A cooperative water wave optimization algorithm with reinforcement learning for the distributed assembly no-idle flowshop scheduling problem

Pan

Complex Syst. Model. Simul.

2021

138

As the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RLbased algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.

Section: Rl For Flow Shop Schedulingmentioning

confidence: 99%

A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

Pan

Complex Syst. Model. Simul.

2021

138

Complex Syst. Model. Simul.

“…Li et al [28] proposed a DABC algorithm to solve the distributed heterogeneous no-wait flow shop scheduling problem. Next, considering the distributed heterogeneous hybrid flow shop scheduling problem with unrelated parallel machines and the SDST, Li et al [29] studied a machine position-based mathematical model and designed an improved artificial bee colony algorithm. For the distributed assembly flow shop scheduling problem, Zhao et al [30] proposed a cooperative water wave optimization algorithm to minimize the maximum assembly completion time.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Han

Chen

et al. 2021

To meet the multi-cooperation production demand of enterprises, the distributed permutation flow shop scheduling problem (DPFSP) has become the frontier research in the field of manufacturing systems. In this paper, we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequencedependent setup times. To solve DPFSPs, significant developments of some metaheuristic algorithms are necessary. In this context, a simple and effective improved iterated greedy (NIG) algorithm is proposed to minimize makespan in DPFSPs. According to the features of DPFSPs, a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm. We compare the proposed algorithm with state-of-the-art algorithms, including the iterative greedy algorithm (2019), iterative greedy proposed by Ruiz and Pan (2019), discrete differential evolution algorithm (2018), discrete artificial bee colony (2018), and artificial chemical reaction optimization (2017). Simulation results show that NIG outperforms the compared algorithms.

“…Production shop-related constraints, including no wait, no idle, blocking, limited buffer, and lot streaming, are addressed in Refs. [40,47,[52][53][54][55][56][57][58][59][60][61][62][63]. In distributed production scheduling, a one-or two-stage assembly line as a special phase in flow shops and job shops have been researched in many publications [42,45,51,[64][65][66][67][68][69][70][71][72] .…”

Section: Problemmentioning

confidence: 99%

Distributed scheduling problems in intelligent manufacturing systems

Hou

et al. 2021

Tsinghua Sci. Technol.

122

Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations.Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems.This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.