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Advances in Operations Research for Logistic and Operations Management of...
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Advances in Operations Research for Logistic and Operations Management of Supply Chain Management

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 12 March 2026 | Viewed by 886

Special Issue Editor

Prof. Dr. John Willmer Escobar

E-Mail Website
Guest Editor
Department of Accounting and Finance, Faculty of Business and Management, Universidad del Valle, Cali 760001, Colombia
Interests: vehicle routing problem; logistics; supply chain optimization; financial sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A supply chain considers different echelons, ranging from suppliers of raw materials, carriers, depots, small suppliers, manufacturers or producers, distribution centers, intermediaries, and end customers—all of which include advanced operations research techniques for solving different strategic, tactical, and operational aspects of the supply chain. These optimization techniques include exact, heuristic, and metaheuristic approaches that consider linear and non-linear supply chain problems. These tools seek to establish viable supply chains, including sustainable aspects, resilience robustness, and agility issues, by considering modern technology aspects based on big data. Viability is the supply chain's ability to survive disruptions due to multiple factors, including environmental, social, and economic aspects. Different works with the triple-bottom line aligned with economic, social, and environmental issues, such as those mentioned in the Sustainable Development Goals (SDG), considering the sustainable aspects of the supply chain. Resilience and robustness are faced with the property of chain recovery due to failures related to disruptions. Finally, agility is the supply chain characteristic that includes technological issues for the recovery of the entire network. All of these aspects for different levels are solved under the framework of the present Special Issue.  

This Special Issue aims to collate original research papers that offer the latest and different advanced operations research techniques aimed toward the development and application of different levels of supply chain management and logistics in a broad range of fields.

Prof. Dr. John Willmer Escobar
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. 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

  • heuristic and metaheuristic approaches for supply chain management and logistics
  • possibilistic approaches for supply chain management and logistics
  • viability of supply chain management
  • supply chain optimization and logistics management
  • robust approaches for supply chain management
  • resilient supply chain optimization
  • sustainable supply chains
  • green supply chains
  • agility supply chains in supply chain management
  • operations management in supply chains
  • sustainable vehicle routing problems
  • social aspects for vehicle routing problems
  • economic aspects of vehicle routing problems
  • resilient, robust, and stochastic vehicle routing problems

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Published Papers (2 papers)

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29 pages, 870 KiB  
Article
Deep Reinforcement Learning for Optimal Replenishment in Stochastic Assembly Systems
by Lativa Sid Ahmed Abdellahi, Zeinebou Zoubeir, Yahya Mohamed, Ahmedou Haouba and Sidi Hmetty
Mathematics 2025, 13(14), 2229; https://doi.org/10.3390/math13142229 - 9 Jul 2025
Viewed by 104
Abstract
This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times [...] Read more.
This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article
(This article belongs to the Special Issue Advances in Operations Research for Logistic and Operations Management of Supply Chain Management)
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31 pages, 6941 KiB  
Article
A Heuristic Approach for Last-Mile Delivery with Consistent Considerations and Minimum Service for a Supply Chain
by Esteban Santana Contreras, John Willmer Escobar and Rodrigo Linfati
Mathematics 2025, 13(10), 1553; https://doi.org/10.3390/math13101553 - 8 May 2025
Viewed by 422
Abstract
This paper considers the problem of consistent routing with minimum service (ConVRPms). ConVRPms aims to determine the minimum cost routes for each day of a planning horizon. In particular, the goal is to satisfy all individual demands and serve every customer via a [...] Read more.
This paper considers the problem of consistent routing with minimum service (ConVRPms). ConVRPms aims to determine the minimum cost routes for each day of a planning horizon. In particular, the goal is to satisfy all individual demands and serve every customer via a single driver, with times that do not differ by more than L time units. There is a fleet of homogeneous vehicles that start from a single depot. In this paper, a heuristic algorithm for ConVRPms is proposed. The algorithm is based on classical constructive heuristics and the tabu search metaheuristic. The proposed algorithm has been tested on benchmark instances from the literature. The experimental results show that the proposed approach produces high-quality solutions within computing times considerably less than those observed with CPLEX. The proposed algorithm can optimally solve instances with 20 customers and a planning horizon of three days, producing more economical solutions in some of the larger instances and those requiring hourly consistency (L=1 h). Full article
(This article belongs to the Special Issue Advances in Operations Research for Logistic and Operations Management of Supply Chain Management)
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