Optimizing Delhivery’s Midmile Logistics Network Using a Hybrid Evolutionary Search Algorithm

Kishan Thakkar (), Purvi Rastogi () and Sarada P. Sarmah ()
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Kishan Thakkar: Operations Research Department, Delhivery Limited, Bengaluru, Karnataka 560025, India
Purvi Rastogi: Operations Research Department, Delhivery Limited, Bengaluru, Karnataka 560025, India
Sarada P. Sarmah: Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India

Interfaces, 2025, vol. 55, issue 2, 121-136

Abstract: This study demonstrates the application of operations research techniques to enhance the operational efficiency of a large logistics network. The focus is on addressing a variant of the vehicle routing problem faced by Delhivery, a leading logistics company in India, to improve its midmile operations. The problem involves enhancing a complex distribution network with more than 3,000 locations, which can be closely described as a multidepot fleet size and mix site-dependent asymmetric distance-constrained vehicle routing problem with time windows. The study also considers the geographic scope of the network, which has not been previously explored in the literature. A novel mixed integer linear programming formulation is presented along with a powerful hybrid evolutionary search algorithm that has been tested in many real-world routing problems. Additionally, a novel insertion algorithm that significantly reduces computational time is introduced. Furthermore, the capabilities of the algorithm are expanded to determine the optimal locations for new hubs within Delhivery’s network. The proposed algorithm achieves significant cost savings of nearly 7.3% and offers various managerial advantages. The algorithm converges rapidly and automates the entire planning and operations process, resulting in improved overall efficiency.

Keywords: vehicle routing problem; hybrid evolutionary search algorithm; multidepot; heterogeneous fleet; time windows (search for similar items in EconPapers)
Date: 2025
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