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Mathematics
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Computational Modelling and Optimization in Production, Logistics, and Supply Chain
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Computational Modelling and Optimization in Production, Logistics, and Supply Chain

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 11253

Special Issue Editors

Dr. Victor Fernandez-Viagas

E-Mail Website
Guest Editor
Industrial Management, School of Engineering, University of Seville, Seville, Spain
Interests: planning and scheduling in manufacturing and healthcare systems
Special Issues, Collections and Topics in MDPI journals
Dr. Masood Fathi

E-Mail Website
Guest Editor
1. Division of Industrial Engineering and Management, Uppsala University, P.O. Box 534, 75121 Uppsala, Sweden
2. School of Engineering Science, University of Skövde, P.O. Box 408, 54128 Skövde, Sweden
Interests: operations management; industry 4.0; industrial digital transformation; operations research; production and logistics optimization; heuristic and metaheuristic algorithms; mathematical modeling; simulation-based optimization;production planning and scheduling; supply chain management

Special Issue Information

Dear Colleagues,

Optimization techniques and computational models are widely used to address complex problems including production, logistics, and supply chain. Designing and controlling complex production and logistics systems, and supply chain networks have always been challenging for decision-makers, organizations, and industry. Computational modelling and optimization form an integrated part of any modern design practice in engineering and industry. Despite the tremendous progress on computational methods and optimization approaches over the last few decades, many challenging issues remain unresolved. Thus, introducing recent trends in modelling and optimization, and their applications are of prime importance to both scholars and practitioners.

This special issue aims to publish state-of-the-art research papers concerning the application of Computational models and optimization techniques to tackle the production, logistics, and supply chain problems. We highly encourage and invite researchers to contribute to this special issue by submitting their high-quality research and review articles in these areas.

Dr. Victor Fernandez-Viagas
Dr. Masood Fathi
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
  • mathematical modeling
  • operations research
  • production
  • logistics
  • supply chain
  • computation
  • scheduling
  • sequencing

Published Papers (5 papers)

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Research

14 pages, 1658 KiB  
Article
An Iterated Population-Based Metaheuristic for Order Acceptance and Scheduling in Unrelated Parallel Machines with Several Practical Constraints
by Chun-Lung Chen
Mathematics 2023, 11(6), 1433; https://doi.org/10.3390/math11061433 - 16 Mar 2023
Viewed by 1011
Abstract
This study considers order acceptance and scheduling problems in unrelated parallel machines with several practical constraints, including order release times, sequence-dependent setup times, machines’ unequal ready times, and preventive maintenance. In a make-to-order production environment, issues with order acceptance and scheduling are mainly [...] Read more.
This study considers order acceptance and scheduling problems in unrelated parallel machines with several practical constraints, including order release times, sequence-dependent setup times, machines’ unequal ready times, and preventive maintenance. In a make-to-order production environment, issues with order acceptance and scheduling are mainly caused by the limited production capacity of a factory, which makes it impossible to accept all orders. Consequently, some orders must be rejected in order to maximize profits and the accepted orders must be completed by the due date or no later than the deadline. An iterated population-based metaheuristic is proposed to solve the problems. The algorithm begins with an efficient initial solution generator to generate an initial solution, and then uses the destruction and construction procedure to generate a population with multiple solutions. Then, a solution is selected from the population, and a variable neighborhood descent search algorithm with several new reduced-size neighborhood structures is applied to improve the selected solution. Following the completion of the local search, a method for updating the members of the population was devised to enhance its diversity. Finally, the metaheuristic allows the populations to evolve for several generations until the termination condition is satisfied. To evaluate the performance of the proposed metaheuristic, a heuristic rule and an iterated local search algorithm are examined and compared. The computational experimental results indicate that the presented metaheuristic outperforms the other heuristics. Full article
(This article belongs to the Special Issue Computational Modelling and Optimization in Production, Logistics, and Supply Chain)
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32 pages, 737 KiB  
Article
The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm
by Victor Fernandez-Viagas, Luis Sanchez-Mediano, Alvaro Angulo-Cortes, David Gomez-Medina and Jose Manuel Molina-Pariente
Mathematics 2022, 10(19), 3446; https://doi.org/10.3390/math10193446 - 22 Sep 2022
Cited by 4 | Viewed by 2583
Abstract
In this paper, we address the permutation flow shop scheduling problem with sequence-dependent and non-anticipatory setup times. These setups are performed or supervised by multiple servers, which are renewable secondary resources (typically human resources). Despite the real applications of this kind of human [...] Read more.
In this paper, we address the permutation flow shop scheduling problem with sequence-dependent and non-anticipatory setup times. These setups are performed or supervised by multiple servers, which are renewable secondary resources (typically human resources). Despite the real applications of this kind of human supervision and the growing attention paid in the scheduling literature, we are not aware of any previous study on the problem under consideration. To cover this gap, we start theoretically addressing the problem by: proposing three mixed-integer linear programming models to find optimal solutions in the problem; and proposing different decoding procedures to code solutions in approximated procedures. After that, the best decoding procedure is used to propose a new mechanism that generates 896 different dispatching rules, combining different measures, indicators, and sorting criteria. All these dispatching rules are embedded in the traditional NEH algorithm. Finally, an iterated greedy algorithm is proposed to find near-optimal solutions. By doing so, we provide academics and practitioners with efficient methods that can be used to obtain exact solutions of the problem; applied to quickly schedule jobs and react under changes; used for initialisation or embedded in more advanced algorithms; and/or easily updated and implemented in real manufacturing scenarios. Full article
(This article belongs to the Special Issue Computational Modelling and Optimization in Production, Logistics, and Supply Chain)
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22 pages, 2723 KiB  
Article
Variable Neighborhood Search Algorithms to Solve the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery
by Yusuf Yilmaz and Can B. Kalayci
Mathematics 2022, 10(17), 3108; https://doi.org/10.3390/math10173108 - 29 Aug 2022
Cited by 10 | Viewed by 2532
Abstract
This paper addresses the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery (EVRP-SPD), in which electric vehicles (EVs) simultaneously deliver goods to and pick up goods from customers. Due to the limited battery capacity of EVs, their range is shorter than that [...] Read more.
This paper addresses the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery (EVRP-SPD), in which electric vehicles (EVs) simultaneously deliver goods to and pick up goods from customers. Due to the limited battery capacity of EVs, their range is shorter than that of internal combustion vehicles. In the EVRP, in addition to the depot and the customers, there are also charging stations (CS) because EVs need to be charged when their battery is empty. The problem is formulated as an integer linear model, and an efficient solution is proposed to minimize the total distance traveled. To create a feasible initial solution, Clarke and Wright’s savings algorithm is used. Several variants of variable neighborhood search are tested, and the reduced-variable neighborhood search algorithm is used to find the best solution in a reasonable time. Computer experiments are performed with benchmark instances to evaluate the effectiveness of our approach in terms of solution quality and time. The obtained results show that the proposed method can achieve efficient solutions in terms of solution quality and time in all benchmark instances. Full article
(This article belongs to the Special Issue Computational Modelling and Optimization in Production, Logistics, and Supply Chain)
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27 pages, 5379 KiB  
Article
Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption
by Nan Ding, Jingshuai Yang, Zhibin Han and Jianming Hao
Mathematics 2022, 10(17), 3099; https://doi.org/10.3390/math10173099 - 29 Aug 2022
Cited by 7 | Viewed by 1618
Abstract
In this paper, we establish the Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC) to optimize the delivery routing of electric vehicles (EVs). As energy consumption may affect the maximal driving range and the recharging behavior of EVs, we [...] Read more.
In this paper, we establish the Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC) to optimize the delivery routing of electric vehicles (EVs). As energy consumption may affect the maximal driving range and the recharging behavior of EVs, we first develop a nonlinear electric energy consumption model based on typical driving cycles of suburban and urban areas, with consideration of vehicle load, travel distance, and speed. An adaptive particle swarm optimization algorithm is then designed to solve the problem. Moreover, we study cases built from the actual operational data of Company J and compare the optimal delivery schemes of EVRPTW-DC and EVRPTW under the traditional linear electric energy consumption law. The results show that our nonlinear energy consumption model, which provides a better simulation of energy consumption, can lead to a more realistic delivery plan. Finally, we explore the applicability of the proposed EVPRTW-DC and discuss the conditions of using a linear electric energy consumption coefficient. Full article
(This article belongs to the Special Issue Computational Modelling and Optimization in Production, Logistics, and Supply Chain)
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16 pages, 2691 KiB  
Article
Risk Propagation and Supply Chain Health Control Based on the SIR Epidemic Model
by Di Liang, Ran Bhamra, Zhongyi Liu and Yucheng Pan
Mathematics 2022, 10(16), 3008; https://doi.org/10.3390/math10163008 - 20 Aug 2022
Cited by 5 | Viewed by 2265
Abstract
Risk propagation is occurring as an exceptional challenge to supply chain management. Identifying which supplier has the greater possibility of interruptions is pivotal for managing the occurrence of these risks, which have a significant impact on the supply chain. Identifying and predicting how [...] Read more.
Risk propagation is occurring as an exceptional challenge to supply chain management. Identifying which supplier has the greater possibility of interruptions is pivotal for managing the occurrence of these risks, which have a significant impact on the supply chain. Identifying and predicting how these risks propagate and understanding how these risks dynamically diffuse if control strategies are installed can help to better manage supply chain risks. Drawing on the complex systems and epidemiological literature, we research the impact of the global supply network structure on risk propagation and supply network health. The SIR model is used to dynamically identify and predict the risk status of the supply chain risk at different times. The results show that there is a significant relationship between network structure and risk propagation and supply network health. We demonstrate the importance of supply network visibility and of the extraction of the information of node firms. We build up an R package for geometric graphs and epidemics. This paper applies the R package to model the supply chain risk for an automotive manufacturing company. The R package provides a firm to construct the complicated interactions among suppliers and display how these interactions impact on risks. Theoretically, our study adapts a computational approach to contribute to the understanding of risk management and supply networks. Managerially, our study demonstrates how the supply chain network analysis approach can benefit the managers by developing a more holistic framework of system-wide risk propagation. This provides guidance for network governance policies, which will lead to healthier supply chains. Full article
(This article belongs to the Special Issue Computational Modelling and Optimization in Production, Logistics, and Supply Chain)
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