Capacity Driven Due Date Settings in Make-to-Order Production Systems

Abstract

Due date setting is a difficult task for make-to-order companies and requires efficient capacity and lead time management. In the job-shop literature, production planning for make-to-order companies is tackled by using shop floor scheduling algorithms coupled with simulation. However, capacity management cannot be realized in the most detailed phase of planning and should be carried out at a higher decision level so that work loads are smoothed over time.

A linear capacity planning model is proposed for dealing with problems of load leveling in bottleneck departments over a planning horizon. In this model, end items in each order are conveniently grouped into product families so that the proposed model becomes tractable in terms of practical problem sizes. Capacity constraints are included in the model and covers a number of future periods with known firm orders. The objective is to minimize total backorder and overtime costs. In this context, an order is assumed to be backordered if it is delivered some weeks later than that of its arrival. Actually, the time during which an order is backordered represents the production lead time for that order. Furthermore, the production lead time of each order is constrained by an order specific maximum lead time determined as a company policy. The output of the model conveys weekly order release and delivery times which serve as order due dates. Hence, lead time management and due date setting as well as load leveling are achieved by the proposed model. Since the model does not take family set-up times into consideration, an iterative algorithm which balances over time, the additional capacity demanded by set-up times, is proposed.

The production plan developed for the bottleneck department is propagated to the other stages of the production system through a binary LP model whose binary variables are reduced at every stage. The proposed multi-stage planning system is applied for planning production in a manufacturing company which makes/assembles custom kitchen cupboards. The case study demonstrates the advantages of the system.