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We study the problem of allocating stocked fibers to made-to-order cables with the goals of satisfying due dates and reducing the costs of scrap, setup, and fiber circulation. These goals are achieved by generating remnant fibers either long enough to satisfy future orders or short enough to scrap with little waste. They are also achieved by manufacturing concatenations, in which multiple cable orders are satisfied by the production of a single cable that is afterwards cut into the constituent cables ordered. We use a function that values fibers according to length, and which can be viewed as an approximation to the optimal value function of an underlying dynamic programming problem. The daily policy that arises under this approximation is an integer program with a simple linear objective function that uses changes in fiber value to take into account the multi-period consequences of decisions. We describe our successful implementation of this integer program in the factory, summarizing our computational experience as well as realized operational improvements.Cable Manufacturing, Resource Pricing and Allocation, Remnant Inventory System, Discrete Optimization, Dynamic Programming Approximations

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Purpose -The efficiency of assembly lines is a critical factor for the competitiveness of industries in the global market. The purpose of this paper is to present a line balancing methodology consisting of the combination of a heuristic model and an exact algorithm with intelligent task location or line zone constraints. The objective is to find a minimum cost solution in a feasible computational time with a realistic cost function considering short-term operation costs, task-related and workstation capital investment costs, and workstation paralleling. Design/methodology/approach -The methodology is evaluated using different problem sizes, zone sizes and cycle time scenarios. The quality of results is measured by the closeness to the global optimum and the computational time requirements. Findings -The proposed methodology is found to be highly effective with an average percentage difference of 20.63 percent from the optimum solution. In the experimentation, results are compared against the global optimum in 24 of the 36 scenarios tested. In 23 of the 24 (95.8 percent) results, the largest percentage difference is 0.55 percent. In eight of the 12 cases in which the global optimum is not found, the algorithm with zone constraints provided a better solution than the upper bound available when the simulation model pursuing the optimum is stopped. These unfinished runs are stopped after a minimum run time of 24 hours. Originality/value -The originality of the methodology is on the strategy used to consider workstation paralleling with task-related capital investment costs. It is the only one with an exact algorithm considering task-related capital investment costs in combination with workstation paralleling.