Capacity allocation in flexible production networks: Theory and applications

In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity, to meet pre-determined fill rate requirements? We develop a...

Full description

Saved in:
Bibliographic Details
Main Authors: LYU, Guodong, CHEUNG, Wang-Chi, CHOU, Mabel C., TEO, Chung-Piaw, ZHENG, Zhichao, ZHONG, Yuanguang
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/6221
https://ink.library.smu.edu.sg/context/lkcsb_research/article/7220/viewcontent/Capacity_Allocation_2018_pp.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary:In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity, to meet pre-determined fill rate requirements? We develop a new approach for network capacity configuration and allocation, and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services.We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that "long chain is almost as good as the fully flexible network": For given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network, and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last mile delivery operations, and in semi-conductor production planning, using real data from two companies.