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Journal articles on the topic 'Travelling salesman problem with time windows'

Author: Grafiati

Published: 28 June 2021

Last updated: 12 February 2022

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1

Obi, Chris Jojo. "Using genetic algorithm to solve multiple traveling salesman problem and considering Carbon emissions." Indian Journal of Science and Technology 13, no. 36 (2020): 3707–15. http://dx.doi.org/10.17485/ijst/v13i36.1316.

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Objectives: The Multiple Travelling Salesman problem is a complex combinatorial optimization problem which is a variance of the Traveling Salesman Problem,where a lot of salesmen are utilized in the solution. In this work a cold chain logistics and route optimization model with minimum transport cost, carbon cost and Refrigeration cost are constructed. Methods: A genetic algorithm is then proposed to solve for the Multiple Travelling Salesman Problem with time windows while transport cost, carbon emission cost and refrigeration cost is minimized. Findings: It was observed that the algorithm evolved towards the direction of the optimal value of the fitness function. Novelty: There are a number of studies that considered tournament selection strategy but just a few have applied genetic algorithm considering insertion method to solve a Multiple Travelling salesman Problem. This study uses insertion method to obtain optimal solution. Also, the researcher considered time windows, transport cost, carbon emission cost and refrigeration cost. Keywords: Genetic algorithm method; cold-logistics; multiple travelling salesman problem

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2

López-Ibáñez, Manuel, and Christian Blum. "Beam-ACO for the travelling salesman problem with time windows." Computers & Operations Research 37, no. 9 (2010): 1570–83. http://dx.doi.org/10.1016/j.cor.2009.11.015.

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3

Mladenovic, Nenad, Raca Todosijevic, and Dragan Urosevic. "An efficient General Variable Neighborhood Search for large Travelling Salesman Problem with Time Windows." Yugoslav Journal of Operations Research 23, no. 1 (2013): 19–30. http://dx.doi.org/10.2298/yjor120530015m.

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General Variable Neighborhood Search (GVNS) is shown to be a powerful and robust methodology for solving travelling salesman and vehicle routing problems. However, its efficient implementation may play a significant role in solving large size instances. In this paper we suggest new GVNS heuristic for solving Travelling salesman problem with time windows. It uses different set of neighborhoods, new feasibility checking procedure and a more efficient data structure than the recent GVNS method that can be considered as a state-of-the-art heuristic. As a result, our GVNS is much faster and more effective than the previous GVNS. It is able to improve 14 out of 25 best known solutions for large test instances from the literature.

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4

Tae, Hyun-Chul, and Byung-In Kim. "Dynamic Programming Approach for Prize Colleting Travelling Salesman Problem with Time Windows." IE interfaces 24, no. 2 (2011): 112–18. http://dx.doi.org/10.7232/ieif.2011.24.2.112.

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5

Ascheuer, Norbert, Matteo Fischetti, and Martin Grötschel. "Solving the Asymmetric Travelling Salesman Problem with time windows by branch-and-cut." Mathematical Programming 90, no. 3 (2001): 475–506. http://dx.doi.org/10.1007/pl00011432.

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6

López-Ibáñez, Manuel, Christian Blum, Jeffrey W. Ohlmann, and Barrett W. Thomas. "The travelling salesman problem with time windows: Adapting algorithms from travel-time to makespan optimization." Applied Soft Computing 13, no. 9 (2013): 3806–15. http://dx.doi.org/10.1016/j.asoc.2013.05.009.

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7

Cheng, Chi-Bin, and Chun-Pin Mao. "A modified ant colony system for solving the travelling salesman problem with time windows." Mathematical and Computer Modelling 46, no. 9-10 (2007): 1225–35. http://dx.doi.org/10.1016/j.mcm.2006.11.035.

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8

Hill, Stephen E., and Marco Lam. "A teaching exercise for the travelling salesman problem with time windows using real-world data." International Journal of Information and Operations Management Education 5, no. 4 (2014): 363. http://dx.doi.org/10.1504/ijiome.2014.067566.

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9

Budak, Gerçek, and Xin Chen. "Evaluation of the size of time windows for the travelling salesman problem in delivery operations." Complex & Intelligent Systems 6, no. 3 (2020): 681–95. http://dx.doi.org/10.1007/s40747-020-00167-y.

