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Systems
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Production Scheduling and Planning in Manufacturing Systems
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Production Scheduling and Planning in Manufacturing Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4904

Special Issue Editor

Prof. Dr. Frank Herrmann

E-Mail Website
Guest Editor
Ostbayerische Technische Hochschule Regensburg, 93025 Regensburg, Germany
Interests: for operational production planning and control: quantitative methods; (stochastic) optimisation (models and solution methods); simulation; case studies

Special Issue Information

Dear Colleagues,

This Special Issue showcases the latest research in the field of production scheduling and planning in manufacturing systems.

The focus is on two important practice-relevant aspects that are the subject of intense research: the consideration of limited capacities and the handling of uncertainty. Planning models and procedures should be addressed. Traditionally, heuristics and optimization models are used. The possibilities of extending these to include artificial intelligence methods should form a focal point.

The papers may frame operational production planning and control as the backbone of production process planning in commercially available IT systems. These approaches are welcome to be implemented in industrial practice.

The following topics serve as examples, but are by no means exhaustive.

  • Modelling and consideration of uncertainty through forecasting, inventory strategies, safety stocks, scenario technology, and stochastic optimization.
  • Planning improvement through resource-constrained project scheduling.
  • Lot sizing under capacity restrictions.
  • Scheduling.
  • Clearing functions for capacity estimation.
  • Simulation and optimization.
  • Heuristic solution methods.
  • Methods of artificial intelligence:
    • Symbolic and neural artificial intelligence.
    • Simulation methods and phenomenological methods.
    • Mathematically based approaches from statistics, mathematical programming, and approximation theory.

Prof. Dr. Frank Herrmann
Guest Editor

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. Systems 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

  • planning hierarchy
  • lot-sizing
  • scheduling
  • optimisation
  • (meta) heuristics
  • machine learning, neuronal networks
  • uncertainty
  • limited capacities

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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20 pages, 3167 KiB  
Article
A System Dynamics Stability Model for Discrete Production Ramp-Up
by Julian Haller, Bharath Kumar, Amon Göppert and Robert H. Schmitt
Systems 2024, 12(12), 575; https://doi.org/10.3390/systems12120575 - 18 Dec 2024
Viewed by 1024
Abstract
Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail [...] Read more.
Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research. Full article
(This article belongs to the Special Issue Production Scheduling and Planning in Manufacturing Systems)
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24 pages, 4972 KiB  
Article
Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing
by Qinglei Zhang, Ning Li, Jianguo Duan, Jiyun Qin and Ying Zhou
Systems 2024, 12(4), 133; https://doi.org/10.3390/systems12040133 - 15 Apr 2024
Cited by 3 | Viewed by 2391
Abstract
In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer [...] Read more.
In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios. Full article
(This article belongs to the Special Issue Production Scheduling and Planning in Manufacturing Systems)
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Review

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24 pages, 1589 KiB  
Review
Lot-Streaming Workshop Scheduling with Operation Flexibility: Review and Extension
by Zhiqiang Tian, Xingyu Jiang, Weijun Liu, Baohai Zhao, Shun Liu, Qingze Tan and Guangdong Tian
Systems 2025, 13(4), 271; https://doi.org/10.3390/systems13040271 - 9 Apr 2025
Viewed by 279
Abstract
Lot-streaming scheduling methods with operation flexibility have been widely used in aerospace, semiconductor, automotive, pharmaceutical and other manufacturing enterprises. Lot-splitting scheduling methods have attracted much more attention from academia and industry due to an urgent requirement for an effective way to improve the [...] Read more.
Lot-streaming scheduling methods with operation flexibility have been widely used in aerospace, semiconductor, automotive, pharmaceutical and other manufacturing enterprises. Lot-splitting scheduling methods have attracted much more attention from academia and industry due to an urgent requirement for an effective way to improve the productivity of the flexible workshop scheduling. During the past decade, many works have been made on the different lot-streaming scheduling methods of the flexible workshop scheduling. The scope of this review focuses on the journal publications collected in the Web of Science database, among which 80% are from high-ranked journals. This paper aims to provide a comprehensive survey on the lot-streaming workshop scheduling with operation flexibility. First, the lot-streaming methods of jobs are discussed and the objectives as well as constraints in applications are summarized. Then, the problem models and their solution approaches are reviewed. Next, the research trends of problem applications, modeling and solution approaches are recalled. Finally, the potential future research directions are concluded. Full article
(This article belongs to the Special Issue Production Scheduling and Planning in Manufacturing Systems)
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