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

Abstract

Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

“…So it is desirable to further improve the performance of NSGA-II/SDR on MaOPs by developing a new MOEA which can effectively distinguish the non-dominated solutions identified by SDR. In addition, it is also interesting to assess the performance of SDR on real-world applications with many objectives [56], [57] in the future.…”

Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems

“…(Blackstone et al, 1982;Holthaus and Rajendran, 1997)). Recently, automatically designing rules using Genetic Programming Hyper-Heuristics (GPHH) (Burke et al, 2009) has become a dominant approach to automatically evolving the rules, such as evolving the dispatching rules for job shop scheduling (Branke et al, 2016;Nguyen et al, 2017), vehicle routing (Jacobsen-Grocott et al, 2017), and online bin packing (Burke et al, 2006). A commonly used GPHH approach evolves a priority function (as a Lisp tree), which is used to select the next candidate task from the current pool (e.g.…”

A review on the self and dual interactions between machine learning and optimisation

“…Generally, these approaches perform search at a high-level and operate in an offline or online manner. Since there are extensive studies providing comprehensive reviews of the relevant well-established research fields ranging from automated algorithm generation to memetic computing [23,28,29,52,85,122,123,153,165], we briefly cover a few selected studies in this section to point out those different research areas.…”

“…Automated generation of heuristics is of interest in many fields and Genetic Programming is one of the most commonly used tools as an optimisation technique to generate components of optimisation approaches [29]. The studies in [23,123] covered many approaches using Genetic Programming (hyper-heuristics) for solving various scheduling problems. As an example of a selection hyper-heuristic, the [32] employed tabu search for detecting the best permutation of graph colouring heuristics to cooperatively construct near-optimal exam and course timetables.…”