Approximate Combinatorial Optimization Models for Large-Scale Production Lot Sizing and Scheduling with Sequence-Dependent Setup Times

This paper develops three mixed integer programming (MIP) models and solution methods to assist in identifying a capacity feasible master production schedule (MPS) in Material Requirements Planning (MRP) systems. The initial exact model takes into account sequence-dependent setup times of both end-items and components, but is optimally solvable only for small product structures. A first approximate model and solution method, to be used with larger product structures, suboptimally schedules setups and lots on a period-by-period basis, estimating the capacity usage of future setups through the use of linear rather than integer variables. A second model and method, developed from the first, greatly accelerates computing time by sequencing setups gradually within each period, but again suboptimally. The trade-offs between schedule quality and computing time are analyzed in computational tests. The second model is able to schedule setups of up to 100 products on 10 machines over 5 periods in reasonable computing time. The tests show that this complex production scheduling problem can be practicably and successfully simplified both in terms of modelling and of solution method.