Vehicle route planning in e-waste mobile collection on demand supported by artificial intelligence algorithms

An evolutionary approach to the vehicle route planning in e-waste mobile collection on demand

“…To create a base solution, which was applied during the determination of the value of the objective function of the result; of the value of the objective function equal to 1, the GrA proposed by Nowakowski et al (2018) Remove locations in pva from p 10: end while 11: return X…”

“…To ensure timely and cost-efficient waste collection, AI algorithms should be used to solve the VRPTW and TOP issues. Prototype systems for on-demand waste collection have been discussed by Nowakowski et al (2018). The following algorithms were used in applied solutions: Simulated Annealing (SA), Tabu Search (TS), Greedy Algorithm (GrA) and improved Bee Colony Optimization (BCOi).…”

“…This article is a development of previous research (Król et al 2016;Nowakowski et al 2017Nowakowski et al , 2018 as an attempt to test the application of other algorithms and to assess their efficiency during the execution of transportation tasks in designing vehicle routes for on-demand waste collection from inhabitants within a defined time window. Due to the great popularity of Evolutionary Algorithms (EA), although basic evolutionary techniques were formulated several decades ago, they are still used to solve many optimization problems.…”

“…For example, Rahmani Hosseinabadi et al 2018used Extended Genetic Algorithm to solve open-shop scheduling problem, and Kadri and Boctor (2018) used Genetic Algorithm for the resource-constrained project scheduling problem with transfer times. Considering the results of the research presented by Nowakowski et al (2018), indicating the efficiency of local search algorithms for solving the problem of planning mobile WEEE collection, we assume the opportunity to further improve the process of e-waste mobile collection by using the synergic effect occurring in the MA, which is formed as a result of a combination of the local search and the EA. The MA has been successfully used in solving classic VRPTW (e.g.…”

The adaptation of the harmony search algorithm to the ATSP with the evaluation of the influence of the pitch adjustment place on the quality of results

“…The Traveling Salesman Problem (TSP) is a classical combinatorial optimization problem that involves finding the shortest Hamiltonian cycle in a complete weighted graph. Its popularity stems from the fact that it belongs to the class of NP-hard problems and that it can model a variety of utilitarian issuesits asymmetric variant (characterized by the possibility of varying weights of edges connecting the same nodes), representing line infrastructure located in urban areas, has become the basis for many logistical problems (it models, for example, the process of planning the mobile collection of waste electrical and electronic equipment, which has been described by Mrówczyńska & Nowakowski, 2015;Nowakowski, Szwarc, & Boryczka, 2018, and the process of transport activities related to the acquisition of municipal waste, described by Płaczek & Szołtysek, 2008;Syberfeldt, Rogstrom, & Geertsen, 2015).…”