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10

Baltz, Andreas, Mourad El Ouali, Gerold Jäger, Volkmar Sauerland, and Anand Srivastav. "Exact and heuristic algorithms for the Travelling Salesman Problem with Multiple Time Windows and Hotel Selection." Journal of the Operational Research Society 66, no. 4 (2015): 615–26. http://dx.doi.org/10.1057/jors.2014.17.

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11

MIZOGAKI, Tadanobu, Masao SUGI, Masashi YAMAMOTO, Hidetoshi NAGAI, Yusuke SHIOMI, and Jun OTA. "S142011 A Solution for Asymmetric Travelling Salesman Problem with Time Windows Using Pre-Processed Compressed Annealing." Proceedings of Mechanical Engineering Congress, Japan 2011 (2011): _S142011–1—_S142011–5. http://dx.doi.org/10.1299/jsmemecj.2011._s142011-1.

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12

Yuliastuti, Gusti Eka, Wayan Firdaus Mahmudy, and Agung Mustika Rizki. "Penanganan Fuzzy Time Window pada Travelling Salesman Problem (TSP) dengan Penerapan Algoritma Genetika." MATICS 9, no. 1 (2017): 38. http://dx.doi.org/10.18860/mat.v9i1.4072.

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<p class="Text"><strong><span lang="EN-US">The route of the travel tour packages offered by travel agents is not considered optimum, so the level of satisfaction the tourist is not maximal. Selection of the route of the travel packages included in the traveling salesman problem (TSP). The problem that occurs is uncertain tourists visiting destinations at the best destinations timing hereinafter be referred to as the fuzzy time window problem. Therefore, the authors apply the genetic algorithm to solve the problem. Based on test results obtained optimum solution with the fitness value of 1.3291, a population size of 100, the number of generations of 1000, a combination of CR=0,4 and MR=0.6.</span></strong></p>

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13

Küçükoğlu, İlker, Reginald Dewil, and Dirk Cattrysse. "Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates." Expert Systems with Applications 134 (November 2019): 279–303. http://dx.doi.org/10.1016/j.eswa.2019.05.037.

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14

Santoso, Herdiesel, and Rachmad Sanuri. "Implementasi Algoritma Genetika dan Google Maps API Dalam Penyelesaian Traveling Salesman Problem with Time Window (TSP-TW) Pada Penjadwalan Rute Perjalanan Divisi Pemasaran STMIK El Rahma." Teknika 8, no. 2 (2019): 110–18. http://dx.doi.org/10.34148/teknika.v8i2.187.

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Divisi pemasaran STMIK El Rahma memiliki permasalahan dengan penjadwalan rute kunjungan ketika harus melakukan perjalanan multi destinasi ke sekolah-sekolah untuk melakukan promosi. Perjalanan multi destinasi dengan mempertimbangkan waktu kunjungan merupakan permasalahan Travelling Salesman Problem with Time Windows (TSP-TW). Algoritma Genetika merupakan salah satu metode pencarian yang dapat digunakan untuk memberikan rute perjalanan yang optimal. Rekomendasi yang diberikan tidak hanya mempertimbangkan jarak tetapi juga waktu tempuh didapatkan menggunakan Google Maps API. Skenario pengujian yang dilakukan adalah pengujian banyak generasi optimal, pengujian banyak populasi optimal, pengujian kombinasi probabilitas crossover (Pc) dan proabilitas mutasi (Pm), serta pengujian konsistensi solusi yang dihasilkan Algoritma Genetika. Hasil pengujian menunjukan bahwa jumlah individu terbaik adalah 150 individu dalam satu populasi. Kriteria berhenti jika setelah 127 generasi berturut-turut didapatkan nilai fitness tertinggi yang tidak berubah dan kombinasi probabilitas crossover dan probabilitas mutasi yang paling optimal adalah {0.3 : 0.7}.

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15

Salomon, Marc, Marius M. Solomon, Luk N. Van Wassenhove, Yvan Dumas, and Stephane Dauzère-Pérès. "Solving the discrete lotsizing and scheduling problem with sequence dependent set-up costs and set-up times using the Travelling Salesman Problem with time windows." European Journal of Operational Research 100, no. 3 (1997): 494–513. http://dx.doi.org/10.1016/s0377-2217(96)00020-3.

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16

Fagerholt, K., and M. Christiansen. "A travelling salesman problem with allocation, time window and precedence constraints — an application to ship scheduling." International Transactions in Operational Research 7, no. 3 (2000): 231–44. http://dx.doi.org/10.1111/j.1475-3995.2000.tb00196.x.

