Flexible service policies for a Markov inventory system with two demand classes

Liu, Mingwu; Feng, Mengying; Wong, Chee Yew

doi:10.1016/j.ijpe.2013.10.010

Cited by 25 publications

(16 citation statements)

References 17 publications

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“…In addition, many scholars have used it for inventory management to determine optimal order points and quantities, to implement sensitivity analyses, to measure fluctuations of policy variables, and to determine final overall changes in profit. Its main purpose is to provide a value analysis using given probability distribution assumptions and to expand inventory control analysis results [36]. Four major elements are required to employ the MDP:…”

Section: Markov Decision Process Elementsmentioning

confidence: 99%

Data-Driven Iron and Steel Inventory Control Policies

Tseng

Yu²

2019

Mathematics

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In this study, we investigated the optimal material inventory policy with regard to the iron and steel industry’s effort to reduce massive overstocking issues in the face of increased corporate competitiveness. We gathered actual data, including sales and inventory numbers, from a steel and iron company over a period of 216 weeks between January 2010 and February 2014. We then utilized the Markov decision process (MDP) to analyze this data for inventory problems, such as relevant reorder points and reorder quantity issues as they relate to lead time, stock on hand and the limitations of having stock in-transit. The purpose of the study was to determine the most effective method for minimizing costs by using the optimal inventory policy to calculate and verify the effectiveness of the results. The final 52 weeks of data were put aside, while the initial 164 weeks were used to create an inbound material receipt system to ultimately establish a yearly (52-week) policy based on the inventory and sales data for weeks 113–164. Finally, we verified the effectiveness of the policy using the data from the final 52 weeks. The results showed that our proposed categorization method was effective for reducing the quantity of inventory while still meeting quarterly demands.

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Section: Markov Decision Process Elementsmentioning

confidence: 99%

Data-Driven Iron and Steel Inventory Control Policies

Tseng

Yu²

2019

Mathematics

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“…The conventional models for inventory management with uncertain demand, such as variations of Harris (1913) formulation (Cárdenas-Barrón, Chung, & Treviño-Garza, 2014;Nobil & Taleizadeh, 2016;Budd & Taylor, 2019), Markov equation based ones (Boute, Disney, Lambrecht, & Van Houdt, 2007;Broyles, Cochran, & Montgomery, 2010;Liu, Feng, & Wong, 2014), Wilson's formulation (Wilson, 1934;Schwartz, Wang, & Rivera, 2006;Sarkar, 2013;Manna, Dey, & Mondal, 2017) are designed to minimize the expected costs of replenishment and stockouts, assuming, that complete satisfaction of uncertain and hardly predictable demand is too expensive or even deemed impossible. All these models are design under the constant order quantity principle, where the size of following order is based on the objective to minimize the whole costs of company's inventory management.…”

Section: Introductionmentioning

confidence: 99%

Eoq for Perishable Goods: Modification of Wilson’s Model for Food Retailers

Zeng

Nestorenko

et al. 2019

Technological and Economic Development of Economy

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A timely response to a fluctuating and ever-changing consumer demand is an important decision for a company, as it may impact its position in the market. Thus, proper inventory management becomes a focal point in retail business process management and can provide a substantial competitive advantage. In this paper, we introduce a modified version of Wilson’s model, which takes into account trends in consumer demand and offer flexibility in reordering time. The illustration of the proposed model is presented, showing the significant economic benefit under particular conditions.

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“…Zhao and Lian [13] addressed a queueing-inventory system with two classes of customers in which the priority for service is decided by the server. Liu et al [14] analysed a flexible service policies for an inventory system with two classes of customers. When the on-hand inventory drops to pre-determined safety level, arrival ordinary customers receive service at a fixed probability.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Optimization on a Two Demand Classes Inventory System

Malligarjunan¹

2015

Int. J. Appl. Comput. Math

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This paper investigates impact of change of priority on a continuous review inventory system with a mixture of backorders and lost sales. The inter-arrival processes are assumed to be exponentially distributed. The objective is to minimize the anticipated total cost rate by simultaneously optimizing the maximum inventory level and backorders. An entropy-based uncertainty metric for evaluating the operating performance is demonstrated. The proposed modification offers the practical value of establishing how the long-run cost rate is reduced. Various system performance measures are derived. Arrival rate of type-1(high priority) demands λ 2 KeywordsArrival rate of type-2(low priority) demands L(t)On-hand inventory level at time t φ i

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Flexible service policies for a Markov inventory system with two demand classes

Cited by 25 publications

References 17 publications

Data-Driven Iron and Steel Inventory Control Policies

Data-Driven Iron and Steel Inventory Control Policies

Eoq for Perishable Goods: Modification of Wilson’s Model for Food Retailers

Cost-Effective Optimization on a Two Demand Classes Inventory System

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