An Iterated Local Search Algorithm for the Pollution Traveling Salesman Problem

Cacchiani, Valentina; Contreras‐Bolton, Carlos; Escobar, John Willmer; Escobar-Falcón, Luis Miguel; Linfati, Rodrigo; Toth, Paolo

doi:10.1007/978-3-030-00473-6_10

Cited by 5 publications

(29 citation statements)

References 7 publications

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“…The latter introduces a mixed integer nonlinear programming model, and proposes an ALNS heuristic for solving the problem. Cacchiani et al (2018) introduced the PTSP: they presented a mixed integer linear programming (MILP) model, enhanced with explicit subtour elimination constraints (SECs). This model was solved by a cut-and-branch algorithm (C&B) and tested on instances with up to 50 customers.…”

Section: Literature Overview On Prp and Ptspmentioning

confidence: 99%

“…Then, it applies a multioperator genetic algorithm (GA) to improve these solutions, using effective crossover and mutation TSP operators from the literature. Finally, it refines the best solution found by applying the ILS algorithm proposed in Cacchiani et al (2018). The contributions of this work are as follows:…”

Section: Contributionsmentioning

confidence: 99%

“…• to find better solutions than those found by the C&B and ILS algorithms, proposed in Cacchiani et al (2018) for the PTSP, on instances with up to 200 customers in short computing times; • to find the best solution, in short computing times, for all (but two) instances presented in Wang et al (2020) for the EMTSP, most of which were solved to optimality by their branch-and-bound algorithm.…”

Section: Contributionsmentioning

confidence: 99%

“…We study two generalizations of the asymmetric traveling salesman problem (ATSP), both focusing on environmental issues: given a set of customers to be visited by a vehicle, the goal is to reduce its impact on the environment. The pollution traveling salesman problem (PTSP) was recently introduced in Cacchiani et al (2018): it requires to determine a Hamiltonian tour minimizing pollution, that is, fuel consumption (dependent on vehicle speed and load), but also accounting for driver costs. The energy minimization traveling salesman problem (EMTSP) was newly introduced in Wang et al (2020): it requires to find a Hamiltonian tour minimizing energy: the latter is measured as the sum, over all arcs, of the products between distance and vehicle load (including curb weight).…”

Section: Introductionmentioning

confidence: 99%

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A matheuristic algorithm for the pollution and energy minimization traveling salesman problems

Cacchiani

Contreras‐Bolton

Falcón

et al. 2021

Int Trans Operational Res

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The pollution traveling salesman problem (PTSP) and the energy minimization traveling salesman problem (EMTSP) generalize the well‐known asymmetric traveling salesman problem by including environmental issues and the goal of reducing carbon emissions. Both problems call for determining a Hamiltonian tour that, in the PTSP, minimizes a function of fuel consumption and driver cost (where the fuel consumption depends on the distance traveled, the vehicle speed, and the vehicle load), while, in the EMTSP, minimizes a function depending on the vehicle load and the traveled distances. For both PTSP and EMTSP, we propose a matheuristic algorithm that uses the solution of the linear programming relaxation of a mixed integer linear programming model for the considered problem to determine good initial feasible solutions, applies a multioperator genetic algorithm to improve these solutions, and refines the best solution found through an iterated local search procedure. In order to evaluate the performance of the proposed matheuristics, we compare them with exact and heuristic algorithms from the literature on benchmark instances of both problems.

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Section: Literature Overview On Prp and Ptspmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A matheuristic algorithm for the pollution and energy minimization traveling salesman problems

Cacchiani

Contreras‐Bolton

Falcón

et al. 2021

Int Trans Operational Res

Self Cite

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“…In [29], a new mathematical programming formulation was generated, and an iterated local search method was in order to minimize the sum of the travelling and the waiting times at the depot. Cacchiani et al [30] developed an iterated local search algorithm to solve the pollution TSP that looks for generating a Hamiltonian cycle which optimizes a function of driver costs and fuel consumption (which depends on load, speed, and distance). In [31], the iterated local search was used with the variable neighbourhood descent to solve the multivehicle inventory routing problem by minimizing the total cost of storage and transportation.…”

Section: The Proposed Metaheuristic: Dhouib-matrix-3 (Dm3)mentioning

confidence: 99%

Novel Metaheuristic Based on Iterated Constructive Stochastic Heuristic: Dhouib-Matrix-3 (DM3)

Dhouib

2021

Applied Computational Intelligence and Soft Computing

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This paper presents a new metaheuristic named Dhouib-Matrix-3 (DM3) inspired by our recently developed constructive stochastic heuristic Dhouib-Matrix-TSP2 (DM-TSP2) and characterized by only one parameter: the number of iterations. The proposed metaheuristic DM3 is an iterative algorithm in which every iteration is based on two relay hybridization techniques. At first, the constructive stochastic heuristic DM-TSP2 starts by generating a different initial basic feasible solution and then each solution is intensified by the novel procedure Far-to-Near which exchanges far cities by closer ones using three perturbation techniques: insertion, exchange, and 2-opt. Experimental results carried out on the classical travelling salesman problem using the well-known TSP-LIB benchmark instances demonstrate that our approach DM3 outclasses the simulated annealing algorithm, the genetic algorithm, and the cellular genetic algorithm. Furthermore, the proposed DM3 is statistically concurrent to the hybrid simulated annealing cellular genetic algorithm. Nevertheless, DM3 is easier to implement and needs only one parameter to identify (the maximum number of iterations).

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