A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

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.

Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

“…Ning et al [38] proposed an improved hybrid multi-phase quantum particle swarm algorithm to solve the dynamic scheduling of flexible job-shop problems. To deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions, Wang et al [39] adopted a modified GA to construct rescheduling strategy. Xiong et al [40] simulated and analyzed dispatching rules for dynamic job shop scheduling.…”

“…To speed up model training, they also proposed a parallel training method which combined asynchronous updates with deep deterministic policy gradient. [34] Job shop scheduling S M Genetic algorithm [35] Dynamic job shop scheduling M M Genetic local search algorithm [36] Flexible manufacturing system S M Fuzzy rule-based system for an adaptive scheduling [37] Job shop scheduling S M Hybrid genetic algorithm [38] Dynamic flexible job shop scheduling M M Improved hybrid multi-phase quantum particle swarm algorithm [39] Dynamic flexible job shop scheduling S M Improved genetic algorithm [40] Dynamic job shop scheduling S M Four new proposed dispatching rules [41] Flexible job shop scheduling S M Game theory [42] Flexible job shop scheduling M M Improved multi-objective genetic algorithm [43] Distributed job shop scheduling S M hybrid ant colony algorithm combined with local search [44] Dynamic job shop scheduling S S Q-III [45,46] Job shop scheduling S S Q-learning to the single machine dispatching rule selection [47] Stochastic lot-scheduling S S Multi-agent RL approach [48] Stochastic production scheduling S M Homogeneous multi-agent system [ [66] Semiconductor production scheduling S M Deep Q-network [67] Job shop scheduling S M Deep Q-network [65] Socio-technical manufacturing system S M Deep Q-Network [68] Job shop scheduling S M Actor-Critic DRL M denotes multi, and S denotes single…”

Review on flexible job shop scheduling

“…An initialisation operation associated with a time matrix is devised to accelerate the convergence speed and the generation gap coefficient is applied to guarantee the survival rate of superior offspring. For solving the dynamic FJSP considering machine failure, urgent job arrival, and job damage as disruptions, Wang et al [107] proposed a dynamic rescheduling method based on variable interval rescheduling strategy. Meanwhile, they proposed an improved GA to solve the dynamic FJSP with the objective of minimising makespan.…”

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

“…(i) Dual-resource constrained FJSSP, for example, [52,162] (ii) Sequence dependent setup times, for example, [88,207] (iii) Distributed and flexible JSSP, for example, [79,99] (iv) Just-In-Time dynamic scheduling, for example, [80,83] (v) Overlapping in operations, for example, [73,98] (vi) Random machine breakdowns, for example, [91,184] (vii) Dynamic FJSSP, for example, [94,96].…”