Two-stage risk-averse stochastic programming approach for multi-item single source ordering problem: CVaR minimisation with transportation cost

Integrating inventory and transportation decisions is vital in supply chain management and can enable decision-makers to achieve competitive advantages. This study considers a multi-item replenishment problem (MIRP) with a piece-wise linear transportation cost under demand uncertainty, which usually...

Full description

Saved in:
Bibliographic Details
Published in:International journal of production research Vol. 61; no. 7; pp. 2129 - 2146
Main Authors: Taghizadeh, Elham, Venkatachalam, Saravanan
Format: Journal Article
Language:English
Published: London Taylor & Francis 03.04.2023
Taylor & Francis LLC
Subjects:
conditional-value-at-risk (CVaR)
Decision making
L-shaped algorithm
multi-cuts
Operating costs
Replenishment
Risk
sample average algorithm
Stochastic models
Stochastic programming
Supply chains
Transportation
ISSN:0020-7543, 1366-588X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Integrating inventory and transportation decisions is vital in supply chain management and can enable decision-makers to achieve competitive advantages. This study considers a multi-item replenishment problem (MIRP) with a piece-wise linear transportation cost under demand uncertainty, which usually occurs both in retail and production environment when several items must be ordered from a single supplier. Conventionally, two-stage stochastic programming formulation is risk-neutral, and it lacks robustness in the presence of high data variability. Hence, we introduce the Conditional Value at Risk (CVaR) approach for MIRP. Additionally, we deploy both single and multi-cut L-shaped and the sample average approximation method to circumvent the computational complexity to solve large-scale instances. The data-driven simulation study is used to benchmark the results from deterministic, risk-neutral, and risk-averse stochastic models. The results indicate that under higher data variations, the risk-averse model provides better perspectives for a decision-maker. The results show a 40-50% reduction in lost sales with marginal growth in total cost while considering CVaR instead of a risk-neutral approach.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2022.2060770