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17

Bhavani, V., and M. Sundara Murthy. "Time-Dependent Travelling Salesman Problem." OPSEARCH 42, no. 3 (2005): 199–227. http://dx.doi.org/10.1007/bf03398730.

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18

Srivastav, Yatharth, and J. K. Saini. "Comparative Study on Travelling Salesman Problem." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 853–57. http://dx.doi.org/10.51201/jusst/21/07236.

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Travelling Salesman Problem (TSP) is a kind of LPP to find a minimum cost sequence in order to travel in each set of cities in a way that starting as well as ending should be on the same city and each city is visited exactly one time. In this paper, we will compare different optimization algorithms working principles, and we will also discuss the advantages and limitations of all the optimization techniques.

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19

Roberti, R., and M. Wen. "The Electric Traveling Salesman Problem with Time Windows." Transportation Research Part E: Logistics and Transportation Review 89 (May 2016): 32–52. http://dx.doi.org/10.1016/j.tre.2016.01.010.

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20

Voccia, Stacy A., Ann M. Campbell, and Barrett W. Thomas. "The probabilistic traveling salesman problem with time windows." EURO Journal on Transportation and Logistics 2, no. 1-2 (2013): 89–107. http://dx.doi.org/10.1007/s13676-013-0018-0.

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21

Kalaiarasi, S., and P. Sriramya. "Seed based plant propagation algorithm for multiple travelling salesman problem." International Journal of Engineering & Technology 7, no. 3.3 (2018): 515. http://dx.doi.org/10.14419/ijet.v7i2.33.14823.

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Multiple Travelling Salesman Problem is a complex problem in which route for a salesman is assigned to visit a city that has various hurdles such as congested road, damaged road, etc. In recent years biologically inspired algorithms are most widely used to solve many optimization problems. Here seed based plant propagation algorithm is applied for the multiple travelling salesman problem that is also a optimization problem, and the result is compared with a short-cut routing algorithm. The result shows that Seed based Propagation Algorithm is easy to implement since it has few parameters to be utilized and also time complexity is reduced when implemented in multiple travelling salesman problem.

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22

Kaliberda, E. A., M. Yu Gunenkov, D. S. Dyusekenov, and I. V. Fedotova. "ANT ALGORITHM FOR SOLVING TRAVELLING SALESMAN PROBLEM." Applied Mathematics and Fundamental Informatics 7, no. 2 (2020): 010–17. http://dx.doi.org/10.25206/2311-4908-2020-7-2-10-17.

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The paper provides a brief theoretical overview of existing approaches to solving travelling salesman problem. The software implementation of the problem solution using the ant algorithm is performed. The analysis of the time efficiency of the algorithm on different volumes of input data is carried out. Dependency graph is plotted. Further directions of studying this algorithm are determined.

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23

Hatamlou, Abdolreza. "Solving Travelling Salesman Problem Using Heart Algorithm." International Journal of Applied Evolutionary Computation 8, no. 4 (2017): 32–42. http://dx.doi.org/10.4018/ijaec.2017100103.

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Solving hard problems like the Travelling Salesman Problem (TSP) is a major challenge faced by analysts even though many techniques are available. The main goal of TSP is that a number of cities should be visited by a salesman and return to the starting city along a number of possible shortest paths. TSP is although looking a simple problem, but it is an important problem of the classical optimization problems that are difficult to solve conventionally. It has been proved that solving TSP by the conventional approaches in a reasonable time is not possible. So, the only feasible option left is to use heuristic algorithms. In this article, we apply and investigate a new heuristic approach called Heart algorithm to solve Travelling Salesman Problem. It simulates the heart action and circulatory system procedure in the human beings for searching the problem space. The Heart algorithm has the advantages of strong robustness, fast convergence, fewer setting parameters and simplicity. The results of the Heart algorithm on several standard TSP instances is compared with the results of the PSO and ACO algorithms and show that the Heart algorithm performs well in finding the shortest distance within the minimum span of time.

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24

Garai, Arindam, and Tapan Kumar Roy. "Intuitionistic Fuzzy Modeling to Travelling Salesman Problem." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 9 (2013): 3015–24. http://dx.doi.org/10.24297/ijct.v11i9.3414.

