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Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

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Abstract

The workforce scheduling and routing problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better GA performance. This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator’s effectiveness. Best operators are given high application rates and low rates are given to the worse. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators were better than using only one operator used in isolation. This study provided an important opportunity to advance the understanding of the best method to use crossover operators for this highly-constrained optimisation problem effectively.

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Notes

  1. The weights used here are available as “Evaluation Tool” (blue setting) at https://drive.google.com/drive/folders/0BzWzQ97MNBVZQXhicTUxRTNRWGc

  2. The instances data used here (A, B, C, D, E and F) are available as “Webroster set” at https://drive.google.com/drive/folders/0B2OtHr1VocuSMjAzd3FkWks2bDQ.

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Authors and Affiliations

  1. Collage of Computers and Information Technology, Taif University, Ta’if, Saudi Arabia

    Haneen Algethami

  2. Estadstica y Investigacin Operativa, Universidad de Valencia, Valencia, Spain

    Anna Martínez-Gavara

  3. ASAP Research Group, School of Computer Science, The University of Nottingham, Nottingham, UK

    Dario Landa-Silva

Authors
  1. Haneen Algethami
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  2. Anna Martínez-Gavara
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  3. Dario Landa-Silva
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Correspondence to Haneen Algethami.

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Algethami, H., Martínez-Gavara, A. & Landa-Silva, D. Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem. J Heuristics 25, 753–792 (2019). https://doi.org/10.1007/s10732-018-9385-x

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  • DOI: https://doi.org/10.1007/s10732-018-9385-x

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