Distributionally robust inventory routing problem to maximize the service level under limited budget. (June 2019)

• Record Type: Journal Article
• Authors: Liu, Ming; Liu, Xin; Chu, Feng; Zheng, Feifeng; Chu, Chengbin
• Is Part Of: Transportation research. Volume 126(2019)
• Page Start: 190
• Page End: 211
• Publication Date: 2019-06
• Subjects: Inventory routing problem -- Stochastic optimization -- Distributionally robust -- Ambiguity set -- Service level; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2019.04.005
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: A new stochastic IRP with partial distributional information on demands is studied. A distributionally robust model with a probabilistic objective function is proposed. The sample average approximation method and a hierarchical approach are proposed. Abstract: This paper studies a stochastic inventory routing problem with alternative handling modules and limited capital budget, under partial distributional information (i.e., the mean and covariance matrix of customer demands). The objective is to maximize the service level, i.e., the probability of jointly ensuring no stockout and respecting the warehouse capacities for all customers at the end of each period. A novel distributionally robust chance constrained formulation is proposed. The sample average approximation method and a model-based hierarchical approach based on problem analysis are developed. Computational results show that the latter approach is more efficient. We also draw some managerial insights.