병렬분해 환경 하에 작업순서 종속 준비비용을 고려하는 선택적 분해순서 결정에 대한 확정적/확률적 모형

Abstract

This dissertation focuses on the selective disassembly sequencing problem with sequence-dependent setups in the parallel disassembly that removes one or more components at the same time by a single disassembly operation. The problem is to determine the sequence of disassembly operations to extract multiple target components while satisfying the precedence relations among disassembly operations. In this dissertation, we consider one deterministic model and two stochastic models for the selective disassembly sequencing problem. In Chapter 2, we suggest the deterministic model for the selective disassembly sequencing problem. The problem is to determine the sequence of disassembly operations to extract multiple target components while satisfying the precedence relations among disassembly operations. The objective is to minimize the sum of sequence-dependent setup and operation costs. An integer programming model is developed after representing all possible disassembly sequences using an extended process graph, and then an optimal branch and bound algorithm is proposed that incorporates the methods to obtain the lower and upper bounds as well as a dominance property to reduce the search space. To show the performance of the algorithm, computational experiments are done on various random instances, and the results are reported. In particular, it is shown from the test results that the proposed algorithm requires much shorter computation times than a competitive commercial software package. Finally, a case is reported to illustrate the extended process graph and the solution algorithm. In Chapter 3, we propose two stochastic models for the selective disassembly sequencing problem. The first one is the problem that considers random operation times in the parallel disassembly environment in which one or more components can be removed at the same time by a single disassembly operation. An extension of the first one, the second one is the problem that considers irregular disassembly operations and random operation times. Here, the irregular operations are defined as those in which fasteners can be removed by additional destructive operations without damaging to the target components. After representing all possible sequences using the extended process graph, two stochastic integer programming models are developed that minimizes the sum of disassembly and penalty costs, where the disassembly cost consists of sequence-dependent setup and operation costs and the penalty cost is the expectation of the costs incurred when the total disassembly time exceeds a given threshold value. A sample average approximation algorithm is proposed that incorporates an optimal algorithm to solve the sample average approximating problem under a given set of scenarios for each problem. The algorithm is illustrated with a hand-light case and a large sized random instance, and the results are reported.