Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Freight Time and Cost Optimization in Complex Logistics Networks
Download
10.1155:2020:2189275.pdf
Date
2020-2-29
Author
Sert, Egemen
Hedayatifar, Leila
Rigg, Rachel A.
Akhavan, Amir
Buchel, Olha
Saadi, Dominic Elias
Kar, Aabir Abubaker
Morales, Alfredo J.
Bar-Yam, Yaneer
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
4
views
3
downloads
The complexity of providing timely and cost-effective distribution of finished goods from industrial facilities to customers makes effective operational coordination difficult, yet effectiveness is crucial for maintaining customer service levels and sustaining a business. Logistics planning becomes increasingly complex with growing numbers of customers, varied geographical locations, the uncertainty of future orders, and sometimes extreme competitive pressure to reduce inventory costs. Linear optimization methods become cumbersome or intractable due to the large number of variables and nonlinear dependencies involved. Here, we develop a complex systems approach to optimizing logistics networks based upon dimensional reduction methods and apply our approach to a case study of a manufacturing company. In order to characterize the complexity in customer behavior, we define a “customer space” in which individual customer behavior is described by only the two most relevant dimensions: the distance to production facilities over current transportation routes and the customer’s demand frequency. These dimensions provide essential insight into the domain of effective strategies for customers. We then identify the optimal delivery strategy for each customer by constructing a detailed model of costs of transportation and temporary storage in a set of specified external warehouses. In addition, using customer logistics and the <jats:italic>k</jats:italic>-means algorithm, we propose additional warehouse locations. For the case study, our method forecasts 10.5% savings on yearly transportation costs and an additional 4.6% savings with three new warehouses.
Subject Keywords
Multidisciplinary
URI
https://hdl.handle.net/11511/51552
Journal
Complexity
DOI
https://doi.org/10.1155/2020/2189275
Collections
Department of Electrical and Electronics Engineering, Article