Learning effective dispatching rules for batch processor scheduling

Abstract

Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.

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

“…utilisation level). Although some special cases are considered in the literature such as batching [37,118], machine breakdowns [142], and unrelated parallel machines [31], these are very limited. In addition, most scheduling problems handled by GP are dynamic problems where jobs will arrive randomly over time and their information is only available upon their arrivals.…”

“…Yin et al [142] Ho and Tay [50] Geiger et al [38] Jakobovic and Budin [59] Jakobovic et al [60] Tay and Ho [134] Beham et al [14] Geiger and Uzsoy [37] Baykasoglu [9] Li et al [73] Tay and Ho [135] Yang et al [141] Mucientes et al [83] Baykasolu and Gken [12] Kofler et al [66] Furuholmen et al [36] Hildebrandt et al [46] Kuczapski et al…”

Evolving control rules for a dual-constrained job scheduling scenario

“…Geiger, Uzsoy, and Aytuǧ (2006) compare a GP hyper-heuristic approach to wellestablished dispatching rules for a variety of single machine problems. In another paper Geiger and Uzsoy (2008), also apply a GP hyper-heuristic to a single machine batch processor model, whereby the scheduling procedure must perform two steps, both grouping the waiting jobs into batches and ordering the batches for processing. Yin, Liu, and Wu (2003), also apply GP to a single machine scheduling problem, but include stochastic machine breakdowns.…”

Automated Design of Production Scheduling Heuristics: A Review

“…Parametric representation [21], [38], [42], [46] [35], [36], [41], [45], [47], [52], [57], [58], [59], [67] Grammar-based representation [24], [30], [39] [22], [23], [25], [26], [27], [28], [29], [31], [32], [33], [34], [37], [40], [43], [44], [48], [49], [50], [51], [52], [53], [54], [55], [56], [60], [61], [62], [63], [64], [65], [66] The next section (III-A) discusses the two types of learning methods used within hyper-heuristics, followed by a discussion of the selection of attributes to be provided to the hyperheuristic in Section III-B. The different representations of priority functions are presented in Section III-C together with suitable optimisation algorithms as they are closely tied to the chosen representation.…”