PSO-based algorithm for home care worker scheduling in the UK

  • Authors:

  • Chananes Akjiratikarl;Pisal Yenradee;Paul R. Drake

  • Affiliations:

  • Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12121, Thailand;Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12121, Thailand;E-Business and Operations Management Division, University of Liverpool Management School, Liverpool L69 7ZH, UK

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2007

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Abstract

This paper presents the novel application of a collaborative population-based meta-heuristic technique called Particle Swarm Optimization (PSO) to the scheduling of home care workers. The technique is applied to a genuine situation arising in the UK, where the provision of community care service is a responsibility of the local authorities. Within this provision, optimization routes for each care worker are determined in order to minimize the distance traveled providing that the capacity and service time window constraints are not violated. The objectives of this paper are twofold; first to exploit a systematic approach to improve the existing schedule of home care workers, second to develop the methodology to enable the continuous PSO algorithm to be efficiently applied to this type of problem and all classes of similar problems. For this problem, a particle is defined as a multi-dimensional point in space which represents the corresponding care activities and assignment priority. The Heuristic Assignment scheme is specially designed to transform the continuous PSO algorithm to the discrete job schedule. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), i.e. insertion and swap, are embedded in the PSO algorithm in order to further improve the solution quality. The proposed methodology is implemented, tested, and compared with existing solutions on a variety of real problem instances.