Heuristics approaches for three-dimensional strip packing and multiple carrier transportation plans

In transport logistic operations, an efficient delivery plan and better utilisation of vehicles will result in fuel cost savings, reduced working hours and even reduction of carbon dioxide emissions. This thesis proposes various algorithmic approaches to generate improved performance in automated vehicle load packing and route planning. First, modifications to best-fit heuristic methodologies are proposed and then incorporated into a simple but effective “look-ahead” heuristic procedure. The results obtained are very competitive and in some cases best-known results are found for different sets of constraints on three-dimensional strip packing problems. Secondly, a review and comparison of different clustering techniques in transport route planning is presented. This study shows that the algorithmic approach performs according to the specific type of real-world transport route planning scenario under consideration. This study helps to achieve a better understanding of how to conduct the automated generation of vehicle routes that meet the specific conditions required in the operations of a transport logistics company. Finally, a new approach to measuring the quality of transportation route plans is presented showing how this procedure has a positive effect on the quality of the generated route plans. In summary, this thesis proposes new tailored and effective heuristic methodologies that have been tested and incorporated into the real-world operations of a transport logistics company. The research work presented here is a modest yet significant advance to better understanding and solving the difficult problems of vehicle loading and routing in real-world scenarios

Choice function based hyper-heuristics for multi-objective optimization

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic

A genetic programming hyper-heuristic approach to automated packing

This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a heuristic, or sequence of heuristics, from a set pre-defined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation, robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains