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Algorithms
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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 June 2024) | Viewed by 17564

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

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Published Papers (10 papers)

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Research

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25 pages, 3540 KiB  
Article
Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
by Oludolapo Akanni Olanrewaju, Fabio Luiz Peres Krykhtine and Felix Mora-Camino
Algorithms 2024, 17(11), 520; https://doi.org/10.3390/a17110520 - 12 Nov 2024
Viewed by 432
Abstract
This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation [...] Read more.
This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is replaced by the satisfaction of due times for each job. An original two-level mixed-integer formulation of this optimization problem is proposed, where the processed flows of material and their timing are explicitly considered. Its exact solution is discussed, and, considering its computational complexity, a comprehensive heuristic, balancing energy performance and due time constraint satisfaction, is developed to provide acceptable solutions in polynomial time to the minimum-energy flexible job-shop scheduling problem, even when considering its dynamic environment. The proposed approach is illustrated through a small-scale example. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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12 pages, 251 KiB  
Article
An Algorithm for Part Input Sequencing of Flexible Manufacturing Systems with Machine Disruption
by Yumin He, Alexandre Dolgui and Milton Smith
Algorithms 2024, 17(10), 470; https://doi.org/10.3390/a17100470 - 21 Oct 2024
Viewed by 444
Abstract
Because disruption happens unpredictably and generates serious impact in supply chain and production environments in the real world, it is important to develop approaches to handle disruption. This paper investigates disruption handling in part input sequencing of flexible manufacturing systems (FMSs). An algorithm [...] Read more.
Because disruption happens unpredictably and generates serious impact in supply chain and production environments in the real world, it is important to develop approaches to handle disruption. This paper investigates disruption handling in part input sequencing of flexible manufacturing systems (FMSs). An algorithm is proposed for FMS part input sequencing to handle machine breakage. Evaluation is performed for the proposed algorithm by simulation experiments and result analyses. Finally, conclusions are summarized with managerial implications discussed and further research works suggested. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
18 pages, 1001 KiB  
Article
The Parallel Machine Scheduling Problem with Different Speeds and Release Times in the Ore Hauling Operation
by Luis Tarazona-Torres, Ciro Amaya, Alvaro Paipilla, Camilo Gomez and David Alvarez-Martinez
Algorithms 2024, 17(8), 348; https://doi.org/10.3390/a17080348 - 8 Aug 2024
Viewed by 954
Abstract
Ore hauling operations are crucial within the mining industry as they supply essential minerals to production plants. Conducted with sophisticated and high-cost operational equipment, these operations demand meticulous planning to ensure that production targets are met while optimizing equipment utilization. In this study, [...] Read more.
Ore hauling operations are crucial within the mining industry as they supply essential minerals to production plants. Conducted with sophisticated and high-cost operational equipment, these operations demand meticulous planning to ensure that production targets are met while optimizing equipment utilization. In this study, we present an algorithm to determine the minimum amount of hauling equipment required to meet the ore transport target. To achieve this, a mathematical model has been developed, considering it as a parallel machine scheduling problem with different speeds and release times, focusing on minimizing both the completion time and the costs associated with equipment use. Additionally, another algorithm was developed to allow the tactical evaluation of these two variables. These procedures and the model contribute significantly to decision-makers by providing a systematic approach to resource allocation, ensuring that loading and hauling equipment are utilized to their fullest potentials while adhering to budgetary constraints and operational schedules. This approach optimizes resource usage and improves operational efficiency, facilitating continuous improvement in mining operations. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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19 pages, 768 KiB  
Article
Maximizing the Average Environmental Benefit of a Fleet of Drones under a Periodic Schedule of Tasks
by Vladimir Kats and Eugene Levner
Algorithms 2024, 17(7), 283; https://doi.org/10.3390/a17070283 - 28 Jun 2024
Cited by 1 | Viewed by 860
Abstract
Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport, and material handling. In particular, using battery-powered UAVs to transport products can significantly [...] Read more.
Unmanned aerial vehicles (UAVs, drones) are not just a technological achievement based on modern ideas of artificial intelligence; they also provide a sustainable solution for green technologies in logistics, transport, and material handling. In particular, using battery-powered UAVs to transport products can significantly decrease energy and fuel expenses, reduce environmental pollution, and improve the efficiency of clean technologies through improved energy-saving efficiency. We consider the problem of maximizing the average environmental benefit of a fleet of drones given a periodic schedule of tasks performed by the fleet of vehicles. To solve the problem efficiently, we formulate it as an optimization problem on an infinite periodic graph and reduce it to a special type of parametric assignment problem. We exactly solve the problem under consideration in O(n3) time, where n is the number of flights performed by UAVs. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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21 pages, 4805 KiB  
Article
Multi-Criteria Decision Support System for Automatically Selecting Photovoltaic Sets to Maximise Micro Solar Generation
by Guilherme Zanlorenzi, Anderson Luis Szejka and Osiris Canciglieri Junior
