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Systems
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Scheduling Theory and Models in Industrial Management
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Scheduling Theory and Models in Industrial Management

A Special Issue of Systems (ISSN 2079-8954) belonging to the section "Systems Theory and Methodology".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1367

Editor

Prof. Dr. Ruben Ruiz-Torrubiano

E-Mail Website
Guest Editor
Department of Science and Technology, Institute Digitalisation and Informatics, IMC University of Applied Sciences Krems, 3500 Krems, Austria
Interests: optimization; machine learning; digitalization

Special Issue Information

Dear Colleagues,

Industrial management involves processes related to allocating resources, managing production timelines, and monitoring efficiency across manufacturing operations. For these processes to succeed in practice, scheduling models and algorithms play a pivotal role in optimizing the efficiency and effectiveness of all activities. In recent years, advanced planning and scheduling approaches based on real-time data integration have been used to automate, simulate, and optimize scheduling by dynamically adapting to changes and disruptions. Using constrained-based approaches, symbolic rules are introduced to accommodate machine downtime and workforce availability, paving the way for advanced algorithmic techniques to find practical solutions.

This Special Issue considers all scheduling approaches related to industrial management problems, including theory, models and algorithms.

Topics of interest include, but are not limited to, the following:

  • Constraint-based scheduling;
  • Dynamic scheduling models;
  • Metaheuristics, hybrid, and memetic algorithms;
  • Effective communication and monitoring;
  • Mathematical programming techniques;
  • Novel evaluation approaches and frameworks.

Prof. Dr. Ruben Ruiz-Torrubiano
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • optimization
  • scheduling
  • manufacturing
  • industrial management
  • heuristics
  • mathematical programming
  • machine learning
  • reinforcement learning
  • constraint satisfaction

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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

34 pages, 2386 KB  
Article
How to Dynamically Schedule Multiple Knowledge-Intensive Projects Under Uncertainty: An Approximate Dynamic Programming Approach
by Hongbo Li, Wei Chen, Zehui Wei, Qingkang Zhu and Xianchao Zhang
Systems 2026, 14(8), 911; https://doi.org/10.3390/systems14080911 - 1 Aug 2026
Viewed by 271
Abstract
Knowledge-intensive projects in research and development (R&D), software, and high-technology sectors are delivered by knowledge workers who each command several skills. When many such projects compete for a shared pool of multi-skilled workers, and neither project arrival times nor durations are known in [...] Read more.
Knowledge-intensive projects in research and development (R&D), software, and high-technology sectors are delivered by knowledge workers who each command several skills. When many such projects compete for a shared pool of multi-skilled workers, and neither project arrival times nor durations are known in advance, deciding who works on what at each moment becomes a sequential decision problem. Therefore, we propose the multi-skilled, multi-project dynamic scheduling problem with random project arrivals and uncertain durations and formulate it as a Markov decision process (MDP) that minimizes the total human resource cost. Since the cost-to-go function is computationally intractable, we develop a rollout-based approximate dynamic programming (ADP) algorithm that approximates it via Monte Carlo simulation embedded with a randomized base policy and restricts the action space to ten representative allocation policies. On benchmarks extended from the Project Scheduling Problem Library (PSPLIB), the proposed policy lowers the average total cost by 3.1% to 16.7% relative to Q-learning on medium- and large-scale instances while completing more projects with shorter delays; on small-scale instances Q-learning attains a lower nominal cost, revealing a trade-off among cost, completion rate, and delay. Full article
(This article belongs to the Special Issue Scheduling Theory and Models in Industrial Management)
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28 pages, 1576 KB  
Article
Heuristic Algorithms for the 1-m-1 Hybrid Flow Shop Scheduling Problem with Lot Streaming, No-Wait, Blocking, and Sequence-Dependent Setup Times
by Hyejin Park, Minseo Lee and Jinil Han
Systems 2026, 14(8), 900; https://doi.org/10.3390/systems14080900 - 1 Aug 2026
Viewed by 300
Abstract
This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a [...] Read more.
This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a single HFS model has received little attention. The problem is motivated by a real-world order sequencing problem in insulation board manufacturing, where all four constraints arise simultaneously from the production process. To formally characterize the problem, we develop a mixed-integer programming formulation that captures all operational constraints. For practical-scale problems, we propose several dispatching heuristics that can obtain sufficiently good solutions within a short computation time. We further develop a genetic algorithm as an independent solution approach to obtain high-quality solutions close to the optimum within a reasonable computation time. Computational experiments on instances generated based on real insulation board production characteristics demonstrate that the proposed algorithms outperform a benchmark greedy rule, and sensitivity analyses reveal the effects of setup time magnitude and the number of parallel machines on scheduling performance. Full article
(This article belongs to the Special Issue Scheduling Theory and Models in Industrial Management)
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23 pages, 1799 KB  
Article
Automatic Construction Method of Surrogate Evaluation Measures for Job Shop Scheduling
by Zigao Wu, Shichang Xiao and Shaohua Yu
Systems 2026, 14(6), 614; https://doi.org/10.3390/systems14060614 - 27 May 2026
Viewed by 332
Abstract
Job shop scheduling holds significant importance due to its relevance and impact on various industrial and manufacturing systems. Aiming at the job shop scheduling problem with random machine breakdowns, a multi-objective optimization model is established, which considers both the makespan and expected makespan [...] Read more.
Job shop scheduling holds significant importance due to its relevance and impact on various industrial and manufacturing systems. Aiming at the job shop scheduling problem with random machine breakdowns, a multi-objective optimization model is established, which considers both the makespan and expected makespan delay simultaneously. Considering that the expected makespan delay cannot be calculated analytically, this paper proposes a symbolic regression-based construction method, which can automatically learn a surrogate evaluation measure. Then, a multi-objective evolutionary algorithm is proposed for solving this model, where the constructed surrogate evaluation measure is used to replace the expected makespan delay for fitness evaluation, to achieve rapid evaluation and efficient optimization. Finally, extensive simulation experiments are conducted on 40 benchmark problems of job shop scheduling, which verify the effectiveness of the proposed method and its advantages in computational efficiency. Full article
(This article belongs to the Special Issue Scheduling Theory and Models in Industrial Management)
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