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Algorithms
Special Issues
Scheduling Theory and Algorithms for Sustainable Manufacturing
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Scheduling Theory and Algorithms for Sustainable Manufacturing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 6240

Special Issue Editors

Prof. Dr. Alexandre Dolgui

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Guest Editor
Department of Automation, Production and Computer Sciences (DAPI), IMT Atlantique, Cedex 3, Nantes, France
Interests: production planning; manufacturing engineering; supply chain management; mathematical programming optimizers; logistics; lean manufacturing
Prof. Dr. David Lemoine

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Guest Editor
Department of Control and Industrial Engineering (DAP), IMT-Atlantique, 44300 Nantes, France
Interests: tactical planning; optimization
Dr. María I. Restrepo

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Guest Editor
Department of Automation, Production and Computer Science, IMT Atlantique, 44300 Nantes, France
Interests: operations research; data science; healthcare; logistics
Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling, in particular development of exact and approximate algorithms; stability investigations is discrete optimization; scheduling with interval processing times; complexity investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue was initiated at the 10th IFAC triennial conference MIM 2022 (https://hub.imt-atlantique.fr/mim2022/) concerning the topics of combinatorial optimization and scheduling and corresponding sessions and tracks.

Following their presentation at the conference, some authors were invited to submit an extended version of their work to this Special Issue. However, the Special Issue is also open to papers that were not presented at the conference if they are in the scope of the issue.

The aim of this Special Issue is to present state-of-the art mathematical models and algorithms providing efficient solutions for practical planning and scheduling issues in sustainable manufacturing and logistics. Currently, the production and logistics systems for goods and services are faced with both production cost optimization and scarcity of resources. Scheduling plays a central role and offers the possibility to:

  • Reduce production waste,
  • Manage efficiently and limit the consumption of material resources and energy,
  • use efficiently new energy sources, especially renewable ones.

Potential topics to be addressed in this issue on the contributions of scheduling theory and algorithm for sustainable manufacturing include, but are not limited to, the following:

  • Consideration of energy constraints in scheduling and planning;
  • Green scheduling approaches in Industry 4.0;
  • Advanced scheduling and planning algorithms for minimization of waste;
  • Contributions of scheduling and planning theory for minimization of the carbon emissions;
  • Multi-objective scheduling problems taking into account the sustainability criteria.
  • Exact and approximate models and methods for sustainable scheduling and planning;
  • Industrial applications of advanced scheduling and planning algorithms.

Prof. Dr. Alexandre Dolgui
Prof. Dr. David Lemoine
Dr. María I. Restrepo
Prof. Dr. Frank Werner
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. Algorithms 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 1600 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

  • combinatorial optimisation
  • planning and scheduling
  • graph theory
  • mathematical programming
  • decomposition approaches
  • approximation schemes
  • heuristics and metaheuristics
  • multicriteria optimisation

Published Papers (2 papers)

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15 pages, 699 KiB  
Article
Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm
by Manli Dai and Zhongyi Jiang
Algorithms 2023, 16(10), 473; https://0-doi-org.brum.beds.ac.uk/10.3390/a16100473 - 07 Oct 2023
Cited by 1 | Viewed by 1557
Abstract
An improved slime mold algorithm (IMSMA) is presented in this paper for a multiprocessor multitask fair scheduling problem, which aims to reduce the average processing time. An initial population strategy based on Bernoulli mapping reverse learning is proposed for the slime mold algorithm. [...] Read more.
An improved slime mold algorithm (IMSMA) is presented in this paper for a multiprocessor multitask fair scheduling problem, which aims to reduce the average processing time. An initial population strategy based on Bernoulli mapping reverse learning is proposed for the slime mold algorithm. A Cauchy mutation strategy is employed to escape local optima, and the boundary-check mechanism of the slime mold swarm is optimized. The boundary conditions of the slime mold population are transformed into nonlinear, dynamically changing boundaries. This adjustment strengthens the slime mold algorithm’s global search capabilities in early iterations and strengthens its local search capability in later iterations, which accelerates the algorithm’s convergence speed. Two unimodal and two multimodal test functions from the CEC2019 benchmark are chosen for comparative experiments. The experiment results show the algorithm’s robust convergence and its capacity to escape local optima. The improved slime mold algorithm is applied to the multiprocessor fair scheduling problem to reduce the average execution time on each processor. Numerical experiments showed that the IMSMA performs better than other algorithms in terms of precision and convergence effectiveness. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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16 pages, 3314 KiB  
Article
Reducing Nervousness in Master Production Planning: A Systematic Approach Incorporating Product-Driven Strategies
by Patricio Sáez, Carlos Herrera and Victor Parada
Algorithms 2023, 16(8), 386; https://0-doi-org.brum.beds.ac.uk/10.3390/a16080386 - 11 Aug 2023
Viewed by 1015
Abstract
Manufacturing companies face a significant challenge when developing their master production schedule, navigating unforeseen disruptions during daily operations. Moreover, fluctuations in demand pose a substantial risk to scheduling and are the main cause of instability and uncertainty in the system. To address these [...] Read more.
Manufacturing companies face a significant challenge when developing their master production schedule, navigating unforeseen disruptions during daily operations. Moreover, fluctuations in demand pose a substantial risk to scheduling and are the main cause of instability and uncertainty in the system. To address these challenges, employing flexible systems to mitigate uncertainty without incurring additional costs and generate sustainable responses in industrial applications is crucial. This paper proposes a product-driven system to complement the master production plan generated by a mathematical model. This system incorporates intelligent agents that make production decisions with a function capable of reducing uncertainty without significantly increasing production costs. The agents modify or determine the forecasted production quantities for each cycle or period. In the case study conducted, a master production plan was established for 12 products over a one-year time horizon. The proposed solution achieved an 11.42% reduction in uncertainty, albeit with a 2.39% cost increase. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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