A variable iterated greedy algorithm for the traveling salesman problem with time windows

Karabulut, Korhan; Taşgetiren, M. Fatih

doi:10.1016/j.ins.2014.03.127

Cited by 52 publications

(22 citation statements)

References 41 publications

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“…The initial population procedure aims to generate diversified solutions with good quality. Specifically, in order to obtain an initial solution S, which consists of several routes, we present a random greedy construction method inspired from the iterated greedy construction procedure in [24] given as follows:…”

Section: Initial Population Phasementioning

confidence: 99%

Solving the Multi-Depot Green Vehicle Routing Problem by a Hybrid Evolutionary Algorithm

Peng

et al. 2020

Sustainability

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The growing concerns about human pollution has motivated practitioners and researchers to focus on the environmental and social impacts of logistics and supply chains. In this paper, we consider the environmental impact of carbon dioxide emission on a vehicle routing problem with multiple depots. We present a hybrid evolutionary algorithm (HEA) to tackle it by combining a variable neighborhood search and an evolutionary algorithm. The proposed hybrid evolutionary algorithm includes several distinct features such as multiple neighborhood operators, a route-based crossover operator, and a distance- and quality-based population updating strategy. The results from our numerical experiments confirm the effectiveness and superiority of the proposed HEA in comparison with the best-performing methods in the literature and the public exact optimization solver CPLEX. Furthermore, an important aspect of the HEA is studied to assess its effect on the performance of the HEA.

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Section: Initial Population Phasementioning

confidence: 99%

Solving the Multi-Depot Green Vehicle Routing Problem by a Hybrid Evolutionary Algorithm

Peng

et al. 2020

Sustainability

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“…Given any solution s, the basic structure of procedure Two Route Exchange (s) is an extension to the variable iterated greedy algorithm for a TSPTW in Karabulut and Tasgetiren (2014). That is, given each pair of different routes in the solution s, a Two Route Destruct Construct (k 1 ,k 2 ) procedure similar to the Destruct Construct () in Karabulut and Tasgetiren (2014) is applied to exchange k 1 number of customers from route one with the k 2 number of customers from route two in order to generate two new routes. The two new routes are treated as independent TSPTW problems and are optimised using the VNS_1_Opt local search of Karabulut and Tasgetiren (2014).…”

Section: Local Searchmentioning

confidence: 99%

Multi-objective vehicle routing and loading with time window constraints: a real-life application

et al. 2019

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Motivated by a real-life application, this research considers the multi-objective vehicle routing and loading problem with time window constraints which is a variant of the Capacitated Vehicle Routing Problem with Time Windows with one/two-dimensional loading constraints. The problem consists of routing a number of vehicles to serve a set of customers and determining the best way of loading the goods ordered by the customers onto the vehicles used for transportation. The three objectives pertaining to minimisation of total travel distance, number of routes to use and total number of mixed orders in the same pallet are, more often than not, conflicting. To achieve a solution with no preferential information known in advance from the decision maker, the problem is formulated as a Mixed Integer Linear Programming (MILP) model with one objective-minimising the total cost, where the three original objectives are incorporated as parts of the total cost function. A Generalised Variable Neighbourhood Search (GVNS) algorithm is designed as the search engine to relieve the computational burden inherent to the application of the MILP model. To evaluate the effectiveness of the GVNS algorithm, a real instance case study is generated and solved by both the GVNS algorithm and the software provided by our industrial partner. The results show that the suggested approach provides solutions with better overall values than those found by the software provided by our industrial partner.

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“…We also consider the FFO algorithm (namely, nFFO) presented for the semiconductor final testing scheduling problem in Zheng et al [32], the multi-restart iterated local search of Dong et al (ILSD for short, where the capital letter D is taken from authors' name) [24] for the permutation flowshop problem, and the variable iterated greedy (vIG) for the traveling salesman problem with time windows by Karaulut and Tasgetiren [52]. We change the way that the makespan is calculated by the one shown in Section 4.1.1 for the above adapted algorithms.…”

Section: Computational Evaluationmentioning

confidence: 99%

Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times

Pan

Gao

2017

Applied Mathematics and Computation

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This paper proposes a total of nine algorithms to minimize the makespan for the hybrid flowshop scheduling problem with sequence-dependent setup times. The first six algorithms are trajectory-based metaheuristics, including three variants of iterated local search and three variants of iterated greedy. The remaining three algorithms are population-based metaheuristics, namely, the improved fruit fly optimization, the improved migrating birds optimization, and the discrete artificial bee colony optimization. We present some advanced and effective technologies, including three mixed neighborhood structures, an enhanced perturbation method, and an enhanced destruction and construction procedure for the trajectory-based metaheuristics. We propose a path-relinking-based cooperative search, a diversity control scheme, and a diversified initialization approach for the improved fruit fly optimization. We calibrate the parameters and operators for the proposed algorithms by means of a design of experiments approach. To evaluate the proposed algorithms, we present several adaptations of other recent well-known meta-heuristics for the problem and conduct a comprehensive set of computational and statistical experiments to demonstrate the effectiveness of the presented algorithms. Among them, the discrete artificial bee colony optimization is the best-performing algorithm and it is able to improve 126 out of the 240 best known solutions for the benchmarks in the literature.

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A variable iterated greedy algorithm for the traveling salesman problem with time windows

Cited by 52 publications

References 41 publications

Solving the Multi-Depot Green Vehicle Routing Problem by a Hybrid Evolutionary Algorithm

Solving the Multi-Depot Green Vehicle Routing Problem by a Hybrid Evolutionary Algorithm

Multi-objective vehicle routing and loading with time window constraints: a real-life application

Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times

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