An Efficient Simulated Annealing Algorithm for the Vehicle Routing Problem in Omnichannel Distribution

Abstract

A variant of the vehicle routing problem (VRP) known as the Vehicle Routing Problem in Omnichannel Retailing Distribution Systems (VRPO) has recently been introduced in the literature, driven by the increasing adoption of omnichannel logistics in practice. The VRPO scenario involves a large retailer managing several stores, a depot, and a homogenous fleet of vehicles to meet the demands of both stores and online customers. This variant falls within the class of VRPs that consider precedence constraints. Although the vehicle routing problem in omnichannel retailing distribution (VRPO) has been addressed using a few heuristic and metaheuristic approaches, the use of Simulated Annealing (SA) remains largely unexplored in the pickup and delivery problem (PDP) literature, both before and after the rise of omnichannel logistics. This article introduces the Efficient Simulated Annealing (ESA) algorithm, demonstrating its suitability in generating new benchmark solutions for the VRPO. In experiments with sixty large instances, ESA significantly outperformed two previous algorithms, discovering new best-known solutions (BKSs) in fifty-nine out of sixty cases. Additionally, ESA demonstrated superior efficiency in 68.3% of the test cases in terms of reduced computational times, showcasing its higher effectiveness in handling complex VRPO instances.

Keywords

• vehicle routing problem
• pickup and delivery
• simulated annealing
• metaheuristics
• omnichannel distribution