Abstract
In the recent years, theory of constraints (TOC) has emerged as an effective management philosophy for solving decision making problems with the aim of profit maximization by considering the bottleneck in traditional as well as modern manufacturing plants. One of the key components of TOC application is to enumerate quantity of the various products to be manufactured keeping in view the system constraints. Problem of this kind is termed as TOC product mix decision problem. It is a well-known computationally complex problem and thus warrants the application of heuristics techniques or AI based optimization tools to achieve optimal or near optimal solution in real time. In this research, a hybrid algorithm named tabu-simulated annealing is proposed. It exploits the beauty of tabu search and simulated annealing (SA) to ensure the convergence at faster rate. It is found that the performance of hybrid tabu-SA algorithm on a well known data set of product mix optimization problem is superior as compared to tabu search, SA, TOC heuristic, Revised-TOC (R-TOC) heuristic, and Integer Linear Programming (ILP) based approaches.
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