Article: Modelling and optimising the multi-item stochastic joint replenishment problem with uncertain lead-time and controllable major ordering cost Journal: European Journal of Industrial Engineering (EJIE) 2019 Vol.13 No.6 pp.746 - 774 Abstract: In this paper, we extend the existing stochastic joint replenishment model to a more realistic condition by considering uncertainties in lead-time and effective investment to reduce the major ordering cost. The aim is to determine the optimal strict cyclic replenishment policy and the optimal major ordering cost simultaneously to minimise the total cost. The objective cost function is approximated by expressing one element of the cost function as a Taylor series expansion. A bounds-based heuristic algorithm is then developed to solve the proposed model. The performance of the algorithm and the quality of the approximation are examined by computational experiments. The results of the models without considering uncertainty and ordering cost reduction are presented to illustrate the effectiveness of the proposed model. Experimentation and analysis of results demonstrate that the standard deviation of lead-time has a significant effect on the system. [Received: 21 December, 2016; Revised: 29 July 2017; Revised: 29 December 2017; Revised: 30 September 2018; Revised: 17 November 2018; Accepted: 26 January 2019] Inderscience Publishers - linking academia, business and industry through research

Title: Modelling and optimising the multi-item stochastic joint replenishment problem with uncertain lead-time and controllable major ordering cost

Authors: Xue-Yi Ai; Jin-Long Zhang; Dong-Ping Song; Lin Wang

Addresses: School of Management, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China ' School of Management, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China ' Management School, University of Liverpool, Liverpool, L69 7ZH, UK ' School of Management, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China

Abstract: In this paper, we extend the existing stochastic joint replenishment model to a more realistic condition by considering uncertainties in lead-time and effective investment to reduce the major ordering cost. The aim is to determine the optimal strict cyclic replenishment policy and the optimal major ordering cost simultaneously to minimise the total cost. The objective cost function is approximated by expressing one element of the cost function as a Taylor series expansion. A bounds-based heuristic algorithm is then developed to solve the proposed model. The performance of the algorithm and the quality of the approximation are examined by computational experiments. The results of the models without considering uncertainty and ordering cost reduction are presented to illustrate the effectiveness of the proposed model. Experimentation and analysis of results demonstrate that the standard deviation of lead-time has a significant effect on the system. [Received: 21 December, 2016; Revised: 29 July 2017; Revised: 29 December 2017; Revised: 30 September 2018; Revised: 17 November 2018; Accepted: 26 January 2019]

Keywords: joint replenishment problem; JRP; stochastic demand; uncertain lead-time; inventory; optimisation; major ordering cost reduction; heuristics.

DOI: 10.1504/EJIE.2019.104280

European Journal of Industrial Engineering, 2019 Vol.13 No.6, pp.746 - 774

Published online: 02 Jan 2020 *

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Title: Modelling and optimising the multi-item stochastic joint replenishment problem with uncertain lead-time and controllable major ordering cost

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