Scatter Search Applied to the Vehicle Routing Problem with Simultaneous Delivery and Pickup

Abstract

Though its origins can be traced back to 1977, the development and application of the metaheuristic Scatter Search (SS) has stayed dormant for 20 years. However, in the last 10 years, research interest has positioned SS as one of the recognizable methodologies within the umbrella of evolutionary search. This paper presents an application of SS to the problem of routing vehicles that are required both to deliver and pickup goods (VRPSDP). This specialized version of the vehicle routing problem is particularly relevant to organizations that are concerned with sustainable and environmentally-friendly business practices. In this work, the efficiency of SS is evaluated when applied to this problem. Computational results of the application to instances in the literature are presented.

Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

“…Some of metaheuristic algorithms applied for resolving GPDP are nature-inspired, such as the genetic algorithm [23] and ant colony optimization [24] . The others are memory-based search strategies, which include GRASP [25] , Tabu search [14] , and scatter search [2] .…”

“…The transportation cost is defined as a constant multiplication of traveled distance and the operational cost is a fixed constant for all deployed vehicles. Constraints (2)-(6) describe the vehicle routing conservation rules where constraints (2) and (3) indicate every node is served once by a vehicle, constraint (4) ensures the satisfaction of the vehicle flow conservation, and constraints (5) and (6) stipulate that every vehicle at most leaves from and returns to the cross-dock once. Eqs.…”

“…This constraint has several implications: all the suppliers and customers should be served, every service should be performed in a single visit (a few studies do consider divisive service where a supplier or customer can be served with multiple visits), different vehicles should not visit the same supplier/customer, and the guarantee of vehicle flow conservation (i.e., after visiting a supplier/customer, the service vehicle should go directly to the next supplier/customer or the cross-dock). (2) The vehicle capacity constraint stipulates that every type of vehicle has its respective maximal capacity for the storage of goods and the loading of the vehicle should not exceed the vehicle's capacity at any time. (3) The time constraint considers the maximal duration time of the entire pickup and delivery process or a feasible time window for the visit of each supplier/customer.…”

“…These cross-docking research topics cast new operation scenarios on some classic combinatorial problems, such as the vehicle routing [2,3] , task allocation [4] , flow-shop scheduling [5] , and p -median [6] problems. Resembling the existing methods for these noted complex combinatorial problems, the major cross-docking solutions fall into three kinds of approaches: mathematical programing, heuristics, and metaheuristics.…”

A Methodology to Exploit Profit Allocation in Logistics Joint Distribution Network Optimization

“…Customer clustering is usually an intermediate stage during the MDVRP optimization procedure. The distribution centers (DCs) can be assigned to a number of customer clustering units for delivery service, and this issue can be considered as a variant of the quadratic assignment problem (QAP) [25,26]. The QAP was first introduced by Koopmans and Beckmann [27] and aimed to assign facilities to locations in such a way that each facility is assigned to one exact location.…”