Service levels, system cost and stability of production–inventory control systems

Bijulal, D.; Venkateswaran, Jayendran; Hemachandra, N.

doi:10.1080/00207543.2010.538744

Cited by 25 publications

(14 citation statements)

References 22 publications

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“…Many researchers have explored the practical impact of the parameter setting on system stability performance with the POUT policy, that is, α S = α SL . These researchers argue that POUT policy can improve system stability in a continuous review system [45]. Therefore, this paper adopts the POUT policy, β = α S = α SL , to explore the effect of stochastic lead times on the inventory system in the range of [0.1,1.9] for parameter β.…”

Section: Experiments Designmentioning

confidence: 99%

The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost

Fei

Er-min

et al. 2019

Sustainability

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In supply chain operation practices, lead time uncertainty is a common management issue. Uncertain lead time can lead to increased inventory costs and unstable service levels, which will directly affect the overall operation performance of the supply chain. While considering environmental performance in supply chain, it is important to understand how an uncertain lead time will affect sustainable performance. In this paper, we constructed a supply chain model with stochastic lead time and explored the relationship between uncertain lead time and supply chain performance. We considered carbon cost, inventory cost, and service level as a supply chain performance. System dynamics methodology was employed to observe and explore the dynamic change trend of the overall performance in the complicated supply chain model. This was done under both different levels of lead time standard deviation and different order policies. The results demonstrate how stochastic lead times can significantly increase inventory costs and carbon costs. Therefore, we propose appropriate ordering policies which mitigate the negative impacts of stochastic lead times.

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Section: Experiments Designmentioning

confidence: 99%

The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost

Fei

Er-min

et al. 2019

Sustainability

View full text Add to dashboard Cite

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“…Equation models the order rule adopted in our model. The order rule is a well‐known variant of a periodic review Order‐Up‐To (OUT), namely, a smoothing replenishment rule (Boute et al., ; Bijulal et al., ; Strozzi et al., ). The features of smoothing replenishment rules were studied and popularized by Towill (), John et al.…”

Section: The Coordinated Decentralized Linear Sc Modelmentioning

confidence: 99%

A simulation model of a coordinated decentralized supply chain

Cannella

López-Campos

Domínguez

et al. 2015

Int Trans Operational Res

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This paper presents a simulation-based study of a coordinated, decentralized linear supply chain (SC) system. In the proposed model, any supply tier considers its successors as part of its inventory system and generates replenishment orders on the basis of its partners' operational information. We show that the proposed coordinated decision-making process creates a reduction in information distortion along an SC compared with a traditional, noncoordinated strategy. A novel result is that we show how a coordinated SC can avoid the detrimental consequences of demand amplification in terms of penalty costs due to the stockout phenomenon in upstream stages.

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“…The ordering policy/production algorithm of APIOBPCS is representative of work in [40]. Most of the APIOBPCS research has been undertaken using both control theory mathematics and system dynamics (SD), the most representative works in SD are present in [41] where it is applied to APP via transfer function approach; in [42] applying SD for production-inventory systems; in [43] a production-inventory system for APP described by differential equations via control oriented approaches; and in [44] where the SD approach is extended in production-inventory in remanufacturing. Based on this, APIOBPCS models are usually expressed in a continuous control form, but there is a discrete version available as well.…”

Section: Control Engineering and Production/inventory Controlmentioning

confidence: 99%

Optimal Control Approaches to the Aggregate Production Planning Problem

Davizón¹,

Martínez-Olvera

Soto

et al. 2015

Sustainability

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Abstract:In the area of production planning and control, the aggregate production planning (APP) problem represents a great challenge for decision makers in production-inventory systems. Tradeoff between inventory-capacity is known as the APP problem. To address it, static and dynamic models have been proposed, which in general have several shortcomings. It is the premise of this paper that the main drawback of these proposals is, that they do not take into account the dynamic nature of the APP. For this reason, we propose the use of an Optimal Control (OC) formulation via the approach of energy-based and Hamiltonian-present value. The main contribution of this paper is the mathematical model which integrates a second order dynamical system coupled with a first order system, incorporating production rate, inventory level, and capacity as well with the associated cost by work force in the same formulation. Also, a novel result in relation with the Hamiltonian-present value in the OC formulation is that it reduces the inventory level compared with the pure energy based approach for APP. A set of simulations are provided which verifies the theoretical contribution of this work.

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Service levels, system cost and stability of production–inventory control systems

Cited by 25 publications

References 22 publications

The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost

The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost

A simulation model of a coordinated decentralized supply chain

Optimal Control Approaches to the Aggregate Production Planning Problem

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