Advances in Combinatorial Optimization with Applications in Logistics and Supply Chain Management

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1473

Special Issue Editors


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Guest Editor
Department of Engineering UNS, II-UNS and IIESS UNS-CONICET, Bahía Blanca, Argentina
Interests: combinatorial optimization; operations research; metaheuristics; systems modeling

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Guest Editor
Department of Economics UNS and INMABB UNS-CONICET, Bahía Blanca, Argentina
Interests: decision problems; game theory; optimization in socio-economic settings

Special Issue Information

Dear Colleagues,

Combinatorial optimization is a multidisciplinary field that studies the design of methods to find optimal solutions to problems with discrete variables. The solutions obtained from combinatorial optimization can be used to improve decision-making in a wide range of areas, including engineering, economics and management.

One area where combinatorial optimization has had a significant impact is supply chain management (SCM). Supply chains are complex networks of organizations that are responsible for the procurement, production, and distribution of goods and services. The efficient management of supply chains is essential for businesses to remain competitive.

Combinatorial optimization can be used to solve a variety of problems that arise in supply chain management, such as:

  • Designing transportation routes.
  • Allocating requests and loads on vehicles.
  • Designing production schedules.
  • Planning operations.
  • Inventory management.
  • Designing distribution networks.

The solutions obtained from combinatorial optimization can help businesses to reduce costs, improve efficiency, and increase profits.

This Special Issue is dedicated to the publication of cutting-edge research on the applications of combinatorial optimization to SCM. The issue is open to submissions of high quality and originality. Submissions that address the following topics are particularly welcome:

  • Exact solution methods for combinatorial optimization problems (for instance, exhaustive enumeration, branch and bound, dynamic programming, cutting plane, branch and cut, among others).
  • Meta-heuristic and bio-inspired algorithms for combinatorial optimization problems (e.g., differential evolution, evolutionary algorithms, fuzzy optimization, hyper-heuristics, meta-heuristics, particle swarm optimization, etc.).
  • Applications of combinatorial optimization to SCM.

We hope that this Special Issue will make a significant contribution to the literature on supply chain management.

Prof. Dr. Mariano Frutos
Prof. Dr. Fernando Tohmé 
Guest Editors

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Keywords

  • combinatorial optimization
  • supply chain management
  • exact solutions
  • meta-heuristics
  • real-world applications

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Published Papers (1 paper)

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23 pages, 615 KiB  
Article
Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System
by Fabio Maximiliano Miguel, Mariano Frutos, Máximo Méndez, Fernando Tohmé and Begoña González
Mathematics 2024, 12(8), 1246; https://doi.org/10.3390/math12081246 - 19 Apr 2024
Viewed by 861
Abstract
This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve [...] Read more.
This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area. Full article
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