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

Flexible job shop scheduling problems &#x0028 FJSP &#x0029 have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence &#x0028 SI &#x0029 and evolutionary algorithms &#x0028 EA &#x0029 are developed, employed and improved for solving them. More than 60&#x0025 of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm &#x0028 GA &#x0029 and one newest imperialist competitive algorithm &#x0028 ICA &#x0029 with variables neighborhood search &#x0028 VNS &#x0029 for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems

Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.

Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

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Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid to tackle this problem. In this study, the existing solution methods for the FJSP in recent literature are classified into exact algorithms, heuristics and meta-heuristics, which are reviewed comprehensively. Moreover, the real-world applications of the FJSP are also introduced. Finally, the development trends of the manufacturing industry are analysed, and the future research opportunities of the FJSP are summarised in detail.

Review on flexible job shop scheduling

In view of the difficulty of obtaining the optimal solution to the multi-objective scheduling of flexible job-shop by the general genetic algorithm, this paper takes into account the shortest processing time and the balanced use of machines, and puts forward the multi-population genetic algorithm based on the multi-objective scheduling of flexible job-shop. The method attempts to minimize the longest make-span of workpieces, the load of each machine, and the total machine load through the overall process scheduling of the job-shop. Research results reveal that the proposed method is highly efficient in seeking the optimal machine allocation chain, and effective in avoiding the complex process of intermediate assignment, making it easier to obtain the said optimal solution. The feasibility and effectiveness of the proposed method are also validated by two instances. Compared with the conventional flexible job-shop scheduling algorithms, the proposed algorithm boasts better population quality, algorithm starting point, and initial expression. Besides, it is far better than other algorithms in terms of the initial solution quality and the convergence rate. Despite the local fluctuations in the early phase of the genetic process, the total machine load and the machine load variance are gradually declining and the curves start to converge after the 50 generation. (Received, processed and accepted by the Chinese Representative Office.)

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Multi-Objective Scheduling Simulation of Flexible Job-Shop Based on Multi-Population Genetic Algorithm

Explaining the mechanism of superconductivity in the high- T c cuprates requires an understanding of what causes electrons to form Cooper pairs. Pairing can be mediated by phonons, the screened Coulomb force, spin or charge fluctuations, excitons, or by a combination of these. An excitonic pairing mechanism has been postulated, but experimental evidence for coupling between conduction electrons and excitons in the cuprates is sporadic. Here we use resonant inelastic x-ray scattering to monitor the temperature dependence of the dd exciton spectrum of Bi 2 Sr 2 CaCu 2 O 8 − x crystals with different charge carrier concentrations. We observe a significant change of the dd exciton spectra when the materials pass from the normal state into the superconductor state. Our observations show that the dd excitons start to shift up (down) in the overdoped (underdoped) sample when the material enters the superconducting phase. We attribute the superconductivity-induced effect and its sign reversal from underdoped to overdoped to the exchange coupling of the site of the dd exciton to the surrounding copper spins.

W. Zhang

Papers

Multi-Objective Scheduling Simulation of Flexible Job-Shop Based on Multi-Population Genetic Algorithm

Resonant Inelastic X-Ray Scattering Study of Electron-Exciton Coupling in High- T c Cuprates