scholarly journals Evaluation of TSP for Emergency Routing

2021 ◽  
Vol 13 (1) ◽  
pp. 44-53
Author(s):  
A. G. M. Zaman ◽  
◽  
Sajib Hasan ◽  
Mohammad Samawat Ulla
Keyword(s):  
Nearest Neighbor ◽  
Route Planning ◽  
Traveling Salesman ◽  
Route Optimization ◽  
Parallel Optimization ◽  
Heuristic Methods ◽  
Approximate Methods ◽  
Symmetric Traveling Salesman Problem ◽  
Computer Codes ◽  
Best Fit

The paper considers the symmetric traveling salesman problem and applies it to sixty-four (64) districts of Bangladesh (with geographic coordinates) as a new instance of the problem of finding an optimized route in need of emergency. It approached three different algorithms namely Integer Linear Programming, Nearest-neighbor, and Metric TSP as exact, heuristic, or approximate methods of solving the NP-hard class of problem to model the emergency route planning. These algorithms have been implanted using computer codes, used IBM ILOG CPLEX parallel optimization, visualized using Geographic Information System tools. The performance of these algorithms also has been evaluated in terms of computational complexity, their run-time, and resulted tour distance using exact, approximate, and heuristic methods to find the best fit of route optimization in emergence thus contributing to the field of combinatorial optimization.

scholarly journals Tourist Route Optimization in the Context of Covid-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5492
Author(s):  
Cristina Maria Păcurar ◽  
Ruxandra-Gabriela Albu ◽  
Victor Dan Păcurar
Keyword(s):  
Travelling Salesman Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Travelling Salesman ◽  
Social Distancing ◽  
Tourist Destinations ◽  
Innovative Method ◽  
The Social ◽  
Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

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

2021 ◽  
Vol 25 (8) ◽  
pp. 6665-6680
Author(s):  
Krzysztof Szwarc ◽  
Piotr Nowakowski ◽  
Urszula Boryczka
Keyword(s):  
Tabu Search ◽  
Quality Evaluation ◽  
Memetic Algorithm ◽  
Route Planning ◽  
Memetic Algorithms ◽  
Approximate Methods ◽  
On Demand ◽  
Suboptimal Solutions ◽  
Novel Method ◽  
The Given

AbstractThe article discusses the utilitarian problem of the mobile collection of waste electrical and electronic equipment. Due to its $$\mathcal {NP}$$ NP -hard nature, implies the application of approximate methods to discover suboptimal solutions in an acceptable time. The paper presents the proposal of a novel method of designing the Evolutionary and Memetic Algorithms, which determine favorable route plans. The recommended methods are determined using quality evaluation indicators for the techniques applied herein, subject to the limits characterizing the given company. The proposed Memetic Algorithm with Tabu Search provides much better results than the metaheuristics described in the available literature.

Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

2021 ◽  
pp. 1074-1115
Author(s):  
Victer Paul ◽  
Ganeshkumar C ◽  
Jayakumar L
Keyword(s):  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Population Based ◽  
Vital Role ◽  
Traveling Salesman ◽  
Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

An Approximate Algorithm for Triangle TSP with a Four-Vertex-Three-Line Inequality

2015 ◽  
Vol 6 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Yong Wang
Keyword(s):  
Time Complexity ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Optimal Solution ◽  
Simple Algorithm ◽  
Exact Algorithms ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Approximate Algorithm ◽  
Nearest Neighbor Algorithm

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. The time complexity of the exact algorithms is generally an exponential function of the scale of TSP. This work gives an approximate algorithm with a four-vertex-three-line inequality for the triangle TSP. The time complexity is O(n2) and it can generate an approximation less than 2 times of the optimal solution. The paper designs a simple algorithm with the inequality. The algorithm is compared with the double-nearest neighbor algorithm. The experimental results illustrate the algorithm find the better approximations than the double-nearest neighbor algorithm for most TSP instances.

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