Available storage space frequently limits the aggregate stock level carried in multi-item inventory systems. In the deterministic case, the economic impact of storage capacity constraints may be reduced, and the utilization of the available facilities increased, by replenishment policies which include delivery schedules designed to avoid unnecessary clustering of order arrivals in the course of time. Under the assumption of a pure cycle reordering mode, simple expressions are derived for minimum storage space requirements and the associated optimal delivery schedules.
Numerous heuristics have been proposed in the past two decades for the dynamic lot sizing problem, many of them in APICS journals. Their relative performance is explored in extensive numerical tests measuring expected costs, risks of higher than expected costs and computer time consumed. The results indicate that users of pertinent standard software systems could benefit substantially from an incorporation of more recently proposed methods, specifically Groff's (1979) stop rule and a fathoming algorithm expanding it to a look‐ahead heuristic.
Aggregate Production Planning (APP) is concerned with a minimum cost adaptation of some production process to demand fluctuations, by means of controlling overall production rate (P) and workforce size (W). Disaggregation aims at allocating P and W to individual products such that an optimal sales program is obtained. A technique is developed which may be used for disaggregation when product contributions are nonlinear. It relies on decreasing marginal contributions as an evaluation criterion. In conjunction with an APP model, the technique can be used for a simultaneous derivation of aggregate and disaggregate decisions. This is illustrated by an application to a hypothetical production and sales planning problem.
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