Lee, C.Y. (Chung-Yee)
http://repub.eur.nl/ppl/2143/
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RePub, Erasmus University RepositoryA dynamic lot-sizing model with demand time windows
http://repub.eur.nl/pub/14411/
Mon, 01 Oct 2001 00:00:01 GMT<div>Lee, C.Y.</div><div>Cetinkaya, S.</div><div>Wagelmans, A.P.M.</div>
One of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed, then the demand of a given period cannot be delivered earlier or later than the period. If backlogging is allowed, the demand of a given period cannot be delivered earlier than the period, but it can be delivered later at the expense of a backordering cost. Like most mathematical models, the classical dynamic lot-sizing model is a simplified paraphrase of what might actually happen in real life. In most real-life applications, the customer offers a grace period--we call it a demand time window--during which a particular demand can be satisfied with no penalty. That is, in association with each demand, the customer specifies an acceptable earliest and a latest delivery time. The time interval characterized by the earliest and latest delivery dates of a demand represents the corresponding time window. This paper studies the dynamic lot-sizing problem with demand time windows and provides polynomial time algorithms for computing its solution. If backlogging is not allowed, the complexity of the proposed algorithm is O(T²) where T is the length of the planning horizon. When backlogging is allowed, the complexity of the proposed algorithm is O(T³).A Dynamic Lot-Sizing Model with Demand Time Windows
http://repub.eur.nl/pub/7707/
Wed, 22 Dec 1999 00:00:01 GMT<div>Lee, C.Y.</div><div>Çetinkaya, S.</div><div>Wagelmans, A.P.M.</div>
One of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed then the demand of a given period cannot be delivered earlier or later than the period. If backlogging is allowed, the demand of a given period cannot be delivered earlier than the period, but can be delivered later at the expense of a backordering cost. Like most mathematical models, the classical dynamic lot-sizing model is a simplified paraphrase of what might actually happen in real life. In most real life applications, the customer offers a grace period - we call it a demand time window - during which a particular demand can be satisfied with no penalty. That is, in association with each demand, the customer specifies an earliest and a latest delivery time. The time interval characterized by the earliest and latest delivery dates of a demand represents the corresponding time window. This paper studies the dynamic lot-sizing problem with demand time windows and provides polynomial time algorithms for computing its solution. If shortages are not allowed, the complexity of the proposed algorithm is order T square. When backlogging is allowed, the complexity of the proposed algorithm is order T cube.A dynamic lot-sizing model with demand time windows
http://repub.eur.nl/pub/1620/
Wed, 08 Dec 1999 00:00:01 GMT<div>Lee, C.Y.</div><div>Cetinkaya, S.</div><div>Wagelmans, A.P.M.</div>
One of the basic assumptions of the classical dynamic lot-sizing model is that the aggregate demand of a given period must be satisfied in that period. Under this assumption, if backlogging is not allowed then the demand of a given period cannot be delivered earlier or later than the period. If backlogging is allowed, the demand of a given period cannot be delivered earlier than the period, but can be delivered later at the expense of a backordering cost.
Like most mathematical models, the classical dynamic lot-sizing model is a simplified paraphrase of what might actually happen in real life. In most real life applications, the customer offers a grace period - we call it a demand time window - during which a particular demand can be satisfied with no penalty. That is, in association with each demand, the customer specifies an earliest and a latest delivery time. The time interval characterized by the earliest and latest delivery dates of a demand represents the corresponding time window. This paper studies the dynamic lot-sizing problem with demand time windows and provides polynomial time algorithms for computing its solution. If shortages are not allowed, the complexity of the proposed algorithm is of the order T square. When backlogging is allowed, the complexity of the proposed algorithm is of the order T cube.