The Multiregion Dynamic Capacity Expansion Problem: An Improved Heuristic

We consider the problem of determining a schedule of capacity expansions for m producing regions and a schedule of shipments from the regions to n markets so as to meet arbitrary market demands over a T-period planning horizon at minimum discounted capacity expansion and shipment costs. The capacity expansion cost functions can be any arbitrary nonnegative functions but the shipment (and production) costs are assumed to be proportional to the amounts shipped. The cost functions are allowed to be nonstationary and the possibility of imports is considered. The proposed heuristic algorithm improves on feasible solutions by simultaneous spatial-temporal moves of capacity expansions, i.e., several capacity expansions are simultaneously reassigned to different regions or time periods. A look ahead feature prevents the algorithm from becoming myopic and a self-learning feature dynamically updates the computational parameters. Computational results for both real and randomly generated problems show that the heuristic algorithm is computationally efficient and provides solutions which are closer to optimum than previous algorithms.