Abstract
This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.
Highlights
The previous review papers exhibit the following limitations: a. They are confined to optimization problems, with bias toward algorithms and objective functions [7,11,12,13]; They rarely accommodate uncertainty issues and their subsidiary factors, such as setup time, buffer size, and transportation time [7,11,14]; They are limited in addressing the importance of product allocations and sequences, owing to dependency on stationary dispatching rules [7,14,15]
Proposed a multi-agent-based hyper-heuristic algorithm to achieve effective machine selection and job sequencing in a multi-objective Flexible job shop scheduling problem (FJSP); this algorithm adopts a metaheuristic for solving an optimization problem, and machine learning to avoid overfitting
This article reviews the state of the art of prediction and optimization for sequencedriven scheduling in job shop flexible manufacturing systems (JS-Flexible manufacturing systems (FMSs))
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. They rarely trace the exact locations of individual products in (near) real time, because they rely on assumptions and approximations, as described above In this regard, prediction is significant for scheduling so that manufacturers can foresee the allocations and sequences of product flows. This article proposes that a sequence-learning-based approach can be leveraged to create data-driven models from learning data to adaptively incorporate the dynamics and uncertainty in JS-FMSs. The remainder of this paper is organized as follows: Section 2 explains the scope and methodology of the literature review; Section 3 presents our macro- and micro-level analyses of the literature; Section 4 discusses the challenges and future directions; and Section 5 summarizes our conclusions
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