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This paper presents solution technique for travelling salesman problem (TSP) under intuitionistic fuzzy environment. Travelling salesman problemÂ is a non-deterministic polynomial-time (NP) hardÂ problem inÂ combinatorial optimization,Â studied inÂ graph theory, operations researchÂ andÂ theoretical computer science. It must be noted that a traveling sales man even face a situation in which he is not able to achieve his objectives completely. There must be a set of alternatives from which he can select one that best meets his aspiration level. For Multi-Objective Symmetric TSP, in fuzzy environment, it is converted into a Linear Program using Fuzzy Multi-Objective Linear Programming technique. A route cannot be simply chosen just as it will most minimize time or it will cover the least possible distance. Examples with requirements to consider the degree of rejection or hesitation (or both) are overflowing in our materialistic world. Here comes the need to consider TSP under intuitionistic fuzzy environment. The degree of rejection as well as the degree of hesitancy must be studied to find the solution in a truly optimum sense! Proposed technique is an extension as well as collaboration of ideas of fuzzy traveling salesperson problem and intuitionistic fuzzy (IF) optimization technique.

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25

Zharfi, Vahid, and Abolfazl Mirzazadeh. "A Novel Metaheuristic for Travelling Salesman Problem." Journal of Industrial Engineering 2013 (July 18, 2013): 1–5. http://dx.doi.org/10.1155/2013/347825.

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One of the well-known combinatorial optimization problems is travelling salesman problem (TSP). This problem is in the fields of logistics, transportation, and distribution. TSP is among the NP-hard problems, and many different metaheuristics are used to solve this problem in an acceptable time especially when the number of cities is high. In this paper, a new meta-heuristic is proposed to solve TSP which is based on new insight into network routing problems.

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26

Eremeev, Anton, and Yulia Kovalenko. "On solving Travelling Salesman Problem with Vertex Requisitions." Yugoslav Journal of Operations Research 27, no. 4 (2017): 415–26. http://dx.doi.org/10.2298/yjor161012003e.

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We consider the Travelling Salesman Problem with Vertex Requisitions where, for each position of the tour, at most two possible vertices are given. It is known that the problem is strongly NP-hard. The algorithm, we propose for this problem, has less time complexity compared to the previously known one. In particular, almost all feasible instances of the problem are solvable in O(n) time using the new algorithm, where n is the number of vertices. The developed approach also helps in fast enumeration of a neighborhood in the local search and yields an integer programming model with O(n) binary variables for the problem.

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27

Larsen, Allan, Oli B. G. Madsen, and Marius M. Solomon. "The A Priori Dynamic Traveling Salesman Problem with Time Windows." Transportation Science 38, no. 4 (2004): 459–72. http://dx.doi.org/10.1287/trsc.1030.0070.

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28

Chang, Tsung-Sheng, Yat-wah Wan, and Wei Tsang OOI. "A stochastic dynamic traveling salesman problem with hard time windows." European Journal of Operational Research 198, no. 3 (2009): 748–59. http://dx.doi.org/10.1016/j.ejor.2008.10.012.

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29

Doppstadt, Christian, Achim Koberstein, and Daniele Vigo. "The Hybrid Electric Vehicle—Traveling Salesman Problem with time windows." European Journal of Operational Research 284, no. 2 (2020): 675–92. http://dx.doi.org/10.1016/j.ejor.2019.12.031.

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30

Vu, Duc Minh, Mike Hewitt, Natashia Boland, and Martin Savelsbergh. "Dynamic Discretization Discovery for Solving the Time-Dependent Traveling Salesman Problem with Time Windows." Transportation Science 54, no. 3 (2020): 703–20. http://dx.doi.org/10.1287/trsc.2019.0911.

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We present a new solution approach for the time-dependent traveling salesman problem with time windows. This problem considers a salesman who departs from his home, has to visit a number of cities within a predetermined period of time, and then, returns home. The problem allows for travel times that can depend on the time of departure. We consider two objectives for the problem: (1) a makespan objective that seeks to return the salesman to his home as early as possible and (2) a duration objective that seeks to minimize the amount of time that he is away from his home. The solution approach is based on an integer programming formulation of the problem on a time-expanded network, because doing so enables time dependencies to be embedded in the definition of the network. However, because such a time-expanded network (and thus, the integer programming formulation) can rapidly become prohibitively large, the solution approach uses a dynamic discretization discovery framework, which has been effective in other contexts. Our computational results indicate that the solution approach outperforms the best-known methods on benchmark instances and is robust with respect to instance parameters.

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31

Sakiyama, Tomoko, and Ikuo Arizono. "Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem." Complexity 2018 (December 6, 2018): 1–5. http://dx.doi.org/10.1155/2018/6498218.

