A Hybrid Approach Based on LP Metric Method and Genetic Algorithm for the Vehicle-Routing Problem with Time Windows, Driver-Specific Times, and Vehicles-Specific Capacities

2018 ◽  
Vol 9 (4) ◽  
pp. 51-67 ◽  
Author(s):  
Ebrahim Asadi-Gangraj ◽  
Sina Nayeri
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Approach ◽  
Mixed Integer ◽  
Computational Time ◽  
Routing Problem ◽  
Lp Metric

Due to increasing population, increasing number of vehicles as well as environmental pollution, planning vehicles efficiently one of important problems nowadays. This article proposes a Multi-Objective Mixed Integer Programming (MOMIP) model for the vehicle-routing problem with time windows, driver-specific times and vehicles-specific capacities (VRPTDV), a variant of the classical VRPT that uses driver-specific travel and service times and vehicles-specific capacity to model the familiarity of the different drivers with the customers to visit. The first objective function aims to minimize traveled distance and the second objective function minimizing working duration. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, the hybrid approach based on LP metric method and genetic algorithm is proposed to solve the given problem.

Grid Platform Applied to the Vehicle Routing Problem with Time Windows for the Distribution of Products

2012 ◽  
pp. 52-81
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Abelardo Rodríguez-León ◽  
Rafael Rivera-López ◽  
Fredy Juárez-Pérez ◽  
Carmen Peralta-Abarca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Solution Space ◽  
Routing Problem ◽  
Computing Platform ◽  
Hard Problems ◽  
Computing Platforms

Around the world there have recently been new and more powerful computing platforms created that can be used to work with computer science problems. Some of these problems that are dealt with are real problems of the industry; most are classified by complexity theory as hard problems. One such problem is the vehicle routing problem with time windows (VRPTW). The computational Grid is a platform which has recently ventured into the treatment of hard problems to find the best solution for these. This chapter presents a genetic algorithm for the vehicle routing problem with time windows. The algorithm iteratively applies a mutation operator, first of the intelligent type and second of the restricting type. The algorithm takes advantage of Grid computing to increase the exploration and exploitation of the solution space of the problem. The Grid performance is analyzed for a genetic algorithm and a measurement of the latencies that affect the algorithm is studied. The convenience of applying this new computing platform to the execution of algorithms specially designed for Grid computing is presented.

scholarly journals A Grid-Based Genetic Approach to Solving the Vehicle Routing Problem with Time Windows

2019 ◽  
Vol 9 (18) ◽  
pp. 3656 ◽  
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Abelardo Rodríguez-León ◽  
Rafael Rivera-López ◽  
Martín H. Cruz-Rosales
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Information Exchange ◽  
Time Windows ◽  
Optimization Procedure ◽  
Virtual Private Network ◽  
Routing Problem ◽  
Private Network ◽  
Grid Based

This paper describes one grid-based genetic algorithm approach to solve the vehicle routing problem with time windows in one experimental cluster MiniGrid. Clusters used in this approach are located in two Mexican cities (Cuernavaca and Jiutepec, Morelos) securely communicating with each other since they are configured as one virtual private network, and its use as a single set of processors instead of isolated groups allows one to increase the computing power to solve complex tasks. The genetic algorithm splits the population of candidate solutions in several segments, which are simultaneously mutated in each process generated by the MiniGrid. These mutated segments are used to build a new population combining the results produced by each process. In this paper, the MiniGrid configuration scheme is described, and both the communication latency and the speedup behavior are discussed. Experimental results show one information exchange reduction through the MiniGrid clusters as well as an improved behavior of the evolutionary algorithm. A statistical analysis of these results suggests that our approach is better as a combinatorial optimization procedure as compared with other methods.

scholarly journals Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

2020 ◽  
Vol 26 (4) ◽  
pp. 174-184
Author(s):  
Thi Diem Chau Le ◽  
Duy Duc Nguyen ◽  
Judit Oláh ◽  
Miklós Pakurár
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Distribution Networks ◽  
Mixed Integer ◽  
Vehicle Group ◽  
Real Problem ◽  
Routing Problem

AbstractThis study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.

scholarly journals TDGVRPSTW of Fresh Agricultural Products Distribution: Considering Both Economic Cost and Environmental Cost

2021 ◽  
Vol 11 (22) ◽  
pp. 10579
Author(s):  
Daqing Wu ◽  
Chenxiang Wu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Economic Cost ◽  
Agricultural Products ◽  
Time Dependent ◽  
Environmental Cost ◽  
Routing Problem ◽  
Measure Function

The time-dependent vehicle routing problem of time windows of fresh agricultural products distribution have been studied by considering both economic cost and environmental cost. A calculation method for road travel time across time periods is designed in this study. A freshness measure function of agricultural products and a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, carbon emissions, perishable agricultural products, customers’ time windows, and minimum freshness. A time-dependent green vehicle routing problem with soft time windows (TDGVRPSTW) model is formulated. The object of the TDGVRPSTW model is to minimize the sum of economic cost and environmental cost. According to the characteristics of the model, a new variable neighborhood adaptive genetic algorithm is designed, which integrates the global search ability of the genetic algorithm and the local search ability of the variable neighborhood descent algorithm. Finally, the experimental data show that the proposed approaches effectively avoid traffic congestions, reduce total distribution costs, and promote energy conservation and emission reduction.

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