Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Xiaodan Wu, Ruichang Li, Chao Hsien Chu, Richard Amoasi, Shan Liu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

Original languageEnglish (US)
Pages (from-to)653-684
Number of pages32
JournalAnnals of Operations Research
Issue number1-2
StatePublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Management Science and Operations Research


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