Abstract
This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups; the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3].
Highlights
Prudent production scheduling has long been a means for enhancing the competitive position of the manufacturing firm
The second category is the search heuristic associated with the use of transition, where the selection probability is governed by Equation (9b), and the transition matrix is updated via Equation (11)
As the five problem sets and their results are inspected, it becomes clear that the methodology presented with this research consistently outperforms the simulated annealing methodology from an earlier research effort [2], essentially validating the results shown in Tables 4 and 5
Summary
Prudent production scheduling has long been a means for enhancing the competitive position of the manufacturing firm. Effective scheduling can be used to enhance flexibility in a Just-in-time (JIT) system, as well as reduce the required number of setups associated with changeovers. A strong performance of one suggests a poor performance of the other Another caveat related to production sequencing relates to the fact that there are frequently several individual processes involved in the aggregate production function. It may be possible to “re-sequence” the production sequence used in the prior stage of production. This re-sequencing may, if properly done, induce some efficiencies that were not present before re-sequencing. Via development of a detailed example detailing the concept of limited re-sequencing and capturing the efficient frontier, the sequencing methodology is presented, an experiment is described, analyses of performance are made, and general observations are offered
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