop scheduling problem and proposed a dynamic scheduling framework based on improved GEP to build scheduling rules, which achieved good solving results. Ozturk [19] highlighted the application in the dynamic multi-objective flexible shop scheduling problem and used GEP to extract the composite priority rules of the scheduling problem.…”