Rolling planning horizons: Error bounds for the dynamic lot size model
Mathematics of Operations Research
Existence of forecast horizons in undiscounted discrete-time lot size models
Operations Research
Error bound for the dynamic lot size model with backlogging
Annals of Operations Research
Economic lot sizing: an O(n log n) algorithm that runs in linear time in the Wagner-Whitin case
Operations Research - Supplement
Improved algorithms for economic lot size problems
Operations Research
Forecast horizons in the discounted dynamic lot size model
Management Science
Dynamic Programming: Models and Applications
Dynamic Programming: Models and Applications
Dynamic Version of the Economic Lot Size Model
Management Science
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In the dynamic lot size model, the production plan for the first few periods is usually determined based on the forecast data for some fixed data horizon. This fixed data horizon may not be long enough, in the sense that information beyond that data horizon may affect the optimal decisions in the first few periods. In the literature there are two approaches to deal with this insufficiency of information: to extend the data horizon or to find a worst-case bound on the error induced by imposing a finite data horizon on the model. This paper takes the second approach. When the second approach is taken, existing papers only evaluate the error bound after the production plan of the first few periods is determined. By contrast, this paper determines the production plan such that the corresponding error bound is minimal. We provide a polynomial time algorithm for the problem under the realistic assumption that a ''speculative motive'' cost structure is not allowed.