Exploring a Column Generation Approach for a Routing Problem with Sequential Packing Constraints

2015 ◽  
pp. 159-166 ◽  
Author(s):  
Telmo Pinto ◽  
Cláudio Alves ◽  
José Valério de Carvalho
Keyword(s):  
Column Generation ◽  
Routing Problem

scholarly journals A hybrid column generation approach for an industrial waste collection routing problem

2014 ◽  
Vol 71 ◽  
pp. 10-20 ◽  
Author(s):  
Kristian Hauge ◽  
Jesper Larsen ◽  
Richard Martin Lusby ◽  
Emil Krapper
Keyword(s):  
Column Generation ◽  
Industrial Waste ◽  
Waste Collection ◽  
Routing Problem

A column generation approach for an inventory routing problem with fuzzy time windows

2020 ◽  
Author(s):  
Amir Saeed Nikkhah Qamsari ◽  
Seyyed-Mahdi Hosseini-Motlagh ◽  
Seyed Farid Ghannadpour
Keyword(s):  
Column Generation ◽  
Time Windows ◽  
Inventory Routing ◽  
Routing Problem ◽  
Inventory Routing Problem

A column generation approach for an emission-oriented vehicle routing problem on a multigraph

2021 ◽  
Vol 288 (3) ◽  
pp. 794-809 ◽  
Author(s):  
Martin Behnke ◽  
Thomas Kirschstein ◽  
Christian Bierwirth
Keyword(s):  
Vehicle Routing ◽  
Column Generation ◽  
Vehicle Routing Problem ◽  
Routing Problem

A column generation approach for the location-routing problem with time windows

2018 ◽  
Vol 90 ◽  
pp. 249-263 ◽  
Author(s):  
Mohammad Saleh Farham ◽  
Haldun Süral ◽  
Cem Iyigun
Keyword(s):  
Column Generation ◽  
Time Windows ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing

A Column Generation Algorithm for a Rich Vehicle-Routing Problem

2009 ◽  
Vol 43 (1) ◽  
pp. 56-69 ◽  
Author(s):  
Alberto Ceselli ◽  
Giovanni Righini ◽  
Matteo Salani
Keyword(s):  
Vehicle Routing ◽  
Column Generation ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Generation Algorithm

scholarly journals Column Generation for a Multitrip Vehicle Routing Problem with Time Windows, Driver Work Hours, and Heterogeneous Fleet

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Michel Povlovitsch Seixas ◽  
André Bergsten Mendes
Keyword(s):  
Vehicle Routing ◽  
Column Generation ◽  
Vehicle Routing Problem ◽  
Resource Constraints ◽  
Time Windows ◽  
Planning Horizon ◽  
Work Hours ◽  
Start Time ◽  
Routing Problem ◽  
Multiple Routes

This study addresses a vehicle routing problem with time windows, accessibility restrictions on customers, and a fleet that is heterogeneous with regard to capacity and average speed. A vehicle can perform multiple routes per day, all starting and ending at a single depot, and it is assigned to a single driver whose total work hours are limited. A column generation algorithm is proposed. The column generation pricing subproblem requires a specific elementary shortest path problem with resource constraints algorithm to address the possibility for each vehicle performing multiple routes per day and to address the need to set the workday’s start time within the planning horizon. A constructive heuristic and a metaheuristic based on tabu search are also developed to find good solutions.

scholarly journals Matheuristics and Column Generation for a Basic Technician Routing Problem

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 313
Author(s):  
Nicolas Dupin ◽  
Rémi Parize ◽  
El-Ghazali Talbi
Keyword(s):  
Machine Learning ◽  
Column Generation ◽  
Time Window ◽  
Time Windows ◽  
Machine Learning Techniques ◽  
Mixed Integer ◽  
Routing Problems ◽  
Routing Problem ◽  
Feasible Solutions ◽  
Time Window Constraints

This paper considers a variant of the Vehicle Routing Problem with Time Windows, with site dependencies, multiple depots and outsourcing costs. This problem is the basis for many technician routing problems. Having both site-dependency and time window constraints lresults in difficulties in finding feasible solutions and induces highly constrained instances. Matheuristics based on Mixed Integer Linear Programming compact formulations are firstly designed. Column Generation matheuristics are then described by using previous matheuristics and machine learning techniques to stabilize and speed up the convergence of the Column Generation algorithm. The computational experiments are analyzed on public instances with graduated difficulties in order to analyze the accuracy of algorithms for ensuring feasibility and the quality of solutions for weakly to highly constrained instances. The results emphasize the interest of the multiple types of hybridization between mathematical programming, machine learning and heuristics inside the Column Generation framework. This work offers perspectives for many extensions of technician routing problems.

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>

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