Optimal Control of a Two-Station Tandem Production/Inventory System

Veatch, Michael H.; Wein, Lawrence M.

doi:10.1287/opre.42.2.337

Cited by 109 publications

(56 citation statements)

References 26 publications

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“…Extending our analysis to a multistage model would be much more difficult, in light of the analytical intractability of Markovian tandem make-to-stock queueing systems (e.g., Veatch and Wein 1994). One tractable approach (see Rubio and Wein 1996) is to employ a CONWIP policy that maintains the total forecast-corrected WIP plus finished goods inventory (i.e., Qs + It-=l Dt,t+i, where Q is the system-wide WIP) at a constant base-stock level.…”

Section: Extensionsmentioning

confidence: 99%

Analysis of a Forecasting-Production-Inventory System with Stationary Demand

Toktay

Wein²

2001

Management Science

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128

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We consider a production stage that produces a single item in a make-to-stock manner. Demand for finished goods is stationary. In each time period, an updated vector of demand forecasts over the forecast horizon becomes available for use in production decisions. We model the sequence of forecast update vectors using the Martingale Model of Forecast Evolution developed by Graves et al. (1986Graves et al. ( , 1998 and Heath and Jackson (1994). The production stage is modeled as a single-server discrete-time continuous-state queue. We focus on the stationary version of a class of policies that is shown to be optimal in the finite time horizon, deterministic-capacity case, and use an approximate analysis rooted in heavy traffic theory and random walk theory to obtain a closed-form expression for the (forecast-corrected) base-stock level that minimizes the expected steady-state inventory holding and backorder costs. This:expression, which is shown to be accurate under certain conditions in a simulation study, sheds some light on the interrelationships among safety stock, stochastic correlated demand, inaccurate forecasts, and random and capacitated production in forecasting-production-inventory systems.

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Section: Extensionsmentioning

confidence: 99%

Analysis of a Forecasting-Production-Inventory System with Stationary Demand

Toktay

Wein²

2001

Management Science

Self Cite

128

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“…In [16] an inventory control system has been analysed for various control policies in which both the first and the second station can be controlled. The fact that in that paper the first station represents an inventory level, which can be negative, fundamentally changes the analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimal dispatching in a tandem queue

Leeuwen

Queija

2017

Queueing Syst

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We investigate a Markovian tandem queueing model in which service to the first queue is provided in batches. The main goal is to choose the batch sizes so as to minimize a linear cost function of the mean queue lengths. This model can be formulated as a Markov Decision Process (MDP) for which the optimal strategy has nice structural properties. In principle we can numerically compute the optimal decision in each state, but doing so can be computationally very demanding. A previously obtained approximation is computationally efficient for low and moderate loads, but for high loads also suffers from long computation times. In this paper, we exploit the structure of the optimal strategy and develop heuristic policies motivated by the analysis of a related controlled fluid problem. The fluid approach provides excellent approximations, and thus understanding, of the optimal MDP policy. The computational effort to determine the heuristic policies is much lower and, more importantly, hardly affected by the system load. The heuristic approximations can be extended to models with general service distributions, for which we numerically illustrate the accuracy.

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“…A make-to-stock queue refers to a make-to-stock system where the supply process is modeled by servers producing units one by one. Make-to-stock queues have been used to investigate issues such as stock allocation (de Véricourt et al, 2002), production scheduling (Zhao et al, 2008), dynamic pricing (Gayon et al, 2009b) and multi-echelon coordination (Veatch and Wein, 1994). A few make-to-stock papers include product returns (see e.g.…”

Section: Introductionmentioning

confidence: 99%