A Compressed Annealing Approach with Pre-Process for the Asymmetric Traveling Salesman Problem with Time Windows

2011 ◽  
Vol 5 (5) ◽  
pp. 669-678
Author(s):  
Tadanobu Mizogaki ◽  
◽  
Masao Sugi ◽  
Masashi Yamamoto ◽  
Hidetoshi Nagai ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Feasible Solution ◽  
Time Window ◽  
Time Windows ◽  
Computing Time ◽  
Traveling Salesman ◽  
Asymmetric Traveling Salesman Problem ◽  
Insertion Method ◽  
Time Window Constraints ◽  
The Cost

This paper proposes a method of rapidly finding a feasible solution to the asymmetric traveling salesman problem with time windows (ATSP-TW). ATSP-TW is a problem that involves determining the route with the minimum travel cost for visiting n cities one time each with time window constraints (the period of time in which the city must be visited is constrained). “Asymmetrical” denotes a difference between the cost of outbound and return trips. For such a combinatorial optimization problem with constraints, we propose a method that combines a pre-process based on the insertion method with metaheuristics called “the compressed annealing approach.” In an experiment using a 3-GHz computer, our method derives a feasible solution that satisfies the time window constraints for all of up to about 300 cities at an average of about 1/7 the computing time of existing methods, an average computing time of 0.57 seconds, and a maximum computing time of 9.40 seconds.

scholarly journals 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 ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 110-118
Author(s):  
Herdiesel Santoso ◽  
Rachmad Sanuri
Keyword(s):  
Traveling Salesman Problem ◽  
Time Window ◽  
Travelling Salesman Problem ◽  
Time Windows ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Google Maps

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}.

Time-dependent asymmetric traveling salesman problem with time windows: Properties and an exact algorithm

2019 ◽  
Vol 261 ◽  
pp. 28-39 ◽  
Author(s):  
Anna Arigliano ◽  
Gianpaolo Ghiani ◽  
Antonio Grieco ◽  
Emanuela Guerriero ◽  
Isaac Plana
Keyword(s):  
Traveling Salesman Problem ◽  
Time Windows ◽  
Exact Algorithm ◽  
Traveling Salesman ◽  
Time Dependent ◽  
Asymmetric Traveling Salesman Problem

scholarly journals An Algorithm for Mapping the Asymmetric Multiple Traveling Salesman Problem onto Colored Petri Nets

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 143 ◽  
Author(s):  
Furqan Essani ◽  
Sajjad Haider
Keyword(s):  
Petri Nets ◽  
Traveling Salesman Problem ◽  
Feasible Solution ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Colored Petri Nets ◽  
Cost Matrix ◽  
Multiple Traveling Salesman Problem ◽  
Hard Problems ◽  
The Cost

The Multiple Traveling Salesman Problem is an extension of the famous Traveling Salesman Problem. Finding an optimal solution to the Multiple Traveling Salesman Problem (mTSP) is a difficult task as it belongs to the class of NP-hard problems. The problem becomes more complicated when the cost matrix is not symmetric. In such cases, finding even a feasible solution to the problem becomes a challenging task. In this paper, an algorithm is presented that uses Colored Petri Nets (CPN)—a mathematical modeling language—to represent the Multiple Traveling Salesman Problem. The proposed algorithm maps any given mTSP onto a CPN. The transformed model in CPN guarantees a feasible solution to the mTSP with asymmetric cost matrix. The model is simulated in CPNTools to measure two optimization objectives: the maximum time a salesman takes in a feasible solution and the collective time taken by all salesmen. The transformed model is also formally verified through reachability analysis to ensure that it is correct and is terminating.

scholarly journals Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time Features

2021 ◽  
Vol 13 (22) ◽  
pp. 12906
Author(s):  
Majed G. Alharbi ◽  
Ahmed Stohy ◽  
Mohammed Elhenawy ◽  
Mahmoud Masoud ◽  
Hamiden Abd El-Wahed Khalifa
Keyword(s):  
Traveling Salesman Problem ◽  
Time Window ◽  
State Of The Art ◽  
Time Windows ◽  
Model Performance ◽  
Neural Model ◽  
Traveling Salesman ◽  
Medium Size ◽  
Window Width ◽  
Proposed Model

