Distribution planning with slective traveling salesman and vehicle routing problems
Abstract
This dissertation introduces a new class of selective traveling salesman problem (TSP) and
vehicle routing problem (VRP) in which the goal is to maximize the served demand. Using solution
frameworks developed for these selective TSPs and VRPs, the decision-maker has the opportunity
to implement efficient planning tools to schedule vehicle tours with respect to the budget and time
constraints.
In this dissertation, mixed-integer linear mathematical models are developed and solved
for each problem, where the goal is maximization of served demand. The problems considered in
this dissertation are the following: traveling salesman problem with partial coverage (TSPWPC),
capacitated vehicle routing problem with partial coverage (CVRPWPC), multiple traveling
salesman problem with partial coverage (MTSPWPC), and multiple depot capacitated vehicle
routing problem with partial coverage (MDCVRPWPC).
The proposed models were solved using exact solution methods and metaheuristic
methods such as hybrid genetic algorithms. The proposed hybrid genetic algorithms (HGAs)
consider the polar angle of the nodes and incorporate clustering algorithms to determine and
improve the solutions, which can be obtained by regular genetic algorithms (GAs). In the proposed
selective multiple vehicle mathematical models, we also utilize sweep and assignment methods to
generate structured solutions.
This dissertation also presents detailed numerical experimentation, as well as
computational results to assess the performance of the proposed algorithms.
Description
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering