Inventory Rationing in a Make‐to‐Stock System with Batch Production and Lost Sales

Pang, Zhan; Shen, Houcai; Cheng, T.C.E.

doi:10.1111/poms.12190

Cited by 23 publications

(15 citation statements)

References 42 publications

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“…They both show that threshold type policies based on work storage level are optimal. Pang et al [22] allows batch demand and phase-type processing time distributions. Yücel and Bulut [23] assumes Coxian production times with non-zero production start-up cost.…”

Section: Mostmentioning

confidence: 99%

Analysis of make-to-stock queues with general processing times and start-up and lost sales costs

Özkan

Bulut

2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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We consider a make-to-stock environment with a single production unit that corresponds to a single machine or a line. Production and hence inventory are controlled by the two-critical-number policy. Production times are independent and identically distributed general random variables and demands are generated according to a stationary Poisson process. We model this production-inventory system as an M/G/1 make-to-stock queue. The main contribution of the study is to extend the control of make-to-stock literature by considering general production times, lost sales and fixed production costs at the same time. We characterize the long-run behaviour of the system and also propose a simple but very effective approximation to calculate the control parameters of the two-critical-number policy. An extensive numerical study exhibits the effects of the production time distribution and the system parameters on the policy control levels and average system cost.

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Section: Mostmentioning

confidence: 99%

Analysis of make-to-stock queues with general processing times and start-up and lost sales costs

Özkan

Bulut

2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…During the recent years, the rationing policies have been investigated for various systems with different processing time distributions (Lee and Hong 2003;Gayon, DeVericourt, and Karaesmen 2009), with batch ordering (Huang and Iravani 2007;Pang, Shen, and Cheng 2014), with batch arrivals (Çil, Ormeci, and Karaesmen 2009), among others (Carr and Duenyas 2000;Duran et al 2007;Gayon, Benjaafar, and DeVéricourt 2009;Liu et al 2015).…”

Section: Production Control With Multiple Demand Classesmentioning

confidence: 99%

Modelling and analysis of a cooperative production network

Hosseini

Tan

2019

International Journal of Production Research

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In this study, we examine the cooperative production business model for a group of producers serving their own customers and also have access to external customers who can make an agreement to buy products at a lower price if a desired service level can be guaranteed. When the producers cannot meet the desired service level requirement of the external customers at the offered price on their own, they participate in a cooperative network. The network consolidates the external customers for its members and routes an arriving external customer to one of the participants. We determine the optimal production and rationing policies for each participating manufacturer as well as the optimal routing policy for the network. We also propose an accurate approximate method to analyse a network with a high number of homogeneous producers using a single queue approximation method. We show that, based on the parameters of the producers and the external market, the network can provide the desired service level for the external customers at the offered price and makes all the members increase their profit by better utilising their capacity and serving more external customers.

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“…Fadiloglu and Bulut (2010) propose a dynamic rationing policy for the continuous-review inventory systems based on the status of outstanding orders. Moreover, both Huang and Iravani (2008) and Xu et al (2010) consider stock rationing problems with batch demand while Pang et al (2014) study inventory rationing in the make-to-stock systems with batch ordering and lost sales.…”

Section: Introductionmentioning

confidence: 99%

The Joint Stock and Capacity Rationings of a Make-To-Stock System with Flexible Demand

Lin

Chen

Feng

et al. 2018

Asia Pac. J. Oper. Res.

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This paper studies the joint stock and production capacity rationing polices for a make-to-stock system with two partially substitutable products manufactured at two factories, respectively. Customers are classified into three types with each type further segmented into multiple classes. Type I and Type II customers are selective and accept their favorite products only. Type III accepts either of the two products and hence its demand is regarded as the flexible demand. The management has to decide whether to run or stop production and whether the various classes of demand for each type of customers have to be satisfied from available inventory or not — in which case demand is lost — in order to minimize the firm’s inventory and production costs. We formulate the problem as a make-to-stock queue system and characterize the optimal joint stock and production rationing policies as monotone switching curves. Numerical examples are given to illustrate the optimal policies and demonstrate the value of flexible demand. Moreover, we develop a simple heuristic policy and compare the costs associated with the optimal and heuristic policies for different parameters.

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Inventory Rationing in a Make‐to‐Stock System with Batch Production and Lost Sales

Cited by 23 publications

References 42 publications

Analysis of make-to-stock queues with general processing times and start-up and lost sales costs

Analysis of make-to-stock queues with general processing times and start-up and lost sales costs

Modelling and analysis of a cooperative production network

The Joint Stock and Capacity Rationings of a Make-To-Stock System with Flexible Demand

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