Approximate solutions for a stochastic lot-sizing problem with partial customer-order information

Dellaert, Nico; Melo, Teresa

doi:10.1016/s0377-2217(02)00488-5

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…They model the problem via a discrete time make-to-stock queue. Dellaert and Melo [5] model partial ADI in a make-to-stock environment through a Markov decision process given the existence of customer priorities and when the supplylead-time is negligible. ADI in this case is the currently committed demand on some constant number of periods in the future (with the exception of next period's demand information being perfect); that is, it is possible to receive more orders in those periods but not less, making the minimum demand known for these periods.…”

Section: Introduction and Related Literaturementioning

confidence: 99%

Modelling imperfect advance demand information and analysis of optimal inventory policies

Tan

Güllü

Erkip

2007

European Journal of Operational Research

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We consider an inventory control problem where it is possible to collect some imperfect information on future demand. We refer to such information as imperfect Advance Demand Information (ADI), which may occur in different forms of applications. A simple example is a company that uses sales representatives to market its products, in which case the collection of sales representatives' information as to the number of customers interested in a product can generate an indication about the future sales of that product, hence it constitutes imperfect ADI. Other applications include internet retailing, Vendor Managed Inventory (VMI) applications and Collaborative Planning, Forecasting, and Replenishment (CPFR) environments. We develop a model that incorporates imperfect ADI with ordering decisions. Under our system settings, we show that the optimal policy is of order-up-to type, where the order level is a function of imperfect ADI. We also provide some characterizations of the optimal solution. We develop an expression for the expected cost benefits of imperfect ADI for the myopic problem. Our analytical and empirical findings reveal the conditions under which imperfect ADI is more valuable.

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Section: Introduction and Related Literaturementioning

confidence: 99%

Modelling imperfect advance demand information and analysis of optimal inventory policies

Tan

Güllü

Erkip

2007

European Journal of Operational Research

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“…They find that advance demand information is more effective in improving order fill rates than an equivalent reduction in component lead times. Dellaert and Melo [3] examine a single-product system in a make-to-stock environment with a single class of customers ordering in advance of their actual needs. Their problem is to determine the optimal size of a production lot so that they meet delivery requirements with minimal average costs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 5 (left) shows the results for one of our experiments in which we increase the SP demand from λ = 0 to λ = 8. In Figure 5 µ = 4, R = 9, C = 18, h = 1, and the OEM's order size is uniformly distributed in the range (3,13).…”

Section: Effects Of Sp and Oem Marketsmentioning

confidence: 99%

“…We considered uniform distributions for the OEM's order size, since it provides a finite number of values for q. Specifically, we considered cases where q follows uniform distribution with parameters (7,9), (5,11), (3,13), and (1,15). We also considered Bernoulli distribution in which the q can take the values of q 1 or q 2 , with probability p and 1 − p, respectively.…”

Section: Design Of Experiments For the Numerical Studymentioning

confidence: 99%

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A produce‐to‐stock system with advance demand information and secondary customers

Iravani

Liu

Luangkesorn

et al. 2007

Naval Research Logistics

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We consider a manufacturer (i.e., a capacitated supplier) that produces to stock and has two classes of customers. The primary customer places orders at regular intervals of time for a random quantity, while the secondary customers request a single item at random times. At a predetermined time the manufacturer receives advance demand information regarding the order size of the primary customer. If the manufacturer is not able to fill the primary customer's demand, there is a penalty. On the other hand, serving the secondary customers results in additional profit; however, the manufacturer can refuse to serve the secondary customers in order to reserve inventory for the primary customer. We characterize the manufacturer's optimal production and stock reservation policies that maximize the manufacturer's discounted profit and the average profit per unit time. We show that these policies are threshold-type policies, and these thresholds are monotone with respect to the primary customer's order size. Using a numerical study we provide insights into how the value of information is affected by the relative demand size of the primary and secondary customers.

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“…Gallego and Ö zer (2001) modeled perfect ADI through a vector of future demands and showed the optimality of a state-dependent order-up-to policy in a discrete-time setting. Dellaert and Melo (2003) dealt with the lot-sizing problem in a similar environment. Karaesmen et al (2002) considered a capacitated problem under perfect ADI and stochastic lead times.…”

Section: Introduction and Related Literaturementioning

confidence: 99%

Using imperfect advance demand information in ordering and rationing decisions

Tan

Güllü

Erkip

2009

International Journal of Production Economics

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a b s t r a c tIn this paper, we consider an inventory problem with two demand classes having different priorities. The appropriate policy of rationing the available stock, i.e. reserving some stock for meeting prospective future demand of preferred customers at the expense of deliberately losing some of the currently materialized demand of lower demand class(es), relies on the estimation of the future demand. Utilizing current signals on future demand, which we refer to as imperfect advance demand information (ADI), decreases uncertainty on future demand and may help to make better decisions on when to start rejecting lower class demand. We develop a model that incorporates imperfect ADI with inventory ordering (replenishment) decision and rationing available stock. In a two-period setting, we show some structural properties, solve the rationing problem, and propose solution methods based on Monte Carlo simulation for the ordering problem. We conduct numerical tests to measure the impact of system parameters on the expected value of imperfect ADI, and provide useful managerial insights.

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Approximate solutions for a stochastic lot-sizing problem with partial customer-order information

Cited by 15 publications

References 10 publications

Modelling imperfect advance demand information and analysis of optimal inventory policies

Modelling imperfect advance demand information and analysis of optimal inventory policies

A produce‐to‐stock system with advance demand information and secondary customers

Using imperfect advance demand information in ordering and rationing decisions

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