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Vehicle routing in drone-based package delivery services
Published in Klára Szita Tóthné, Károly Jármai, Katalin Voith, Solutions for Sustainable Development, 2019
A. Agárdi, L. Kovács, T. Bányai
The algorithms use permutation representation. The permutation contains the numbers of the costumers. The evaluation of the permutation is the following: we give the elements of the permutation until the capacity constraint of the drone is reached. These customers will belong to one route. Then we give the following elements of the permutation until the capacity constraint of the drone is not met. This will be the second route of the drone. We continue these process until not all costumers are selected. It will give the number of routes of the drone, and this also means the number of customers of the truck. In the following, we give a case of study with some explanation and figures. The permutation of the customers can be described with the permutation (2, 4, 3, 1, 8, 7, 9, 10, 6, 5, 11, 12, 15, 13, 14). The demand of the customers is also given: (20, 30, 50, 10, 20, 40, 60, 30, 10, 20, 40, 50, 50, 10, 10).
Pre-auction lane selection in an integrated production–distribution planning problem
Published in Engineering Optimization, 2021
Chefi Triki, Sujan Piya, Liang-Liang Fu
GAs must have a good genetic representation of the problem under study. Rather than binary representation, permutation representation is used in this article. The individual chromosome consists of two segments. The first segment represents the production schedule with a sequence of orders. The second segment represents the distribution schedule, characterized by a set of trips {A1, … , Ak, B}, where Ai = ai1, … , aim,0} means trip i of the manufacturer’s fleet and aij is the jth order delivered on this trip. Furthermore, B = {b1, … ,bn−m} represents the set of orders delivered by one of the ODs selected based on the auction. Zeros are inserted randomly to create the trips, taking into account the vehicle capacity.