This paper introduces a time efficient deep learning-based solution to the traveling salesman problem with time window (TSPTW). Our goal is to reduce the total tour length traveled by -*the agent without violating any time limitations. This will aid in decreasing the time required to supply any type of service, as well as lowering the emissions produced by automobiles, allowing our planet to recover from air pollution emissions. The proposed model is a variation of the pointer networks that has a better ability to encode the TSPTW problems. The model proposed in this paper is inspired from our previous work that introduces a hybrid context encoder and a multi attention decoder. The hybrid encoder primarily comprises the transformer encoder and the graph encoder; these encoders encode the feature vector before passing it to the attention decoder layer. The decoder consists of transformer context and graph context as well. The output attentions from the two decoders are aggregated and used to select the following step in the trip. To the best of our knowledge, our network is the first neural model that will be able to solve medium-size TSPTW problems. Moreover, we conducted sensitivity analysis to explore how the model performance changes as the time window width in the training and testing data changes. The experimental work shows that our proposed model outperforms the state-of-the-art model for TSPTW of sizes 20, 50 and 100 nodes/cities. We expect that our model will become state-of-the-art methodology for solving the TSPTW problems.

scholarly journals A Data-Guided Lexisearch Algorithm for the Asymmetric Traveling Salesman Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Zakir Hussain Ahmed
Keyword(s):  
Traveling Salesman Problem ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Computational Time ◽  
Cost Matrix ◽  
Asymmetric Traveling Salesman Problem ◽  
Path Representation ◽  
Random Instances ◽  
Representation Method ◽  
The Cost

A simple lexisearch algorithm that uses path representation method for the asymmetric traveling salesman problem (ATSP) is proposed, along with an illustrative example, to obtain exact optimal solution to the problem. Then a data-guided lexisearch algorithm is presented. First, the cost matrix of the problem is transposed depending on the variance of rows and columns, and then the simple lexisearch algorithm is applied. It is shown that this minor preprocessing of the data before the simple lexisearch algorithm is applied improves the computational time substantially. The efficiency of our algorithms to the problem against two existing algorithms has been examined for some TSPLIB and random instances of various sizes. The results show remarkably better performance of our algorithms, especially our data-guided algorithm.

scholarly journals Efficient Constraint Handling in Electromagnetism-Like Algorithm for Traveling Salesman Problem with Time Windows

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Alkın Yurtkuran ◽  
Erdal Emel
Keyword(s):  
Traveling Salesman Problem ◽  
Time Window ◽  
Time Windows ◽  
Traveling Salesman ◽  
Constraint Handling ◽  
Benchmark Problems ◽  
Multidimensional Space ◽  
Boundary Constraints ◽  
Handling Technique ◽  
The Traveling Salesman Problem

The traveling salesman problem with time windows (TSPTW) is a variant of the traveling salesman problem in which each customer should be visited within a given time window. In this paper, we propose an electromagnetism-like algorithm (EMA) that uses a new constraint handling technique to minimize the travel cost in TSPTW problems. The EMA utilizes the attraction-repulsion mechanism between charged particles in a multidimensional space for global optimization. This paper investigates the problem-specific constraint handling capability of the EMA framework using a new variable bounding strategy, in which real-coded particle’s boundary constraints associated with the corresponding time windows of customers, is introduced and combined with the penalty approach to eliminate infeasibilities regarding time window violations. The performance of the proposed algorithm and the effectiveness of the constraint handling technique have been studied extensively, comparing it to that of state-of-the-art metaheuristics using several sets of benchmark problems reported in the literature. The results of the numerical experiments show that the EMA generates feasible and near-optimal results within shorter computational times compared to the test algorithms.

scholarly journals Using genetic algorithm to solve multiple traveling salesman problem and considering Carbon emissions

2020 ◽  
Vol 13 (36) ◽  
pp. 3707-3715
Author(s):  
Chris Jojo Obi ◽  
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Carbon Emission ◽  
Travelling Salesman Problem ◽  
Time Windows ◽  
Traveling Salesman ◽  
Transport Cost ◽  
Travelling Salesman ◽  
Insertion Method ◽  
Multiple Travelling Salesman Problem

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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