Development of a coded suite of models to explore relevant problems in logistics.
Santiago-Omar Caballero-Morales1
1Postgraduate Department of Logistics and Supply Chain Management, Universidad Popular Autonóma del Estado de Puebla, Puebla, Puebla, Mexico.
Peerj. Computer Science
|April 5, 2021
Summary
This study introduces a coded suite of models to aid logistics professionals and researchers in solving complex supply chain problems. It offers tools for problem generation, distance calculation, inventory simulation, data visualization, and meta-heuristic solutions for routing and facility location.
Area of Science:
- Operations Research
- Supply Chain Management
- Computational Logistics
Background:
- Logistics is crucial for efficient supply chain operations, requiring specialized skills and tools.
- Traditional methods for solving complex logistics problems demand significant mathematical and computational expertise.
- Existing tools may lack comprehensive functionalities for practical application and research.
Purpose of the Study:
- To develop a coded suite of models for exploring and solving key logistics problems.
- To provide accessible tools for logistics professionals, students, and researchers.
- To enhance the practical application of operations research techniques in logistics.
Main Methods:
- Development of a software suite with functions for test instance generation (routing, facility location).
- Implementation of various distance metric computations (Euclidean, Manhattan, geographical).
- Simulation of non-deterministic inventory control models and integration of a nearest-neighbor meta-heuristic.
Main Results:
- The coded suite facilitates the generation of realistic test instances with geographical coordinates.
- It enables accurate distance calculations and simulation of inventory systems.
- A nearest-neighbor meta-heuristic is designed to yield effective solutions for large-scale vehicle routing and facility location problems.
Conclusions:
- The developed coded suite offers a valuable resource for addressing complex logistics challenges.
- It bridges the gap between theoretical models and practical implementation in supply chain management.
- The integrated approach supports data analysis, visualization, and the optimization of logistic operations.
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