Preventive maintenance has received considerable attention in industries and the literature. Conventional preventive maintenance models often assume that equipment is always available for maintenance activities. However, in many mission-critical industries, equipment may not be available for scheduled maintenance due to busy operational schedules. Forced shutdown of the equipment may incur extra costs that cannot be offset by the benefits from preventively maintaining the equipment. In this paper, we propose innovative preventive maintenance policies to address the challenges caused by equipment unavailability. Maintenance models with possible rescheduling are developed for both time-based and condition-based maintenance policies, and the objective is to minimize the long-run cost rate of all maintenance activities. The proposed policies, with consideration of equipment unavailability for prescheduled PM, are compared with the policies that ignore this unavailability. Numerical examples are provided to illustrate the proposed policies.

The Facility Layout Problem (FLP) in this paper is an extension of the traditional Quadratic Assignment Problems (QAP). While the objective is still to minimize the summed cost of the (flow ∗ distance), the facilities in the FLP have different given sizes and their locations must be determined on a continual planar site. Based on the visual facility layout design system proposed by Chiang [13], this paper presents a study on using Tabu Search (TS), Particle Swarm Optimization (PSO) and their combinations (TS+PSO and PSO+TS) to tackle the FLP. The computation results show that the two-stage algorithms are able to achieve better results in most cases than TS and PSO individually on the FLP. The proposed two-stage algorithms and visual layout design system provide an effective tool to solve the practical FLP.

This paper presents a massively parallel ant colony optimization - pattern search (ACO-PS) algorithm with graphics hardware acceleration on nonlinear function optimization problems. The objective of this study is to determine the effectiveness of using graphics processing units (GPU) as a hardware platform for ACO-PS. GPU, the common graphics hardware found in modern personal computers, can be used for data-parallel computing in a desktop setting. In this research, the classical ACO is adapted in the data-parallel GPU computing platform featuring `single instruction - multiple thread' (SIMT). The global optimal search of the ACO is enhanced by the classical local pattern search (PS) method. The hybrid ACO-PS method is implemented in a GPU + CPU hardware platform and compared to a similar implementation in a central processing unit (CPU) platform. Computational results indicate that GPU-accelerated SIMT-ACO-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ACO-PS with GPU acceleration.

This paper presents a particle swarm - pattern search optimization (PS2) algorithm with graphics hardware acceleration for bound constrained nonlinear optimization problems. The objective of this study is to determine the effectiveness of using graphics processing units (GPU) as a hardware platform for particle swarm optimization (PSO). GPU, the common graphics hardware which can be found in many personal computers, can be used for desktop data-parallel computing. The classical PSO is adapted in the data-parallel GPU computing platform featuring dasiasingle instruction - multiple threadpsila (SIMT). PSO is also enhanced by adding a local pattern search (PS) improvement. The hybrid PS2optimization method is implemented in the GPU environment and with a central processing unit (CPU) in a PC. Computational results indicate that GPU-accelerated SIMT-PS2method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid PS2with GPU acceleration.