Most existing activity-based travel demand models are implemented in a tour-based microsimulation framework. Due to the significant computational and data storage benefits, the demand microsimulation allows a greater amount of flexibility in terms of demographic market segmentation, temporal scale, and spatial resolution, and thus the models can represent a wider range of travel behavior aspects associated with various policies and scenarios. This dissertation proposes three innovative methodologies, one for each of the three key dimensions, to fulfill the greater level of details toward a more mature state of activity-based travel demand models.

The optimal allocation of buffers in serial production systems is one of the oldest and most researched problems in Industrial Engineering. In general, there are three main approaches to the buffer allocation problem when the objective is to maximize throughput. The first is basically a systematic trial and error procedure supported either by discrete event simulation or analytical models. A second approach is to allocate buffers based on general design rules that have been established in the research literature through experimentation. And the third approach is to apply a buffer allocation optimization algorithm to a specific production line. All these approaches have limitations and could be time and resource consuming. Additionally, most of the existing research on buffer allocation only considers production systems modeled with an infinite supply of raw materials before the first workstation and an unlimited capacity for finished goods after the last workstation. In reality many production systems are designed as closed systems where an interaction between the last and the first workstations in the line is present. In a closed production system, there is a finite buffer after the last workstation and the number of "carriers" holding jobs that move through the line is fixed. The objective of this thesis was to develop efficient heuristic algorithms for the buffer allocation problem in closed production systems. Two heuristics for buffer allocation were implemented. Heuristic H 1 uses the idea that highly utilized workstation stages require any available buffer more than sub-utilized stages. Heuristic H2 uses information stored in the longest path of a network representation of job flow to determine where additional buffers are most beneficial. An experiment was designed to determine if there are any statistically significant differences between throughput values with buffer allocations obtained with a genetic algorithm, also developed in this research, and through puts with buffer allocations generated by Hi and H2. Several types of closed production systems were examined in eight different test cases. No significant differences in performance were observed. The efficiency of the heuristics was also analyzed. A significant difference between the speeds of Hi and H2 is found. The analysis performed in this research indicates that heuristic H2 is sufficiently effective and accurate for determining near optimal buffer allocations in closed production systems. / Graduation date: 2005

Over the last two decades, the assembly of printed circuit boards (PCB) has generated a huge amount of industrial activity. One of the major developments in PCB assembly was introduction of surface mount technology (SMT). SMT has displaced through-hole technology as a primary means of assembling PCB over the last decade. It has also made it easy to automate PCB assembly process. The component placement machine is probably the most important piece of manufacturing equipment on a surface mount assembly line. It is used for placing components reliably and accurately enough to meet the throughput requirements in a cost-effective manner. Apart from the fact that it is the most expensive equipment on the PCB manufacturing line, it is also often the bottleneck. There are a quite a few areas for improvements on the machine, one of them being component placement sequencing. With the number of components being placed on a PCB ranging in hundreds, a placement sequence which requires near minimum motion of the placement head can help optimize the throughput rates. This research develops an application using genetic algorithm (GA) to solve the component placement sequencing problem for a single headed placement machine. Six different methods were employed. The effects of two parameters which are critical to the execution of a GA were explored at different levels. The results obtained show that the one of the methods performs significantly better than the others. Also, the application developed in this research can be modified in accordance to the problems or machines seen in the industry to optimize the throughput rates. / Graduation date: 2004

For some decades, the assembly of printed circuit boards (PCB), had been thought to be an ordinary example of mass production systems. However, technological factors and competitive pressures have currently forced PCB manufacturers to deal with a very high mix, low volume production environment. In such an environment, setup changes happen very often, accounting for a large part of the production time. PCB assembly machines have a fixed number of component feeders which supply the components to be mounted. They can usually hold all the components for a specific board type in their feeder carrier but not for all board types in the production sequence. Therefore, the differences between boards in the sequence determines the number of component feeders which have to be replaced when changing board types. Consequently, for each PCB assembly line, production control of this process deals with two dominant problems: the determination for each manufacturing line of a mix resulting in larger similarity of boards and of a board sequence resulting in setup reduction. This has long been a difficult problem since as the number of boards and lines increase, the number of potential solutions increases exponentially. This research develops an approach for applying Genetic Algorithms (GA) to this problem. A mathematical model and a solution algorithm were developed for effectively determining the near-best set of printed circuit boards to be assigned to surface mount lines. The problem was formulated as a Linear Integer Programming model attempting to setup reduction and increase of machine utilization while considering manufacturing constraints. Three GA based heuristics were developed in order to search for a near optimal solution for the model. The effects of several crucial factors of GA on the performance of each heuristic for the problem were explored. The algorithm was then tested on two different problem structures, one with a known optimal solution and one with a real problem encountered in the industry. The results obtained show that the algorithm could be used by the industry to reduce setups and increase machine utilization in PCB assembly lines. / Graduation date: 1998

Process planning and scheduling are two important manufacturing planning functions which are traditionally performed separately and sequentially. Usually, the process plan has to be prepared first before scheduling can be performed. However, due to the complexity of manufacturing systems and the uncertainties and dynamical changes encountered in practical production, process plans and schedules may easily become inefficient or even infeasible. The concept of integrated process planning and scheduling (IPPS) has been proposed to improve the efficiency, effectiveness as well as flexibility of the respective process plan and schedule. By combining both functions together, the process plan for producing a part could be dynamically arranged in accordance with the availability of manufacturing resources and current status of the system, and its operations’ schedule could be determined concurrently. Therefore, IPPS could provide an essential solution to the dynamic process planning and scheduling problem in the practical manufacturing environment. Nevertheless, process planning and scheduling are both complex functions that depend on many factors and flexibilities in the manufacturing system, IPPS is therefore a highly complex NP-hard problem. Ant colony optimization (ACO) is a widely applied meta-heuristics, which has been proved capable of generating feasible solutions for IPPS problem in previous research. However, due to the nature of the ACO algorithm, the performance is not that favourable compared with other heuristics. This thesis presents an enhanced ACO approach for IPPS. The weaknesses and limitations of standard ACO algorithm are identified and corresponding modifications are proposed to deal with the drawbacks and improve the performance of the algorithm. The mechanism is implemented on a specifically designed multi-agent system (MAS) framework in which ants are assigned as software agents to generate solutions. First of all, the manufacturing processes of the parts are graphically formulated as a disjunctive AND/OR graph. In applying the ACO algorithm, ants are deployed to find a path on the disjunctive graph. Such an ant route indicates a corresponding solution with associated operations scheduled by the sequence of ant visit. The ACO in this thesis is enhanced with the novel node selection heuristic and pheromone update strategy. With the node selection heuristic, pheromone is deposited on the nodes as well as edges on the ant path. This is contrast to the conventional ACO algorithm that pheromone is only deposited on edges. In addition, a more reasonable strategy based on “earliest completion time” of operations are used to determine the heuristic desirability of ants, instead of the “greedy” strategy used in standard ACO, which is based on the “shortest processing time”. The approach is evaluated by a comprehensive set of problems with a full set of flexibilities, while multiple performance measurements are considered, including makespan, mean flow time, average machine utilization and CPU time, among which makespan is the major criterion. The results are compared with other approaches and encouraging improvements on solution quality could be observed. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

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published_or_final_version / Mathematics / Master / Master of Philosophy

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Bibliography: leaves B. 1-B. 23 / 2 v. : ill., map ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Economics, 1984