scholarly journals Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haifei Yu ◽  
Songjian Han ◽  
Dongsheng Yang ◽  
Zhiyong Wang ◽  
Wei Feng
Keyword(s):  
Big Data ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Intelligent Manufacturing ◽  
Future Research ◽  
Cloud Platform ◽  
Actual Problem ◽  
Shop Scheduling ◽  
Digital Twin

The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

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

2021 ◽  
Vol 13 (14) ◽  
pp. 7684
Author(s):  
Raja Awais Liaqait ◽  
Shermeen Hamid ◽  
Salman Sagheer Warsi ◽  
Azfar Khalid
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Intelligent Manufacturing ◽  
Distributed Scheduling ◽  
Lead Times ◽  
Shop Scheduling ◽  
Third Phase ◽  
Customized Production

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.

Digital-Twin-Based Job Shop Scheduling Toward Smart Manufacturing

2019 ◽  
Vol 15 (12) ◽  
pp. 6425-6435 ◽  
Author(s):  
Yilin Fang ◽  
Chao Peng ◽  
Ping Lou ◽  
Zude Zhou ◽  
Jianmin Hu ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Smart Manufacturing ◽  
Shop Scheduling ◽  
Digital Twin

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

2018 ◽  
Vol 2018 ◽  
pp. 1-32 ◽  
Author(s):  
Muhammad Kamal Amjad ◽  
Shahid Ikramullah Butt ◽  
Rubeena Kousar ◽  
Riaz Ahmad ◽  
Mujtaba Hassan Agha ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Future Research ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

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.

scholarly journals On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling Problems

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 242 ◽  
Author(s):  
Julia Lange ◽  
Frank Werner
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Heuristic Method ◽  
General Purpose ◽  
Future Research ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Feasible Solutions ◽  
The One

The job shop scheduling problem with blocking constraints and total tardiness minimization represents a challenging combinatorial optimization problem of high relevance in production planning and logistics. Since general-purpose solution approaches struggle with finding even feasible solutions, a permutation-based heuristic method is proposed here, and the applicability of basic scheduling-tailored mechanisms is discussed. The problem is tackled by a local search framework, which relies on interchange- and shift-based operators. Redundancy and feasibility issues require advanced transformation and repairing schemes. An analysis of the embedded neighborhoods shows beneficial modes of implementation on the one hand and structural difficulties caused by the blocking constraints on the other hand. The applied simulated annealing algorithm generates good solutions for a wide set of benchmark instances. The computational results especially highlight the capability of the permutation-based method in constructing feasible schedules of valuable quality for instances of critical size and support future research on hybrid solution techniques.

A Survey of Recent Developments for JSSP and FJSSP Using ACO

2013 ◽  
Vol 816-817 ◽  
pp. 1133-1139
Author(s):  
Nasir Mehmood ◽  
Muhammad Umer ◽  
Ahmad Riaz
Keyword(s):  
Ant Colony Optimization ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Np Hard ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Recent Developments ◽  
Np Hard Problem

Ant Colony Optimization (ACO) is based on swarm intelligence and it is a constructive meta-heuristic which was first presented in 1991. Job Shop Scheduling Problem (JSSP) is very important problem of the manufacturing industry. JSSP is a combinatorial optimization problem which is NP-hard. The exact solution of NP-hard problem is very difficult to find. Therefore heuristics approach is the best approach for such problems. This paper shall overview the application of ant colony optimization on JSSP and Flexible Job Shop Scheduling problems (FJSSP). This paper shalll cover the major areas in which researchers have worked and it shall also recommend the future area of research in the light of this overview. This paper will also cover the quantitative analysis of the research papers which are considered in this survey. Based upon this survey some conclusions are drawn in the end.The significance of this paper is that it has covered all the efforts and major researches in the area of ACO application on JSSP and FJSSP through the inception of ACO metaheuristics. This enables the researchers and scheduling experts to overview chronologically the development of ACO on JSSP and FJSSP.

scholarly journals A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yi Feng ◽  
Mengru Liu ◽  
Yuqian Zhang ◽  
Jinglin Wang
Keyword(s):  
Optimization Algorithm ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Job shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. The recently proposed dynamic opposite learning (DOL) strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP.

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