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In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions.

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32

Kumar, Ch S. N. Chandra Sunil, and T. T. Narendran. "Heuristics for the Real-time Pickup-and-Delivery Travelling Salesman Problem." International Journal of Logistics Systems and Management 4, no. 1 (2008): 116. http://dx.doi.org/10.1504/ijlsm.2008.015931.

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33

Miranda-Bront, Juan José, Isabel Méndez-Díaz, and Paula Zabala. "Facets and valid inequalities for the time-dependent travelling salesman problem." European Journal of Operational Research 236, no. 3 (2014): 891–902. http://dx.doi.org/10.1016/j.ejor.2013.05.022.

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34

Kaspi, Moshe, Moshe Zofi, and Ron Teller. "Maximizing the profit per unit time for the travelling salesman problem." Computers & Industrial Engineering 135 (September 2019): 702–10. http://dx.doi.org/10.1016/j.cie.2019.06.050.

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35

Adamo, Tommaso, Gianpaolo Ghiani, and Emanuela Guerriero. "An enhanced lower bound for the Time-Dependent Travelling Salesman Problem." Computers & Operations Research 113 (January 2020): 104795. http://dx.doi.org/10.1016/j.cor.2019.104795.

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36

Dash, Sanjeeb, Oktay Günlük, Andrea Lodi, and Andrea Tramontani. "A Time Bucket Formulation for the Traveling Salesman Problem with Time Windows." INFORMS Journal on Computing 24, no. 1 (2012): 132–47. http://dx.doi.org/10.1287/ijoc.1100.0432.

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37

Shirafkan, Mohammad Taher, Hany Seidgar, and Nikbakhsh Javadian. "A new mathematical model for non-fixed destination multi-depot multiple travelling salesmen with time window problem." International Journal of Services and Operations Management 31, no. 4 (2018): 530. http://dx.doi.org/10.1504/ijsom.2018.10017434.

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38

Shirafkan, Mohammad Taher, Hany Seidgar, and Nikbakhsh Javadian. "A new mathematical model for non-fixed destination multi-depot multiple travelling salesmen with time window problem." International Journal of Services and Operations Management 31, no. 4 (2018): 530. http://dx.doi.org/10.1504/ijsom.2018.096167.

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39

Papalitsas, Christos, Theodore Andronikos, Konstantinos Giannakis, Georgia Theocharopoulou, and Sofia Fanarioti. "A QUBO Model for the Traveling Salesman Problem with Time Windows." Algorithms 12, no. 11 (2019): 224. http://dx.doi.org/10.3390/a12110224.

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This work focuses on expressing the TSP with Time Windows (TSPTW for short) as a quadratic unconstrained binary optimization (QUBO) problem. The time windows impose time constraints that a feasible solution must satisfy. These take the form of inequality constraints, which are known to be particularly difficult to articulate within the QUBO framework. This is, we believe, the first time this major obstacle is overcome and the TSPTW is cast in the QUBO formulation. We have every reason to anticipate that this development will lead to the actual execution of small scale TSPTW instances on the D-Wave platform.

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40

Sakiyama, Tomoko, and Ikuo Arizono. "Can the Agent with Limited Information Solve Travelling Salesman Problem?" Complexity 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/9562125.

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Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems.

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41

Chen, Hao-Xiang, Ying Nan, and Yi Yang. "Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm." Sensors 19, no. 3 (2019): 734. http://dx.doi.org/10.3390/s19030734.

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This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

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42

Mladenović, Nenad, Raca Todosijević, and Dragan Urošević. "An efficient GVNS for solving Traveling Salesman Problem with Time Windows." Electronic Notes in Discrete Mathematics 39 (December 2012): 83–90. http://dx.doi.org/10.1016/j.endm.2012.10.012.

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43

Calvo, Roberto Wolfler. "A New Heuristic for the Traveling Salesman Problem with Time Windows." Transportation Science 34, no. 1 (2000): 113–24. http://dx.doi.org/10.1287/trsc.34.1.113.12284.

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44

Dumas, Yvan, Jacques Desrosiers, Eric Gelinas, and Marius M. Solomon. "An Optimal Algorithm for the Traveling Salesman Problem with Time Windows." Operations Research 43, no. 2 (1995): 367–71. http://dx.doi.org/10.1287/opre.43.2.367.

