Design and analysis of algorithms for solving some stochastic vehicle routing and scheduling problems

Ph.DDOCTOR OF PHILOSOPH

Integration of indifference-zone with multi-objective computing budget allocation

In this paper, we consider how to address the issues of having designs with close performance in the multi-objective ranking and selection (MORS) problem. To resolve this issue we propose integrating the indifference-zone (IZ) concept into the multi-objective computing budget allocation (MOCBA) framework. In particular, when IZ is introduced into the MOCBA framework, we address how to determine the probability of non-dominance, how to define the Pareto set, and how to derive allocation rules for the simulation replications. Empirical results show that the MOCBA framework with IZ can significantly save simulation budget when designs to be compared have close performance.Indifference-zone Multi-objective Computing budget allocation Lagrange relaxation

Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem

Simulation optimization has received considerable attention from both simulation researchers and practitioners. In this study, we develop a solution framework which integrates multi-objective evolutionary algorithm (MOEA) with multi-objective computing budget allocation (MOCBA) method for the multi-objective simulation optimization problem. We apply it on a multi-objective aircraft spare parts allocation problem to find a set of non-dominated solutions. The problem has three features: huge search space, multi-objective, and high variability. To address these difficulties, the solution framework employs simulation to estimate the performance, MOEA to search for the more promising designs, and MOCBA algorithm to identify the non-dominated designs and efficiently allocate the simulation budget. Some computational experiments are carried out to test the effectiveness and performance of the proposed solution framework.