Simulated Annealing Algorithm for Job Shop Scheduling on Reliable Real-Time Systems

Zorin, Daniil A.; Костенко, В. А.

doi:10.1007/978-3-319-17509-6_3

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…(2) Combination algorithms and recent studies based on SA Xia and Wu (2005) [57] used SA to avoid being trapped in a local optimum and particle swarm optimization method to enhance high search efficiency and the solutions showed it was a practical method for a multi-objective flexible job shop scheduling problems at a large scale. In more recent study, Zorin et al (2014) [58] applied SA and a multi-layer model to plan scheduling without the exact time of job beginning and ending by estimating the time of execution and proved asymptotic convergence of the algorithm. Shivasankaran et al (2015) [59] proposed a mixed method of SA and immune algorithm to solve sorting limits.…”

Section: Local Search Methodsmentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

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Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems (CPS), Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

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Section: Local Search Methodsmentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

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“…The addition of constraints and characteristics such as the number of plants, types of resources (i.e., renewable and nonrenewable) and type of operations would further classify the JSSP along with increasing the complexity and solution space of the problem. • Split search space into small packets for local and global optimal solutions [62], result in the increment of CPU time and cost [63] Meta Heuristics Method 4…”

Section: Classification Of Scheduling Algorithmmentioning

confidence: 99%

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

et al. 2021

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Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation.

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Simulated Annealing Algorithm for Job Shop Scheduling on Reliable Real-Time Systems

Cited by 2 publications

References 16 publications

Review of job shop scheduling research and its new perspectives under Industry 4.0

Review of job shop scheduling research and its new perspectives under Industry 4.0

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

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