Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

MULTILEVEL ANT COLONY OPTIMIZATION TO SOLVE CONSTRAINED FOREST TRANSPORTATION PLANNING PROBLEMS

In this dissertation, we focus on solving forest transportation planning related problems, including constraints that consider negative environmental impacts and multi-objective optimizations that provide forest managers and road planers alternatives for making informed decisions. Along this line of study, several multilevel techniques and mataheuristic algorithms have been developed and investigated. The forest transportation planning problem is a fixed-charge problem and known to be NP-hard. The general idea of utilizing multilevel approach is to solve the original problem of which the computational cost maybe prohibitive by using a set of increasingly smaller problems of which the computational cost is cheaper. The multilevel techniques are devised consisting of two parts. The first part is to recursively apply a graph coarsening heuristic to the original problem to produce a set of coarser level problems of which the sizes in terms of number of problem components such as edges and nodes are in decreasing order. The second part is to solve the set of the coarser level problems including the original problem bottom up, starting with the coarsest level. We propose that if coarser level problems inherit important properties (such as attribute value distribution) from their ancestor during the coarsening process, they can be treated as smaller versions of the original problem. Based on this hypothesis, the multilevel techniques use solutions obtained for the coarser level problems to solve the finer level problems. Mainly, we develop multilevel techniques to address three problems, namely a constrained fixed-charge problem, parameter configuration problem, and a multi-objective transportation optimization problem in this study. The performance of the multilevel techniques is compared with other commonly used approaches. The statistical analyses on the experimental results indicate that the multilevel approach can reduce computing time significantly without sacrificing the solution quality