Set-up planning is used to determine the set-up of a workpiece with a certain orientation and ®xturing on a worktable, as well as the number and sequence of set-ups and operations performed in each set-up. This paper presents a concurrent constraint planning methodology and a hybrid genetic algorithm (GA) and simulated annealing (SA) approach for set-up planning, and re-set-up planning in a dynamic workshop environment. The proposed approach and optimization methodology analyses the precedence relationships among features to generate a precedence relationship matrix (PRM). Based on the PRM and inquiry results from a dynamic workshop resource database, the hybrid GA and SA approach, which adopts the feature-based representation, optimizes the set-up plan using six cost indices. The PRM acts as the main constraints for the set-up planning optimization. Case studies show that the hybrid GA and SA approach is able to generate optimal results as well as carry out re-set-up planning on the occurrence of workshop resource changes.
IntroductionSet-up planning is concerned with identifying the set-ups to be used when machining a part, determining the number and sequence of set-ups, as well as the number of operations performed in each set-up. The number of set-ups and the sequence of set-ups depends on many parameters such as the available manufacturing resources (tools, ®xtures and machines), optimization objectives such as minimum number of set-ups (minimum changeover time), minimum costs, and maximum achievable accuracy (reduction of overhangs, instability, vibration), etc. With the development of new manufacturing philosophies such as Concurrent Engineering, the requirement to increase competitiveness is challenging the manufacturing industry, as well as the Computer-Aided Set-up Planning (CASP) research. Increasing thē exibility of CASP is necessary in order to react to changes in the internal and external environmental conditions, and provide more manufacturing information to other manufacturin g activities. This means that a CASP system should have the ability to generate¯exible (alternate) set-up plans with di erent operation sequences and assign di erent manufacturin g resources to each operation. When alternate setup plans emerge, the question of optimization arises, which motivates the research described in this research. This paper reports on the research on the set-up planning process with dynamic resource constraints while achieving certain optimization objectives. A dynamic manufacturing environment is considered instead of the usual static environment where all resources are assumed to be available and can be used equally with no extra
The minimum weight vertex cover problem (MWVCP) is one of the most popular combinatorial optimization problems with various real-world applications. Given an undirected graph where each vertex is weighted, the MWVCP is to find a subset of the vertices which cover all edges of the graph and has a minimum total weight of these vertices. In this paper, we propose a multi-start iterated tabu search algorithm (MS-ITS) to tackle MWVCP. By incorporating an effective tabu search method, MS-ITS exhibits several distinguishing features, including a novel neighborhood construction procedure and a fast evaluation strategy. Extensive experiments on the set of public benchmark instances show that the proposed heuristic is very competitive with the state-of-the-art algorithms in the literature.
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