Algorithms and Automated Material Handling Systems Design for Stacking 3D Irregular Stone Pieces

The motive of this research is to develop a good stacking method with an automatic material handling system and the procedures that can increase productivity, reduce production costs, and prevent labor injury. A diversity of products leads to a number of different kinds of stacking problems. Much research has been done focusing on two-dimensional arrangement for rectangles, circles or irregular shapes, and threedimensional regular-shaped objects such as rectangular boxes. To solve stacking problems, many algorithms such as the genetic algorithm, simulated annealing and other heuristic algorithms have been proposed. The three-dimensional stacking problem has a practical application in the transportation, manufacturing, and construction industries. There has been relatively little emphasis on three-dimensional irregular objects; however, stacking three-dimensional irregular objects has become more common in industry. In this thesis research, three heuristic algorithms are proposed to stack irregular stone pieces nested in a container with multiple layers. Primary functions of the heuristic algorithms include three major parts. First, it approximates irregular shapes to a cluster of straight lines. Secondly, it arranges the approximated angles one-by-one with the proposed step-by-step rule. Finally, it considers the weight of the stone pieces from the pixel calculation for reasons of stability. The first and second algorithms are based on the area and angle of the stone piece and the third one is based on the approximated weight of the stone. An automatic real-time stacking system including pneumatic devices, sensors, relays, a conveyor, a programmable logic controller, a robotic arm, and a vision system was developed for this study. The algorithms developed were tested by this automatic stacking system for better utilization. Three performance measures were presented in the experimental result. Comparisons between the results from three proposed algorithms and that from the bottom-back-left algorithm are made. Experimental data demonstrate that the utilizations and the stabilities of the three proposed algorithms are statistically better than that of the bottom-back-left algorithm. However, the cycle times of the three proposed algorithms have no statistical difference from that of the bottom-back-left algorithm. In addition, a statistical test between each proposed algorithm is also conducted. Both the utilizations and stabilities have statistical differences between each proposed algorithm while the cycle times do not. The results of this study show that the algorithm developed works effectively for solving the stone-pieces stacking problem

Evolutionary algorithms and hyper-heuristics for orthogonal packing problems

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area

Evolutionary algorithms and hyper-heuristics for orthogonal packing problems

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area

Genetic algorithms for multiple-choice problems

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems. It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success. Two multiple-choice problems are considered. The first is constructing a feasible nurse roster that considers as many requests as possible. In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income. Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems. However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework. Hence, the main theme of this work is to balance feasibility and cost of solutions. In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches. The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis. To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions. As well as a theoretical discussion as to the underlying reasons for using each operator, extensive computational experiments are carried out on a variety of data. These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality. The most successful variant of our algorithm has a more than 99% chance of finding a feasible solution which is either optimal or within a few percent of optimality