scholarly journals A Novel Algorithm for Capacitated Vehicle Routing Problem for Smart Cities

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1923
Author(s):  
Mohammad Sajid ◽  
Jagendra Singh ◽  
Raza Abbas Haidri ◽  
Mukesh Prasad ◽  
Vijayakumar Varadarajan ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Vehicle Routing Problem ◽  
Smart Cities ◽  
Real Life ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Smart Logistics ◽  
Complex Optimization ◽  
Capacitated Vehicle

Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph.

scholarly journals An Adapted Version of the Water Wave Optimization Algorithm for the Capacitated Vehicle Routing Problem with Time Windows with Application to a Real Case Using Probe Data

2020 ◽  
Vol 12 (9) ◽  
pp. 3666
Author(s):  
Letizia Tebaldi ◽  
Teresa Murino ◽  
Eleonora Bottani
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Vehicle Routing Problem ◽  
Water Wave ◽  
Time Windows ◽  
Real Case ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

Customers’ habits, as far as shipping requests are concerned, have changed in the last decade, due to the fast spread of e-commerce and business to consumer (B2C) systems, thus generating more and more vehicles on the road, traffic congestion and, consequently, more pollution. Trying to partially solve this problem, the operational research field dedicates part of its research to possible ways to optimize transports in terms of costs, travel times, full loads etc., with the aim of reducing inefficiencies and impacts on profit, planet and people, i.e., the well-known triple bottom line approach to sustainability, also thanks to new technologies able to instantly provide probe data, which can detail information as far as the vehicle behavior. In line with this, an adapted version of the metaheuristic water wave optimization algorithm is here presented and applied to the context of the capacitated vehicle routing problem with time windows. This latter one is a particular case of the vehicle routing problem, whose aim is to define the best route in terms of travel time for visiting a set of customers, given the vehicles capacity and time constraints in which some customers need to be visited. The algorithm is then tested on a real case study of an express courier operating in the South of Italy. A nearest neighbor heuristic is applied, as well, to the same set of data, to test the effectiveness and accuracy of the algorithm. Results show a better performance of the proposed metaheuristic, which could improve the journeys by reducing the travel time by up to 23.64%.

scholarly journals Solving Capacitated Vehicle Routing Problem with Demands as Fuzzy Random Variable

2021 ◽  
Author(s):  
Vishnu Pratap Singh ◽  
Kirti Sharma ◽  
Debjani Chakraborty
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Real Life ◽  
Random Variables ◽  
Fuzzy Random Variables ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Intuitionistic Fuzzy ◽  
Capacitated Vehicle ◽  
Fuzzy Random

Abstract Capacitated vehicle routing problem ( CVRP ) is a classical combinatorial optimization problem in which a network of customers with specified demands is given. The objective is to find a set of routes which originates as well as terminates at the depot node. These routes are to be traversed in such a way that the demands of all the customers in the network are satisfied and the cost associated with traversal of these routes come out to be a minimum. In real-world situations, the demand of any commodity depends upon various uncontrollable factors, such as, season, delivery time, market conditions and many more. Due to these factors, the demand can always not be told in advance and a precise information about the demand is nearly impossible to achieve. Hence, the demands of the customers always experience impreciseness and randomness in real-life. The decisions made by the customers about the demands may also have some scope of hesitation as well. In order to handle such demands of customers in the network, fuzzy random variables and intuitionistic fuzzy random variables are used in this work. The work bridges the gap between the classical version of CVRP and the real-life situation and hence makes it easier for the logistic management companies to determine the routes that should be followed for minimum operational cost and maximum profit. Mathematical models corresponding to CVRP with fuzzy stochastic demands ( CVRPFSD ) and CVRP with Intuitionistic fuzzy stochastic demands ( CVRPIFSD ) have been presented. A two-stage model has been proposed to find out the solution for the same. To explain the working of the methodology defined in this work, two different example of a network with fuzzy and intuitionistic fuzzy demands have been worked out. The proposed solution approach is also tested on modified fuzzy versions of some benchmark instances.

Sign in / Sign up

Export Citation Format

Share Document