GIS based heuristic solution of the vehicle routing problem to optimize the school bus routing and scheduling

2016 ◽  
Author(s):  
Emrana Kabir Hashi ◽  
Md. Rokibul Hasan ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
School Bus ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Routing And Scheduling ◽  
Bus Routing

scholarly journals A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems

2020 ◽  
Vol 10 (7) ◽  
pp. 2403
Author(s):  
Yanjun Shi ◽  
Lingling Lv ◽  
Fanyi Hu ◽  
Qiaomei Han
Keyword(s):  
Vehicle Routing ◽  
Waste Disposal ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Waste Collection ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Combination Optimization

This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectively, assigning customers to vehicles and planning the order in which customers are visited by vehicles. In the latter step, we propose the sector combination optimization (SCO) algorithm to generate multiple initial solutions, and then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. Computational experiments on benchmark instances showed that the initial solutions obtained by the sector combination optimization algorithm were more abundant and better than other iterative algorithms using only one solution for initialization, and the solutions with distance gap were obtained using the merge-head and drop-tail strategy in a lower CPU time compared to the Tabu search algorithm.

scholarly journals A hybrid column generation and clustering approach to the school bus routing problem with time windows

Ingeniería ◽  
2015 ◽  
Vol 20 (1) ◽  
Author(s):  
Eduyn Ramiro Lopez Santana ◽  
Jose de Jesus Romero Carvajal
Keyword(s):  
Column Generation ◽  
Rural Areas ◽  
Vehicle Routing Problem ◽  
Clustering Algorithm ◽  
Time Windows ◽  
School Bus ◽  
Routing Problem ◽  
Bus Routing ◽  
Clustering Approach ◽  
Urban And Rural Areas

<span>This paper attempts to solve the School Bus Routing Problem with Time Windows that consists of finding the best set of routes to pick up students distributed geographically with constraints as capacity, time windows and maximum travel time. We formulated the problem as a classic Vehicle Routing Problem with Time Windows and solved it using an approach based on a clustering algorithm and column generation method. A real world case from a school in Bogotá, Colombiais presented including 600 students to pick up in near 400 nodes located in urban and rural areas. The obtained results demonstrate a reduction as the problem’s complexity and  an improvement on the performance measures of the proposed method.</span>

An Integrated Heuristic Approach for the Long-Distance Heterogeneous Vehicle Routing Problem

2021 ◽  
pp. 20-48
Author(s):  
Mehmet Sevkli ◽  
Abdullah S. Karaman ◽  
Yusuf Ziya Unal ◽  
Muheeb Babajide Kotun
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Model Building ◽  
Optimization Procedure ◽  
Fixed Costs ◽  
Long Distance ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Encounter Time

In this chapter, a single depot, long-distance heterogeneous vehicle routing problem is studied with fixed costs and vehicle-dependent routing costs (LD-HVRPFD). The LD-HVRPFD considers retailers far away from the single depot and hence route durations could exceed a day. Thus, the number of available vehicles changes through the course of the multi-day planning horizon. Moreover, it is typical to encounter time-variant demand from retailers. To solve the LD-HVRPFD, the authors developed an iterative heuristic solution methodology integrated into a programming platform. The solution method consists of decomposing the VRP into sequential daily problems, model building using macro programming, obtaining a solution using a solver, determining the route-vehicle pairs and time durations, and dynamically updating the truck availability for the next day. The method is illustrated using real data from one of the biggest retail companies in the ready-to-wear sector of textile supply chains. The performance of the heuristic optimization procedure based on time and gap restriction criteria is presented.

scholarly journals Solving the School Bus Routing Problem by Maximizing Trip Compatibility

2017 ◽  
Vol 2667 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Ali Shafahi ◽  
Zhongxiang Wang ◽  
Ali Haghani
Keyword(s):  
Data Sets ◽  
Scheduling Problem ◽  
School Bus ◽  
Routing Problems ◽  
Routing Problem ◽  
Routing And Scheduling ◽  
Bus Routing ◽  
Bus Scheduling ◽  
Total Travel Time ◽  
New Formulation

School bus planning is usually divided into routing and scheduling because of the complexity of solving them concurrently. However, the separation between these two steps may lead to worse solutions with higher overall costs than from solving them together. When the minimal number of trips in the routing problem is being determined, neglecting trip compatibility could increase the number of buses needed in the scheduling problem. This paper proposes a new formulation for the multischool homogeneous fleet routing problem that maximizes trip compatibility while minimizing total travel time. This plan incorporates the trip compatibility for the scheduling problem in the routing problem. A proposed heuristic algorithm for solving this problem decomposes the problem by schools. To compare the performance of the model with traditional routing problems, eight midsize data sets were generated. Importing the generated trips of the routing problems into the bus scheduling (blocking) problem shows that the proposed model can reduce the buses needed by up to 25%. A sensitivity analysis on coefficients of the model illustrates the effect of the weight of trip compatibility.

