Ensuring an efficient turning process by means of desirability index optimization for correlated quality criteria

The desirability index (DI) is a method for multi-criteria optimization accepted widely in industrial quality management. The DI integrates expert knowledge into the optimization process by setting up desirability functions (DFs) of the quality criteria regarding their objective regions and aggregating them into a single performance index. However, the independence assumption of DFs rarely holds true in real turning applications, and a number of studies have been conducted proving the existence of dependencies between tool wear, surface roughness, tool life and cutting forces. As a consequence, the optimal solution obtained might be biased towards the group of performance measures, which have a high level of association (positive correlations). In this thesis, modfications of DI for handling correlated multi-criteria optimization are developed. By integrating principal component analysis (PCA) into the optimization procedure, the correlations of DFs can be eliminated, and the overall performance index, PCA-based DI, is formulated as a strictly monotonically increasing transformation of DFs; thus, the optimality of solutions can be guaranteed through the research of Legrand [26]. Apart from the PCA-based procedure, the weight-adjustment method provides an attractive alternative approach which is simpler and more exible, by introducing the weight-adjustment cofficients into the original formulas of DIs. The proposed procedures are demonstrated by means of case studies of a turning process optimization, and the optimization results are benchmarked with the traditional DIs. It has been shown in results that optimizations should be also subjected to the correlation information of performance measures. In addition, the procedure for determining correlation is found to be the second important key for a successful optimization

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW