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Processes
Special Issues
Production Scheduling and Optimization Control on Advanced Manufacturing (2nd Edition)
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Production Scheduling and Optimization Control on Advanced Manufacturing (2nd Edition)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 30 May 2024 | Viewed by 3244

Special Issue Editors

Prof. Dr. Danyu Bai

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Guest Editor
School of Maritime Economics & Management, Dalian Maritime University, Dalian 116026, China
Interests: production scheduling; intelligent algorithms; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Chen

E-Mail Website
Guest Editor
School of Electronics & Information Engineering, Liaoning University of Technology, Jinzhou 121001, China
Interests: production scheduling; combinatorial optimization; algorithm evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Dehua Xu

E-Mail Website
Guest Editor
School of International Economics & Business, Nanjing University of Finance & Economics, Nanjing 210023, China
Interests: machine scheduling; approximation algorithm; process optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Jedrzej Musial

E-Mail Website
Guest Editor
Faculty of Computing and Telecommunications, Poznan University of Technology, Poznan, Poland
Interests: combinatorial optimization; algorithm design; e-commerce; uav traffic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue, “Production Scheduling and Optimization Control on Advanced Manufacturing” (for more details, see the MDPI website: https://www.mdpi.com/si/136399), we decided to organize a second edition of this Special Issue.

Advances in smart technologies, such as industrial big data, the Internet of Things and cloud computing, enable manufacturing to achieve intellectualization, greenization, and customization. Therefore, academia and industry have been paying increasing attention to achieving decision-making optimization with production scheduling and optimization control, which are the cores of intelligent production. Recently, many successful applications have been presented in advanced manufacturing processes, including product manufacture, equipment assembly, order processing, warehousing and transportation, etc.

This Special Issue aims to collect up-to-date and high-quality studies in the area of advanced manufacturing, with novel methods of production scheduling and optimization control, and to promote developments and applications of optimization theory and methods in relevant fields. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Production scheduling in advanced manufacturing;
  • Optimization control in industrial production;
  • Reinforcement learning-based production optimization;
  • Routing optimization in product distribution;
  • Data-driven production process optimization;
  • Optimization for industrial facility location.

We look forward to receiving your contributions.

Prof. Dr. Danyu Bai
Prof. Dr. Xin Chen
Dr. Dehua Xu
Dr. Jedrzej Musial
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • production scheduling
  • optimization control
  • routing optimization
  • facility location
  • evolutionary computation
  • intelligent algorithm
  • machine learning
  • advanced manufacturing

Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 2660 KiB  
Article
Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem
by Ke Xu, Caixia Ye, Hua Gong and Wenjuan Sun
Processes 2024, 12(1), 51; https://doi.org/10.3390/pr12010051 - 25 Dec 2023
Cited by 1 | Viewed by 763
Abstract
Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective [...] Read more.
Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective of the makespan and the total energy consumption while taking into consideration blocking and transportation restrictions. An adaptive objective selection-based Q-learning algorithm is designed to solve the problem. Nine state characteristics are extracted from real-time information about jobs, machines, and waiting processing queues. As scheduling actions, eight heuristic rules are used, including SPT, FCFS, Johnson, and others. To address the multi-objective optimization problem, an adaptive objective selection strategy based on t-tests is designed for making action decisions. This strategy can determine the optimization objective based on the confidence of the objective function under the current job and machine state, achieving coordinated optimization for multiple objectives. The experimental results indicate that the proposed algorithm, in comparison to Q-learning and the non-dominated sorting genetic algorithm, has shown an average improvement of 4.19% and 22.7% in the makespan, as well as 5.03% and 9.8% in the total energy consumption, respectively. The generated scheduling solutions provide theoretical guidance for production scheduling in process industries such as steel manufacturing. This contributes to helping enterprises reduce blocking and transportation energy consumption between upstream and downstream. Full article
(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing (2nd Edition))
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17 pages, 2793 KiB  
Article
Multi-Objective Flexible Flow Shop Production Scheduling Problem Based on the Double Deep Q-Network Algorithm
by Hua Gong, Wanning Xu, Wenjuan Sun and Ke Xu
Processes 2023, 11(12), 3321; https://doi.org/10.3390/pr11123321 - 29 Nov 2023
Cited by 1 | Viewed by 867
Abstract
In this paper, motivated by the production process of electronic control modules in the digital electronic detonators industry, we study a multi-objective flexible flow shop scheduling problem. The objective is to find a feasible schedule that minimizes both the makespan and the total [...] Read more.
In this paper, motivated by the production process of electronic control modules in the digital electronic detonators industry, we study a multi-objective flexible flow shop scheduling problem. The objective is to find a feasible schedule that minimizes both the makespan and the total tardiness. Considering the constraints imposed by the jobs and the machines throughout the manufacturing process, a mixed integer programming model is formulated. By transforming the scheduling problem into a Markov decision process, the agent state features and the actions are designed based on the processing status of the machines and the jobs, along with heuristic rules. Furthermore, a reward function based on the optimization objectives is designed. Based on the deep reinforcement learning algorithm, the Dueling Double Deep Q-Network (D3QN) algorithm is designed to solve the scheduling problem by incorporating the target network, the dueling network, and the experience replay buffer. The D3QN algorithm is compared with heuristic rules, the genetic algorithm (GA), and the optimal solutions generated by Gurobi. The ablation experiments are designed. The experimental results demonstrate the high performance of the D3QN algorithm with the target network and the dueling network proposed in this paper. The scheduling model and the algorithm proposed in this paper can provide theoretical support to make the production plan of electronic control modules reasonable and improve production efficiency. Full article
(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing (2nd Edition))
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24 pages, 4146 KiB  
Article
An Efficient and Improved Coronavirus Herd Immunity Algorithm Using Knowledge-Driven Variable Neighborhood Search for Flexible Job-Shop Scheduling Problems
by Xunde Ma, Li Bi, Xiaogang Jiao and Junjie Wang
Processes 2023, 11(6), 1826; https://doi.org/10.3390/pr11061826 - 15 Jun 2023
Cited by 1 | Viewed by 1045
Abstract
By addressing the flexible job shop scheduling problem (FJSP), this paper proposes a new type of algorithm for the FJSP. We named it the hybrid coronavirus population immunity optimization algorithm. Based on the characteristics of the problem, firstly, this paper redefined the discretized [...] Read more.
By addressing the flexible job shop scheduling problem (FJSP), this paper proposes a new type of algorithm for the FJSP. We named it the hybrid coronavirus population immunity optimization algorithm. Based on the characteristics of the problem, firstly, this paper redefined the discretized two-stage individual encoding and decoding scheme. Secondly, in order to realize the multi-scale search of the solution space, a multi-population update mechanism is designed, and a collaborative learning method is proposed to ensure the diversity of the population. Then, an adaptive mutation operation is introduced to enrich the diversity of the population, relying on the adaptive adjustment of the mutation operator to balance global search and local search capabilities. In order to realize a directional and efficient neighborhood search, this algorithm proposed a knowledge-driven variable neighborhood search strategy. Finally, the algorithm’s performance comparison experiment is carried out. The minimum makespans on the MK06 medium-scale case and MK10 large-scale case are 58 and 201, respectively. The experimental results verify the effectiveness of the hybrid algorithm. Full article
(This article belongs to the Special Issue Production Scheduling and Optimization Control on Advanced Manufacturing (2nd Edition))
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