Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/172085
Title: A bin-packing based heuristic for the integrated inventory and vehicle routing problem
Authors: Yang, Yang
Keywords: Business::Operations management::Supply chain management
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Yang, Y. (2023). A bin-packing based heuristic for the integrated inventory and vehicle routing problem. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172085
Abstract: We consider the Integrated Inventory and Vehicle Routing Problem (IIVRP) for a single product distribution system consisting of one upstream central warehouse and multiple downstream retailers and additive manufacturing in a spare parts supply chain in this thesis. In Chapter 2 IIVRP without central inventory, we develop a new heuristic algorithm with a Stationary non-nested Joint Replenishment Policy with Bin-packing feature (SJRPB) to solve this problem. Compared to the existing heuristic of Viswanathan and Mathur (1997), our new heuristic achieves cost savings for randomly generated problems with small to moderate vehicle capacity regardless of the geographical sparsity of retailers' locations. For problems with large vehicle capacity, the solutions generated by our heuristic algorithm improve the cost performance when the locations of the retailers are geographically sparse. In Chapter 3, a more generalized version of IIVRP for a single product distribution system is studied that both the central warehouse and retailers hold inventory. In addition to the decision variables in IIVRP without central inventory problem, the replenishment order amount and schedule at the central warehouse is required to be determined. We develop a new heuristic algorithm called Stationary non-nested Joint Replenishment Policy with Bin-packing feature involving Central inventory (SJRPB-C) to solve this multi-echelon one-warehouse multi-retailer distribution system with central inventory. Computational studies show that in general our heuristic algorithm provides better solutions than a modified SNJRP heuristic, and outperforms the heuristic of Jung and Mathur (2007) under certain scenarios. Chapter 4 investigates the cost performance of two spare parts supply chains, where one is an additive manufacturing enabled distributed production-inventory system and the other one is a mirrored traditional manufacturing system. The central premise is that in distributed additive manufacturing the inventory cost saving from the agile response to demand, reduced leadtime and lowered inventory level potentially offsets the increased production cost of additively manufactured products under certain supply chain configurations and system parameters. We present a series of simulation studies to analyze the impacts of downstream size, leadtime, demand mean, demand variability, cost premiums of additively manufactured products, additive manufacturing printer capacity, and inventory cost parameters on the relative cost performance of the two competing systems. Using simulation, we evaluate the key cost driver in the additive manufacturing system and identify the supply chain configurations that are advantageous for the additive manufacturing enabled distributed production-inventory system.
URI: https://hdl.handle.net/10356/172085
DOI: 10.32657/10356/172085
Schools: Nanyang Business School 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NBS Theses

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