A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling

Abstract

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyper-heuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

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

“…Well-known benchmark instances in the scheduling literature [4,29,71,132] are commonly used for evaluation purposes. Some random instance generators [42,59,135] are also applied to generate training and test instances for GP. However, these are mainly used for static production scheduling problems.…”

“…Since 2010, there have been a dramatic growth in the number of studies on this topic. 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.…”

“…GP has been applied in a wide range of production scheduling problems, ranging from single machine scheduling [30,38,59,100,142], parallel machine scheduling [31,60], to (flexible) job shop scheduling [42,53,59,63,79,81,88,89,95,102, Chen et al [26] Durasevic et al [31] Freitag and Hildebrandt [35] Hart and Sim [42] Karunakaran et al [63] Park et al [115] Park et al [116] Riley et al [123] Branke et al [19] Mei and Zhang [78] Karunakaran et al [64] Masood et al [75] Mei et al [79] Nguyen [84] Nguyen et al [99] Production Scheduling Problems 104,135,136]. Most machines considered in these problems are the same in terms of capability (eligibility to handle a job) and assumptions (e.g.…”

“…Other meta-algorithms have also been investigated such as beam search heuristics [93,111], ensembles of heuristics [42], adaptive scheduling heuristics based on bottleneck machines [59], and NEH heuristics [136]. These evolved heuristics are very different in terms of computational costs and how they build schedules.…”

“…Hart and Sim [42] developed a hyper-heuristic based on an ensemble method called NELLI [131]. The proposed NELLI-GP extends NELLI by evolving a set of dispatching rules represented as an expression tree.…”