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Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment Problem.

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Summary
This summary is machine-generated.

This study presents an adaptive method for the complex Vehicle Routing Problem (VRP), optimizing urban logistics by considering traffic, customer needs, and costs. The novel approach effectively solves multi-objective VRP variations.

Keywords:
Tabu searchVRPcombinatorial optimizationcompromise programminggenetic algorithmlocal searchmetaheuristicsmulti objective optimization

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Area of Science:

  • Operations Research
  • Logistics and Supply Chain Management
  • Computational Optimization

Background:

  • The Vehicle Routing Problem (VRP) is a critical challenge in logistics, with numerous real-world applications.
  • Existing VRP models often struggle to incorporate practical constraints like terrain, traffic, and diverse stakeholder objectives.
  • Urban shipment systems require sophisticated solutions that balance multiple, often conflicting, goals.

Purpose of the Study:

  • To introduce an adaptive, multi-objective optimization method for a complex Vehicle Routing Problem (VRP).
  • To develop a model that integrates practical urban shipment requirements, including terrain, traffic, customer expectations, and cost.
  • To provide an effective decision-making tool for multi-objective VRP and its novel variations.

Main Methods:

  • Introduced an adaptive method combining multi-objective optimization with VRP variants for urban logistics.
  • Employed compromise programming to decompose the multi-objective problem into a minimized distance-based problem.
  • Designed a hybrid genetic algorithm integrated with a local search algorithm for problem-solving.

Main Results:

  • The proposed hybrid algorithm demonstrated effectiveness in solving the complex, multi-objective VRP.
  • Comparative analysis showed the adaptive method outperformed the original genetic algorithm and Tabu Search on the tested dataset.
  • The approach proved to be a valuable decision-making tool for practical urban shipment routing.

Conclusions:

  • The developed adaptive method is an effective solver for a new variation of the multi-objective Vehicle Routing Problem.
  • The integration of multi-objective optimization and practical constraints enhances the applicability of VRP solutions in urban logistics.
  • This research offers a robust framework for optimizing complex routing scenarios with diverse objectives.