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Optimal inventory policies with postponed demand by price discounts

Optimal inventory policies with postponed demand by price discounts
Optimal inventory policies with postponed demand by price discounts
This thesis introduces a demand postponement policy in order to improve the performance of inventory management under batch ordering, advance demand information, capacitated/ uncapacitated and periodic/continuous review inventory systems. The main aim of this study is to find integrated demand postponement and inventory policies. The structure of the thesis consists of five main chapters which starts with an introduction in Chapter 1 which summarizes the main objectives of the study with a background information, followed by a Chapter 2 presenting an overview of the relevant literature and the methodology. Chapter 3 as the first research paper, an inventory problem with stochastic demand and batch ordering and lost sales based on a real case is introduced and a demand postponement policy applied on this system to convert some of the lost sales to advance demand. A Markov Decision Process model is proposed and it is solved through Linear Programming (LP). The dual of the primal model is used to reduce the computational effort and it is tested with several numerical data sets. The optimal inventory policy and discount policy for different batches are shown for managerial insights. In Chapter 4, the same problem without batch ordering is formulated by Markov Decision Process (MDP) solved by Backward induction algorithm. In addition, the demand pattern is changed to Advance Demand Information (ADI) which combines both stochastic and deterministic demand. The properties of optimal inventory and postponement policy parameters are analyzed and the numerical experiments are carried out under the uncapacitated and capacitated systems to show the impact of the postponement policy. The comparison of policy parameters with the literature shows that the demand postponement policy is highly effective for the efficient use of capacity. In Chapter 5, the extension of the problem to a continuous review inventory system with distribution strategies is studied by an Net Present Value (NPV) approach. The effectiveness of demand postponement under different financial settings are examined and an extensive numerical experiments are presented. The thesis ends with a conclusion in Chapter 6 including the summary of the results, limitations of the study and further research directions.
University of Southampton
Alim, Muzaffer
fb85c754-4573-4548-afc7-7ba1ed73f20a
Alim, Muzaffer
fb85c754-4573-4548-afc7-7ba1ed73f20a
Beullens, Patrick
893ad2e2-0617-47d6-910b-3d5f81964a9c

Alim, Muzaffer (2018) Optimal inventory policies with postponed demand by price discounts. University of Southampton, Doctoral Thesis, 119pp.

Record type: Thesis (Doctoral)

Abstract

This thesis introduces a demand postponement policy in order to improve the performance of inventory management under batch ordering, advance demand information, capacitated/ uncapacitated and periodic/continuous review inventory systems. The main aim of this study is to find integrated demand postponement and inventory policies. The structure of the thesis consists of five main chapters which starts with an introduction in Chapter 1 which summarizes the main objectives of the study with a background information, followed by a Chapter 2 presenting an overview of the relevant literature and the methodology. Chapter 3 as the first research paper, an inventory problem with stochastic demand and batch ordering and lost sales based on a real case is introduced and a demand postponement policy applied on this system to convert some of the lost sales to advance demand. A Markov Decision Process model is proposed and it is solved through Linear Programming (LP). The dual of the primal model is used to reduce the computational effort and it is tested with several numerical data sets. The optimal inventory policy and discount policy for different batches are shown for managerial insights. In Chapter 4, the same problem without batch ordering is formulated by Markov Decision Process (MDP) solved by Backward induction algorithm. In addition, the demand pattern is changed to Advance Demand Information (ADI) which combines both stochastic and deterministic demand. The properties of optimal inventory and postponement policy parameters are analyzed and the numerical experiments are carried out under the uncapacitated and capacitated systems to show the impact of the postponement policy. The comparison of policy parameters with the literature shows that the demand postponement policy is highly effective for the efficient use of capacity. In Chapter 5, the extension of the problem to a continuous review inventory system with distribution strategies is studied by an Net Present Value (NPV) approach. The effectiveness of demand postponement under different financial settings are examined and an extensive numerical experiments are presented. The thesis ends with a conclusion in Chapter 6 including the summary of the results, limitations of the study and further research directions.

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PhD THESIS FINAL VIVA Corrected - Version of Record
Restricted to Repository staff only until 19 November 2019.
Available under License University of Southampton Thesis Licence.

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Published date: October 2018

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Local EPrints ID: 426439
URI: https://eprints.soton.ac.uk/id/eprint/426439
PURE UUID: 8d38f919-199a-45a6-b1a2-baac82081566

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Date deposited: 27 Nov 2018 17:30
Last modified: 13 Mar 2019 17:49

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