Rural School Vehicle Routing Problem

2005 ◽  
Vol 1922 (1) ◽  
pp. 105-110 ◽  
Author(s):  
David Ripplinger
Keyword(s):  
Rural Areas ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Urban Context ◽  
School Bus ◽  
Students With Special Needs ◽  
Routing Problem ◽  
Bus Routing ◽  
The Cost ◽  
Initial Route

The school bus routing problem traditionally has been defined in an urban context. However, because of the unique attributes of the problem in rural areas, traditional heuristic methods for solving the problem may produce impractical results. In many cases, these characteristics also provide the opportunity to investigate what size and mix of vehicles, whether large or small buses, conforming vans, or other modes, are most efficient. In addition, these vehicles may be further differentiated by the presence of equipment for transporting students with special needs. To address this situation, a mathematical model of the problem was constructed and a new heuristic was developed. This heuristic consists of two parts: constructing the initial route and then improving it by using a fixed tenure tabu search algorithm. This rural routing heuristic, in addition to several existing ones, is then applied to a randomly generated school district with rural characteristics. For the relevant measure, a function of student ride time, the new heuristic provides a set of routes superior to those produced by existing methods. Because ride times produced by the new heuristic are lower than those for routes generated by existing methods, the likelihood of injury to students may decrease. Also, with the cost of operation for each route calculated in dollars, a comparison of solutions in financial, as well as temporal, terms is possible.

A Hierarchical Market Solution to the Min–Max Multiple Depots Vehicle Routing Problem

2014 ◽  
Vol 02 (01) ◽  
pp. 87-100 ◽  
Author(s):  
Elad Kivelevitch ◽  
Balaji Sharma ◽  
Nicholas Ernest ◽  
Manish Kumar ◽  
Kelly Cohen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Mixed Integer ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Binary Programming ◽  
Multiple Depots ◽  
Initial Location ◽  
Spatially Distributed

The problem of assigning a group of Unmanned Aerial Vehicles (UAVs) to perform spatially distributed tasks often requires that the tasks will be performed as quickly as possible. This problem can be defined as the Min–Max Multiple Depots Vehicle Routing Problem (MMMDVRP), which is a benchmark combinatorial optimization problem. In this problem, UAVs are assigned to service tasks so that each task is serviced once and the goal is to minimize the longest tour performed by any UAV in its motion from its initial location (depot) to the tasks and back to the depot. This problem arises in many time-critical applications, e.g. mobile targets assigned to UAVs in a military context, wildfire fighting, and disaster relief efforts in civilian applications. In this work, we formulate the problem using Mixed Integer Linear Programming (MILP) and Binary Programming and show the scalability limitation of these formulations. To improve scalability, we propose a hierarchical market-based solution (MBS). Simulation results demonstrate the ability of the MBS to solve large scale problems and obtain better costs compared with other known heuristic solution.

scholarly journals Enhancement of Firefly Technique for Effective Optimization of Multi-Depot Vehicle Routing

2019 ◽  
Vol 8 (11) ◽  
pp. 2346-2353 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Research Work ◽  
Variable Neighbourhood Search ◽  
Two Phase ◽  
Routing Problem ◽  
Bus Routing ◽  
Pareto Ranking ◽  
Neighbourhood Search

Vehicle Routing Problem (VRP) plays a significant role in today’s demanding world, especially in Logistics, Disaster relief supplies or Emergency transportation, Courier services, ATM cash replenishment, School bus routing and so on and it acts as a central hub for distribution management. The objectives of the present research are to solve NP-Hard Multi-depot Vehicle Routing Problem (MDVRP) by using an enhanced firefly approach as well as to examine the efficiency of the proposed technique Cordeau benchmark dataset of MDVRP were used. The foremost principle of MDVRP is to optimize the cost of the solution, to minimize the overall vehicles, travelling distance and number of routes. MDVRP is constructed with two phase, assignment and routing. The firefly technique is enhanced by using inter depot, which is applied in assignment phase. In routing phase saving cost, intra and inter-route were used. The results were compared with Ant colony optimization (ACO), Genetic algorithm (GA), Intelligent water drops (IWD), Particle Swarm Optimization (PSO), Genetic cluster (GC), Genetic using Pareto Ranking (GAPR), Nomadic Genetic algorithm (NGA), and General Variable neighbourhood search (GVNS) algorithm. The solutions obtained in this research work found to be optimal for most of the benchmark instances

Sign in / Sign up

Export Citation Format

Share Document