Solving vehicle routing problem by using improved genetic algorithm for optimal solution (original) (raw)
Related papers
Development of optimal and cost effective bus scheduling using genetics algorithm
Engineering Today
Most higher institutions in Nigeria operates intra and inter campus transportation, but lack proper planning on movement schedule which contributes significantly to poor academic performance of students as it causes great fatigue due to long queue at the parks and consequently, resulting to losses of revenue to bus management. A met-heuristic algorithm, G.A and L.P model were used to optimize bus scheduling for efficient transportation. To achieve this, travel demands for peak and off-peak seasons at the two campuses of FUTMinna, Nigeria were obtained using 4 numbers of CCTV cameras located at strategic positions. Data were analyzed using the design travel times of 40, 50 and 60 minutes considering traffic and road conditions using 19 numbers of 18 seater bus, 11 numbers of 35 seater bus and 15 numbers of 60seater bus capacities buses with15 minutes departure time. It was observed that with N100/head of revenue charges, a total of N185,000/day corresponding to 231 trips would be ach...
Development of a Genetic Algorithm for the School Bus Routing Problem
The School Bus Routing Problem (SBRP) covers the issue of establishing plans to efficiently transport students distributed across a designated area to the relevant schools using defined resources. As with the similar Vehicle Routing Problem (VRP), the SBRP may have diverse constraints such as heterogeneous vehicles, the allotted time window and multiple depots. Many solutions for effectively solving the problem are currently being studied. By their nature, these routing problems are NP-Hard (non-deterministic polynomial-time hard) problems in which the search domains increase exponentially as they become larger, thus making it difficult to obtain solutions using an exact approach except for relatively simple and localized problems. Therefore the heuristic approach is being studied in many regions. In this study, an algorithm was developed using genetic algorithms, which stem from meta-heuristic algorithms, and the algorithm was tested against diverse problems to identify its performance and practicality.
eprints.utm.my
The Vehicle Routing Problem (VRP) is an important area and has been studied as combinatorial optimization problems. VRP calls for the determination of the optimal set of routes to be performed by a fleet of vehicle to serve a given set of customers. VRP in which demand at each location is unknown at the time when the route is designed but is follow a known probability distribution, is known as VRP with Stochastic Demands (VRPSD). VRPSD finds its application on wide-range of distribution and logisticstransportation sector with the objective is to serve a set of customers at minimum total expected cost. One of the applications of VRPSD is in the case of picking up garbage done by solid waste collection company. The computational complexity of most vehicle routing problem and moreover the intricate of stochastic VRP algorithm has made them an important candidate for solution using metaheuristics. This research proposes the enhanced metaheuristic algorithms that exploit the power of Tabu Search, Genetic Algorithm, and Simulated Annealing for solving VRPSD. Genetic Algorithm as population-based methods are better identifying promising areas in the search space, while Tabu Search and Simulated Annealing as trajectory methods are better in exploring promising areas in search space. Simulated Annealing is a global optimization technique which traverses the search space by generating neighboring solutions of the current solution. A superior neighbor is always accepted and an inferior neighbor is accepted with some probability. Tabu Search is similar to Simulated Annealing, in that both traverse the solution space by testing mutations of an individual solution. However, simulated annealing generates only one mutated solution but Tabu Search generates many mutated solutions and moves
School bus routing using genetic algorithms
Applications of Artificial Intelligence X: Knowledge-Based Systems, 1992
The school bus routing problem involves transporting students from predefined locations to the school using a fleet of school buses with varying capacity. The objective is to minimize the fleet size in addition to minimizing the distance traveled by the buses and the travel time of the students. As the school bus routing problem belongs to the NP-complete class of problems, search strategies based on heuristic methods are most promising for problems in this class. GENROUTER is a system that uses genetic algorithms, an adaptive heuristic search strategy, for routing school buses. The GENROUTER system was used to route school buses for two school districts. The routes obtained by GENROUTER system were superior to those obtained by the Cl-lOOSE school bus routing system and the current routes in use by the two school districts.
Solving the Vehicle Routing Problem using Genetic Algorithm
The main goal of this research is to find a solution of Vehicle Routing Problem using genetic algorithms. The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that belongs to the NP-complete class. Due to the nature of the problem it is not possible to use exact methods for large instances of the VRP. Genetic algorithms provide a search technique used in computing to find true or approximate solution to optimization and search problems. However we used some heuristic in addition during crossover or mutation for tuning the system to obtain better result.
Solution to Vehicle Routing Problem Using Genetic Algorithms
2014
Vehicle routing problem is one of the most challenging areas of research in the field of combinatorial research. This problem is designing optimal set of routes for fleet of vehicles in order to serve a given set of costumers. Our project is mainly concerned about finding the optimized path between source and destination having several intermediate stages. We consider the problem in 2 regards: (a) Transportation View and (b) Personalized view. When coming to transportation, the goal is to maximize the income of the journey that is facilitating maximum people. The key objectives considered there is route constraint that limits the length of all feasible routes. When coming to the personalized view, a travel of a single person is concerned with the objectives like minimum cost, maximum mileage and so on To implement this we are using GA which come under multi objective Optimization techniques (MOO) .We choose to implement this because general conventional methods like heuristic search...
Optimization Approach for Capacitated Vehicle Routing Problem Using Genetic Algorithm
IJSRD, 2013
Vehicle Routing Problem (VRP) is a combinatorial optimization problem which deals with fleet of vehicles to serve n number of customers from a central depot. Each customer has a certain demand that must be satisfied using each vehicle that has the same capacity (homogeneous fleet). Each customer is served by a particular vehicle in such a way that the same customer is not served by another vehicle. In this paper, Genetic Algorithm (GA) is used to get the optimized vehicle route with minimum distance for Capacitated Vehicle Routing Problem (CVRP). The outcomes of GA achieve better optimization and gives good performance. Further, GA is enhanced to minimize the number of vehicles.
Mathematical Problems in Engineering, 2022
Transportation is regarded as one of the most important issues currently being researched; this issue needs the search for approaches or processes that might lessen many contemporary tra c concerns. Congestion, pollution, and accidents have escalated lately, negatively impacting urban environments, economic development, and citizens' lifestyles. e rise of illnesses and epidemics throughout the world, such as COVID-19, has created an urgent need to nd the best way to save people's lives. e vehicle routing problem (VRP) is a well-known moniker for improving transportation systems and is regarded as one of the ancient and contemporary di culties in route planning applications. One of the main tasks of VRP is serving many customers by determining the optimal route from an initial point to a destination on a real-time road map. e best route is not necessarily the shortest-distance route, but, in emergency cases, it is the route that takes the least tness cost (time) and the fastest way to arrive. is paper aims to provide an adaptive genetic algorithm (GA) to determine the optimal time route, taking into account the factors that in uence the vehicle arrival time and cause delays. In addition, the Network Analyst tool in ArcGIS is used to determine the optimal route using real-time map based on the user's preferences and suggest the best one. Experimental results indicate that the performance of GA is mainly determined by an e cient representation, evaluation of tness function, and other factors such as population size and selection method.