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45

Carlton, William B., and J. Wesley Barnes. "Solving the Traveling-Salesman Problem with Time Windows Using Tabu Search." IIE Transactions 28, no. 8 (1996): 617–29. http://dx.doi.org/10.1080/15458830.1996.11770707.

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46

Papalitsas, Christos, and Theodore Andronikos. "Unconventional GVNS for Solving the Garbage Collection Problem with Time Windows." Technologies 7, no. 3 (2019): 61. http://dx.doi.org/10.3390/technologies7030061.

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GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard GVNS that borrows principles from quantum computing during the shaking stage. The Traveling Salesman Problem with Time Windows (TSP-TW) is a characteristic NP-hard variation in the standard Traveling Salesman Problem. One can utilize TSP-TW as the basis of Global Positioning System (GPS) modeling and routing. The focus of this work is the study of the possible advantages that the proposed unconventional GVNS may offer to the case of garbage collector trucks GPS. We provide an in-depth presentation of our method accompanied with comprehensive experimental results. The experimental information gathered on a multitude of TSP-TW cases, which are contained in a series of tables, enable us to deduce that the novel GVNS approached introduced here can serve as an effective solution for this sort of geographical problems.

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Bang, Ban Ha. "EFFICIENT METAHEURISTIC ALGORITHMS FOR THE MULTI-STRIPE TRAVELLING SALESMAN PROBLEM." Journal of Computer Science and Cybernetics 36, no. 3 (2020): 233–50. http://dx.doi.org/10.15625/1813-9663/36/3/14770.

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The Multi-stripe Travelling Salesman Problem (Ms-TSP) is an extension of the Travelling Salesman Problem (TSP). In the \textit{q}-stripe TSP with $q \geq 1$, the objective function sums the costs for travelling from one customer to each of the next \textit{q} customers along the tour. The resulting \textit{q}-stripe TSP generalizes the TSP and forms a special case of the Quadratic Assignment Problem. To solve medium and large size instances, a metaheuristic algorithm is proposed. The proposed algorithm has two main components, which are construction and improvement phases. The construction phase generates a solution using Greedy Randomized Adaptive Search Procedure (GRASP) while the optimization phase improves the solution with several variants of Variable Neighborhood Search, both coupled with a technique called Shaking Technique to escape from local optima. In addition, Adaptive Memory is integrated into our algorithms to balance between the diversification and intensification. To show the efficiency of our proposed metaheuristic algorithms, we extensively experiment on benchmark instances. The results indicate that the developed algorithms can produce efficient and effective solutions at a reasonable computation time.

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Arigliano, Anna, Tobia Calogiuri, Gianpaolo Ghiani, and Emanuela Guerriero. "A branch-and-bound algorithm for the time-dependent travelling salesman problem." Networks 72, no. 3 (2018): 382–92. http://dx.doi.org/10.1002/net.21830.

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Juwairiah, Juwairiah, Dicky Pratama, Heru Cahya Rustamaji, Herry Sofyan, and Dessyanto Boedi Prasetyo. "Genetic Algorithm for Optimizing Traveling Salesman Problems with Time Windows (TSP-TW)." International Journal of Artificial Intelligence & Robotics (IJAIR) 1, no. 1 (2019): 1. http://dx.doi.org/10.25139/ijair.v1i1.2024.

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The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.

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Sekhar, Tirumalasetti Guna. "An efficient approach for solving Travelling Sales Man Problem." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2793——2798. http://dx.doi.org/10.22214/ijraset.2021.37838.

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Abstract: Global Position System (GPS) application is quite possibly the most valuable instrument in transportation the executives nowadays. The Roadway transportation is an significant function of GPS. To track down the briefest courses to a spot is the key issue of organization investigation. To address this issue, we have numerous calculations and procedures like Dijkstra algorithm, Ant Colony Optimization, Bellman Portage Algorithm, Floyd-Warshall algorithm, Genetic Algorithm, A* Algorithm furthermore, numerous others. In this paper our fundamental goal is to assess the brute force algorithm and the dynamic programming algorithm in settling the Shortest path issue (The travelling salesman issue). The paper will be finished up by giving the results of time and space complexity of these algorithms. To help a salesman visit every one of the urban communities in the rundown (giving the area of urban areas as the information) and he knows the area of the multitude of urban communities and track down the shortest path with the end goal that he visits every one of the urban areas just a single time and gets back to the city where he begun. The distance (cost) and the relating way ought to be shown as yield.

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