Using the Deterministic EOQ Formula in Stochastic Inventory Control

Axsäter, Sven

doi:10.1287/mnsc.42.6.830

Cited by 109 publications

(63 citation statements)

References 4 publications

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“…Zheng (1992) has shown that the maximum relative error is bounded by 0.125. Axsater (1996) shows a slightly better bound of 0.118. Numerical experiments of practical situations show that the performance of this heuristic is usually much better than the worst-case bound.…”

Section: A Joint Location-inventory Modelmentioning

confidence: 96%

“…The retailers that are selected to operate as distribution centers (DCs) order inventory from the plant using an economic order quantity model (EOQ), which is an approximation to the Q r model with Type I Service constraint (Hopp andSpearman 1996, Nahmias 1997). Axsater (1996) points out that for Q r model, although it is, in general, relatively easy to derive the optimal solution, it is common in practice to use an approximate solution that is obtained in two steps. First the stochastic demand is replaced by its mean and the order quantity Q is determined by the deterministic EOQ formula.…”

Section: The Location Allocation Risk-pooling Modelmentioning

confidence: 99%

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A Joint Location-Inventory Model

Shen

Coullard²,

Daskin³

2003

Transportation Science

571

403

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W e consider a joint location-inventory problem involving a single supplier and multiple retailers. Associated with each retailer is some variable demand. Due to this variability, some amount of safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers (and therefore inventory storage locations) for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. We formulate this problem as a nonlinear integer-programming model. We then restructure this model into a set-covering integer-programming model. The pricing problem that must be solved as part of the column generation algorithm for the set-covering model involves a nonlinear term in the retailerdistribution-center allocation terms. We show that this pricing problem can (theoretically) be solved efficiently, in general, and we show how to solve it practically in two important cases. We present computational results on several instances of sizes ranging from 33 to 150 retailers. In all cases, the lower bound from the linear-programming relaxation to the set-covering model gives the optimal solution.

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Section: A Joint Location-inventory Modelmentioning

confidence: 96%

Section: The Location Allocation Risk-pooling Modelmentioning

confidence: 99%

A Joint Location-Inventory Model

Shen

Coullard²,

Daskin³

2003

Transportation Science

571

403

View full text Add to dashboard Cite

show abstract

“…For example, interest has been devoted to analysing the size of errors incurred when replacing stochastic demand by its expected value in the model, e.g. (Zheng, 1992) and Axsäter (1996). Demand may be sequences of discrete demand events with variable size and in-between time intervals.…”

Section: Preface To the State Of The Artmentioning

confidence: 99%

“…This is probably because of two reasons. First, the total inventory cost has a very low sensitivity to inventory cost parameters (Axsäter, 1996). A second reason is the complexity of algebraic operations among random parameters with a probability distribution.…”

Section: Stochastic Modelsmentioning

confidence: 99%

A century of evolution from Harris׳s basic lot size model: Survey and research agenda

Andriolo

Battini

Grubbström

et al. 2014

International Journal of Production Economics

170

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Determining the economic lot size has always represented one of the most important issues in production planning. This problem has long attracted the attention of researchers, and several models have been developed to meet requirements at minimum cost. In this paper we explore and discuss the evolution of these models during one hundred years of history, starting from the basic model developed by Harris in 1913, up to today. Following Harris's work, a number of researchers have devised extensions that incorporate additional considerations. The evolution of EOQ theory strongly reflects the development of industrial systems over the past century. Here we outline all the research areas faced in the past by conducting a holistic analysis of 219 selected journal papers and trying to give a comprehensive view of past work on the EOQ problem. Finally, a new research agenda is proposed and discussed.

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“…Zheng (1992) demonstrates that the maximum relative error bound is 12.5%. Furthermore, Axsäter (1996) states that the increase is no more than 11.80%. Considering the cost and time required to develop inventory policies with more complex methodologies and software, we found that it is perfectly justified to take advantage of the simplicity of the deterministic EOQ formula even in stochastic situations.…”

Section: Deterministic Inventory Systemsmentioning

confidence: 99%