Freight Time and Cost Optimization in Complex Logistics Networks

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Sert E., Hedayatifar L., Rigg R. A., Akhavan A., Buchel O., Saadi D. E., ...More

COMPLEXITY, vol.2020, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2020
  • Publication Date: 2020
  • Doi Number: 10.1155/2020/2189275
  • Journal Name: COMPLEXITY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, BIOSIS, Compendex, Computer & Applied Sciences, INSPEC, zbMATH, Directory of Open Access Journals
  • Middle East Technical University Affiliated: No


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 k-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.