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Adaptive Memory Procedure for Solving Real-world Vehicle Routing Problem

Nikica Peric, Slaven Begovic, Vinko Lesic

TL;DR

A new procedure based on adaptive memory metaheuristic combined with local search is proposed based on adaptive memory metaheuristic combined with local search for real-world vehicle routing problem with large number of constraints.

Abstract

Logistics and transport are core of many industrial and business processes. One of the most promising segments in the field is optimisation of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environment and cover a fraction of real industrial application due to complex combinatorial algorithms required to be promptly executed. In this paper, a real-world case study in all its complexity is observed and formulated as a real-world vehicle routing problem (VRP). To be able to computationally cope with the complexity, we propose a new procedure based on adaptive memory metaheuristic combined with local search. The initial solution is obtained with Clarke-Wright algorithm extended here by introducing a dropout factor to include a required stochastic attribute. The procedure and corresponding algorithms are tested on the existing benchmarks and further on the real industrial case study, which considers capacities, time windows, soft time windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery, crew skills, split delivery and, finally, time-dependent routes as the most significant factor. In comparison with the current state-of-the-art algorithms for vehicle routing problem with a large number of constraints, we obtain an average savings of 2.03% in delivery time and 20.98% in total delivery costs.

Adaptive Memory Procedure for Solving Real-world Vehicle Routing Problem

TL;DR

A new procedure based on adaptive memory metaheuristic combined with local search is proposed based on adaptive memory metaheuristic combined with local search for real-world vehicle routing problem with large number of constraints.

Abstract

Logistics and transport are core of many industrial and business processes. One of the most promising segments in the field is optimisation of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environment and cover a fraction of real industrial application due to complex combinatorial algorithms required to be promptly executed. In this paper, a real-world case study in all its complexity is observed and formulated as a real-world vehicle routing problem (VRP). To be able to computationally cope with the complexity, we propose a new procedure based on adaptive memory metaheuristic combined with local search. The initial solution is obtained with Clarke-Wright algorithm extended here by introducing a dropout factor to include a required stochastic attribute. The procedure and corresponding algorithms are tested on the existing benchmarks and further on the real industrial case study, which considers capacities, time windows, soft time windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery, crew skills, split delivery and, finally, time-dependent routes as the most significant factor. In comparison with the current state-of-the-art algorithms for vehicle routing problem with a large number of constraints, we obtain an average savings of 2.03% in delivery time and 20.98% in total delivery costs.
Paper Structure (13 sections, 15 equations, 6 figures, 6 tables, 3 algorithms)

This paper contains 13 sections, 15 equations, 6 figures, 6 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Mathematical Formulation
  3. Algorithm design
  4. Initial solution generation
  5. Local search
  6. Adaptive memory
  7. Benchmarks
  8. Standardized benchmark
  9. Real world benchmark
  10. Results
  11. Results on the standardized benchmark
  12. Results on the real world benchmark
  13. Conclusion

Figures (6)

  • Figure 1: Comparison of CVRP solutions using extended Clarke-Wright algorithm with different $\lambda$ values
  • Figure 2: Average cost and solution diversity of the initial solution depending on the route shape parameter $\lambda$ and the dropout parameter $p_d$
  • Figure 3: Section of the CVRP solution after using the RCCW algorithm and local search without dropout and with dropout
  • Figure 4: Swap vertices and move vertices example
  • Figure 5: Average cost and solution diversity of the solution after local search depending on the route shape parameter $\lambda$ and the dropout parameter $p_d$
  • ...and 1 more figures