In order to handle the
vehicle scheduling problem caused by the emergence of random customer demands
in the logistics distribution process, this paper generated the emergence
probabilities, locations, and demands of dummy customers by employing the
aggregation and prediction methods with past random customer demand information
and experiences empirical data. Based on the analysis of the function of
customer dissatisfaction, the objective of minimization of weighting
generalized total delivery distribution cost was proposed with the
consideration of two factors of the economy of vehicle scheduling plan and
customers’ satisfaction. A vehicle scheduling model was established in
accordance with the principle of real customer first and dummy customer next.
The revised genetic algorithm with local search was designed. The numerical
example of Solomon Standard Test verifies the effectiveness and applicability
of the proposed model and algorithm. The results show that compared with other
existing methods, the vehicle scheduling plan proposed in this paper not only
effectively reduces the total delivery cost of logistics company, but also
quickly responses to customers demands, which improves the customers
satisfaction and the service level.
