Evolutionary computation applied to combinatorial optimisation problems

This thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem. Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. We evaluate the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches is made in two forms: firstly a handcrafted approach compares GAs without repair against those using GeneRepair. A second automated approach is then presented. This meta-genetic algorithm examines different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search. Results have identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions for 50, 70 and 100 city problems. GeneRepair assists in finding TSP solutions in an extremely efficient manner, in both time and number of evaluations required

Hybrid metaheuristics for solving multi-depot pickup and delivery problems

In today's logistics businesses, increasing petrol prices, fierce competition, dynamic business environments and volume volatility put pressure on logistics service providers (LSPs) or third party logistics providers (3PLs) to be efficient, differentiated, adaptive, and horizontally collaborative in order to survive and remain competitive. In this climate, efficient computerised-decision support tools play an essential role. Especially, for freight transportation, e efficiently solving a Pickup and Delivery Problem (PDP) and its variants by an optimisation engine is the core capability required in making operational planning and decisions. For PDPs, it is required to determine minimum-cost routes to serve a number of requests, each associated with paired pickup and delivery points. A robust solution method for solving PDPs is crucial to the success of implementing decision support tools, which are integrated with Geographic Information System (GIS) and Fleet Telematics so that the flexibility, agility, visibility and transparency are fulfilled. If these tools are effectively implemented, competitive advantage can be gained in the area of cost leadership and service differentiation. In this research, variants of PDPs, which multiple depots or providers are considered, are investigated. These are so called Multi-depot Pickup and Delivery Problems (MDPDPs). To increase geographical coverage, continue growth and encourage horizontal collaboration, efficiently solving the MDPDPs is vital to operational planning and its total costs. This research deals with designing optimisation algorithms for solving a variety of real-world applications. Mixed Integer Linear Programming (MILP) formulations of the MDPDPs are presented. Due to being NP-hard, the computational time for solving by exact methods becomes prohibitive. Several metaheuristics and hybrid metaheuristics are investigated in this thesis. The extensive computational experiments are carried out to demonstrate their speed, preciseness and robustness.Open Acces