Algorithms 2024, 17(7), 274; https://doi.org/10.3390/a17070274 - 22 Jun 2024
Viewed by 1005
Abstract
Technological advancements have improved solar energy generation and reduced the cost of installing photovoltaic (PV) systems. However, challenges such as low energy-conversion efficiency and the unpredictability of electricity generation due to shading or climate conditions persist. Despite decreasing costs, access to solar energy [...] Read more.
Technological advancements have improved solar energy generation and reduced the cost of installing photovoltaic (PV) systems. However, challenges such as low energy-conversion efficiency and the unpredictability of electricity generation due to shading or climate conditions persist. Despite decreasing costs, access to solar energy generation technologies remains limited. This paper proposes a multi-criteria decision support system (MCDSS) for selecting the most suitable PV set (comprising PV modules, inverters, and batteries) for microgrid installations. The MCDSS employs two multi-criteria decision-making methods (MCDM) for analysis and decision-making: AHP and TOPSIS. The system was tested in two case studies: Barreiras, with a global efficiency of 14.4% and an internal rate of return (IRR) of 56.0%, and Curitiba, with a worldwide efficiency of 14.8% and an IRR of 52.0%. The research provided a framework for assessing and selecting PV sets based on efficiency, cost, and return on investment. Methodologically, it integrates multiple MCDM techniques, demonstrating their applicability in renewable energy. Managerially, it offers a practical tool for decision-makers in the energy sector to enhance the feasibility and attractiveness of microgeneration projects. This research highlights the potential of MCDSS to improve the efficiency and accessibility of solar energy generation. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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21 pages, 1299 KiB  
Article
Towards Sustainable Inventory Management: A Many-Objective Approach to Stock Optimization in Multi-Storage Supply Chains
by João A. M. Santos, Miguel S. E. Martins, Rui M. Pinto and Susana M. Vieira
Algorithms 2024, 17(6), 271; https://doi.org/10.3390/a17060271 - 20 Jun 2024
Viewed by 2437
Abstract
Within the framework of sustainable supply chain management and logistics, this work tackles the complex challenge of optimizing inventory levels across varied storage facilities. It introduces a comprehensive many-objective optimization model designed to minimize holding costs, energy consumption, and shortage risk concurrently, thereby [...] Read more.
Within the framework of sustainable supply chain management and logistics, this work tackles the complex challenge of optimizing inventory levels across varied storage facilities. It introduces a comprehensive many-objective optimization model designed to minimize holding costs, energy consumption, and shortage risk concurrently, thereby integrating sustainability considerations into inventory management. The model incorporates the distinct energy consumption profiles associated with various storage types and evaluates the influence of stock levels on energy usage. Through an examination of a 60-day production schedule, the dynamic relationship between inventory levels and operational objectives is investigated, revealing a well-defined set of optimal solutions that highlight the trade-off between energy savings and shortage risk. Employing a 30-day rolling forward analysis with daily optimization provides insights into the evolving nature of inventory optimization. Additionally, the model is extended to encompass a five-objective optimization by decomposing shortage risk, offering a nuanced comprehension of inventory risks. The outcomes of this research provide a range of optimal solutions, empowering supply chain managers to make informed decisions that strike a balance among cost, energy efficiency, and supply chain resilience. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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11 pages, 924 KiB  
Article
Mitigating Co-Activity Conflicts and Resource Overallocation in Construction Projects: A Modular Heuristic Scheduling Approach with Primavera P6 EPPM Integration
by Khwansiri Ninpan, Shuzhang Huang, Francesco Vitillo, Mohamad Ali Assaad, Lies Benmiloud Bechet and Robert Plana
Algorithms 2024, 17(6), 230; https://doi.org/10.3390/a17060230 - 24 May 2024
Viewed by 1227
Abstract
This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates Primavera P6 EPPM and Synchro 4D, enabling proactive clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. [...] Read more.
This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates Primavera P6 EPPM and Synchro 4D, enabling proactive clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. This integrated approach facilitates the generation of conflict-free and feasible construction schedules. By adhering to project constraints and seamlessly integrating with existing industry tools, the proposed solution offers a comprehensive and robust approach to construction project management. This constitutes, to our knowledge, the first dynamic digital twin for the delivery of a complex project. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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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://doi.org/10.3390/a16100473 - 7 Oct 2023
Cited by 1 | Viewed by 2007
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://doi.org/10.3390/a16080386 - 11 Aug 2023
Cited by 1 | Viewed by 1635
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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Review

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23 pages, 5009 KiB  
Review
A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
by Vladimir Modrak, Ranjitharamasamy Sudhakarapandian, Arunmozhi Balamurugan and Zuzana Soltysova
Algorithms 2024, 17(8), 343; https://doi.org/10.3390/a17080343 - 7 Aug 2024
Viewed by 1374
Abstract
In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to [...] Read more.
In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of pertinent publications contained in Scopus, ScienceDirect, and Google Scholar databases. The study focuses on a wide spectrum of publications spanning the years between 1996 and 2024. The findings of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques. Full article
(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)
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