| dc.description.abstract | The pharmaceutical industry faces unique challenges in the management of medicines
and dietary supplements due to their short product lifecycles, small size, lightweight,
and requirement for precise temperature and humidity control during transit.
Additionally, the widespread use of cutting-edge technology has changed how
consumers engage with pharmacies, driving a requirement for effective information
collecting and communication. To address these complexities, logistics companies
operating in the pharmaceutical sector often utilize various types of vehicles with
different carrying capacities. When maximizing their service delivery, these businesses
must carefully consider both fixed and variable expenses, making vehicle assignment
and route optimization difficult. This paper focuses on solving the complex problem of
multi-type vehicles and mixed-integer programming route optimization while adhering
to distribution time and carrying capacity constraints. The study proposes the use of a
hybrid particle swarm intelligence (PSI) heuristic approach, which incorporates
crossover and mutation operators from genetic algorithms and integrates a 2-opt local
search strategy. This approach is applied to the case of An Khang Pharmacy, a
prominent pharmacy retailer in Ho Chi Minh City. This study evaluates the efficiency
of the suggested algorithm through a thorough examination utilizing An Khang
Pharmacy as a case study. The findings of this study offer useful information for
improving transportation logistics in the pharmaceutical sector. The use of this
algorithm may enhance order fulfillment, cost-effectiveness, and general customer
pleasure, which will eventually be advantageous to pharmaceutical businesses and
their clients | en_US |
