Inventory sharing in integrated network design and inventory optimization with low-demand parts

• Published in: European journal of operational research Vol. 224; no. 3; pp. 497 - 506
• Main Authors: Iyoob, Ilyas Mohamed, Kutanoglu, Erhan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2013; Elsevier; Elsevier Sequoia S.A
• Subjects: Decision analysis; Heuristic; Inventory; Inventory management; Inventory sharing; Logistics; Network design; Service parts logistics; Studies; Transportation problem (Operations research); Inventory sharing; Network design; Inventory; Service parts logistics; Logistics
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2012.09.033

► We analyze cost/service effects of inventory sharing in service parts logistics. ► We decide which facilities in which inventory sharing pools should be stocked. ► Single pool analysis simplifies the multi-pool version to a knapsack-type model. ► We show that a greedy heuristic is on average within 0.12% of the optimal solution. Service Parts Logistics (SPL) problems induce strong interaction between network design and inventory stocking due to high costs and low demands of parts and response time based service requirements. These pressures motivate the inventory sharing practice among stocking facilities. We incorporate inventory sharing effects within a simplified version of the integrated SPL problem, capturing the sharing fill rates in 2-facility inventory sharing pools. The problem decides which facilities in which pools should be stocked and how the demand should be allocated to stocked facilities, given full inventory sharing between the facilities within each pool so as to minimize the total facility, inventory and transportation costs subject to a time-based service level constraint. Our analysis for the single pool problem leads us to model this otherwise non-linear integer optimization problem as a modified version of the binary knapsack problem. Our numerical results show that a greedy heuristic for a network of 100 facilities is on average within 0.12% of the optimal solution. Furthermore, we observe that a greater degree of sharing occurs when a large amount of customer demands are located in the area overlapping the time windows of both facilities in 2-facility pools.