Abstract
Abstract A heuristic algorithm is presented for solving the scheduling of several items in parallel under capacity constraints with setup and carrying costs. The method is based upon finding a lower bound solution for these costs, securing the feasibility of the solution, and improving the feasible solution so obtained until no further improvements can be made. Comparison of the performance of the proposed heuristic algorithm to that of an exact mixed-integer programming model showed that best solution costs found by the heuristic deviated on an average by 1% from the optimal values, while the computing time was on an average 1/140 of that required by the exact method.
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