Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

Abstract

A scheduling policy strongly influences the performance of a manufacturing system. However, the design of an effective scheduling policy is complicated and time-consuming due to the complexity of each scheduling decision as well as the interactions among these decisions. This paper develops four new multi-objective genetic programming based hyper-heuristic (MO-GPHH) methods for automatic design of scheduling policies including dispatching rules and due-date assignment rules in job shop environments. Besides using three existing search strategies NSGA-II, SPEA2 and HaD-MOEA to develop new MO-GPHH methods, a new approach called Diversified Multi-Objective Cooperative Coevolution (DMOCC) is also proposed. The novelty of these MO-GPHH methods is that they are able to handle multiple scheduling decisions simultaneously. The experimental results show that the evolved Pareto fronts represent effective scheduling policies that can dominate scheduling policies from combinations of existing dispatching rules with dynamic/regression-based duedate assignment rules. The evolved scheduling policies also show dominating performance on unseen simulation scenarios with different shop settings. In addition, the uniformity of the scheduling policies obtained from the proposed method of DMOCC is better than those evolved by other evolutionary approaches. Index Terms-Genetic Programming, job shop scheduling, hyper-heuristic, dispatching rule. NOMENCLATURE JSS job shop scheduling DJSS dynamic job shop scheduling DR dispatching rule CDR composite dispatching rule DDAR due-date assignment rule SP scheduling policy GP genetic programming GPHH genetic programming based hyper-heuristic MO-GPHH multi-objective GPHH SPEA2 strength Pareto evolutionary algorithm 2 NSGA-II non-dominated sorting genetic algorithm II HaD-MOEA harmonic distance based multi-objective evolutionary algorithm DMOCC diversified multi-objective cooperative coevolution

Genetic programming for production scheduling: a survey with a unified framework

“…These recent studies have focused on improving the effectiveness and efficiency of GP for production scheduling by developing new representations [89], new surrogate-assisted models [45], local search heuristics [97], and ensemble methods [42,113]. Practical issues such as multiple conflicting objectives [35,90], multiple decisions [95,104], attribute selection [79] are catching more attentions. Moreover, researchers have been interested in reusability of evolved dispatching rules as well as their interpretability [46,95].…”

“…Practical issues such as multiple conflicting objectives [35,90], multiple decisions [95,104], attribute selection [79] are catching more attentions. Moreover, researchers have been interested in reusability of evolved dispatching rules as well as their interpretability [46,95]. Table 1 shows a list of major papers about automatic design of production scheduling heuristics via GP and their focuses.…”

“…The studies of applying evolved [88] improvement heuristics to dynamic environments will be an interesting research topic in future studies. When dealing with different planning and scheduling decisions, different meta-algorithms can also be used [95,103].…”

“…For example, Nie et al [103,104] considered both dispatching rules and routing rules simultaneously when designing scheduling heuristics for flexible job shop scheduling. Nguyen et al [95] developed a GP technique to deal with both sequencing decisions and due date assignment decisions. Yin et al [142] aimed at designing predictive scheduling heuristics using GP to evolve two components, i.e.…”

“…Multiple tree representations are also useful for representing different scheduling decisions such as acceptance/rejection [111], due date assignment [95] and maintenance [142]. Figure 9 shows the representation of scheduling policies developed by Nguyen et al [95].…”