Integrating Operations Research and Neural Networks for Vehicle Routing

1995 ◽  
pp. 245-262
Author(s):  
Jean-Yves Potvin ◽  
Christian Robillard
Keyword(s):  
Neural Networks ◽  
Vehicle Routing ◽  
Operations Research

Neural networks and operations research: An overview

1992 ◽  
Vol 19 (3-4) ◽  
pp. 179-189 ◽  
Author(s):  
Laura I. Burke ◽  
James P. Ignizio
Keyword(s):  
Neural Networks ◽  
Operations Research

scholarly journals Combinatorial Optimization and Reasoning with Graph Neural Networks

2021 ◽  
Author(s):  
Quentin Cappart ◽  
Didier Chételat ◽  
Elias B. Khalil ◽  
Andrea Lodi ◽  
Christopher Morris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Combinatorial Optimization ◽  
Computer Science ◽  
Operations Research ◽  
Building Block ◽  
Related Data ◽  
Problem Instances ◽  
Graph Neural Networks

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning.

scholarly journals Intuitionistic Fuzzy Cycles and Intuitionistic Fuzzy Trees

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Akram ◽  
N. O. Alshehri
Keyword(s):  
Neural Networks ◽  
Structural Properties ◽  
Operations Research ◽  
Computer Network ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Graphs ◽  
Research Problems ◽  
Intuitionistic Fuzzy Graphs

Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types.

Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints

2021 ◽  
Author(s):  
Xiangyi Zhang ◽  
Lu Chen ◽  
Michel Gendreau ◽  
André Langevin
Keyword(s):  
Vehicle Routing ◽  
Operations Research ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Learning Model ◽  
Exact Algorithms ◽  
Two Dimensional ◽  
Branch And Price ◽  
Routing Problem ◽  
Loading Constraints

A capacitated vehicle routing problem with two-dimensional loading constraints is addressed. Associated with each customer are a set of rectangular items, the total weight of the items, and a time window. Designing exact algorithms for the problem is very challenging because the problem is a combination of two NP-hard problems. An exact branch-and-price algorithm and an approximate counterpart are proposed to solve the problem. We introduce an exact dominance rule and an approximate dominance rule. To cope with the difficulty brought by the loading constraints, a new column generation mechanism boosted by a supervised learning model is proposed. Extensive experiments demonstrate the superiority of integrating the learning model in terms of CPU time and calls of the feasibility checker. Moreover, the branch-and-price algorithms are able to significantly improve the solutions of the existing instances from literature and solve instances with up to 50 customers and 103 items. Summary of Contribution: We wish to submit an original research article entitled “Learning-based branch-and-price algorithms for a vehicle routing problem with time windows and two-dimensional loading constraints” for consideration by IJOC. We confirm that this work is original and has not been published elsewhere, nor is it currently under for publication elsewhere. In this paper, we report a study in which we develop two branch-and-price algorithms with a machine learning model injected to solve a vehicle routing problem integrated the two-dimensional packing. Due to the complexity brought by the integration, studies on exact algorithms in this field are very limited. Our study is important to the field, because we develop an effective method to significantly mitigate computational burden brought by the packing problem so that exactness turns to be achievable within reasonable time budget. The approach can be generalized to the three-dimensional case by simply replacing the packing algorithm. It can also be adapted for other VRPs when high-dimensional loading constraints are concerned. Broadly speaking, the study is a typical example of adopting supervised learning to achieve acceleration for operations research algorithms, which expands the envelop of computing and operations research. Hence, we believe this manuscript is appropriate for publication by IJOC.

Selection of Vehicle Routing Heuristic Using Neural Networks

1997 ◽  
Vol 4 (3) ◽  
pp. 211-221 ◽  
Author(s):  
D. Tuzun ◽  
M.A. Magent ◽  
L.I. Burke
Keyword(s):  
Neural Networks ◽  
Vehicle Routing ◽  
Selection Of

scholarly journals VEHICLE ROUTING PROBLEM WITH TIME WINDOWS

2014 ◽  
pp. 72-80
Author(s):  
Vladimir Vacic ◽  
Tarek M. Sobh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Operations Research ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Traveling Salesman ◽  
Vehicle Routing Problems ◽  
Routing Problem ◽  
Common Problems

The topic of this paper is a Genetic Algorithm solution to the Vehicle Routing Problem with Time Windows, a variant of one of the most common problems in contemporary operations research. The paper will introduce the problem starting with more general Traveling Salesman and Vehicle Routing problems and present some of the prevailing strategies for solving them, focusing on Genetic Algorithms. At the end, it will summarize the Genetic Algorithm solution proposed by K.Q. Zhu which was used in the programming part of the project.

scholarly journals Benedict Delisle Burns. 22 February 1915—6 September 2001

2020 ◽  
Vol 68 ◽  
pp. 49-70
Author(s):  
Christopher D. Richards ◽  
Timothy V. P. Bliss
Keyword(s):  
Neural Networks ◽  
Statistical Analysis ◽  
Operations Research ◽  
Applied Mathematics ◽  
Respiratory Rhythm ◽  
Cell Activity ◽  
Research Unit ◽  
Memory And Attention ◽  
Active Service ◽  
Mcgill University

Ben Burns was a pioneer of operations research and of the statistical analysis of neuronal activity. During the war, Ben served in Solly Zuckermann's operations research unit, which included a period of active service in the Mediterranean. After the war he worked with G. L. Brown at the National Institute for Medical Research (NIMR), where he investigated the effects of agents that affected neuromuscular transmission. In 1950 he moved to the Physiology Department of McGill University in Montreal, where he explored the properties of neural networks in neurologically isolated slabs of cerebral cortex and established the mechanisms responsible for maintaining rhythmic periods of excitation in isolated nerve networks. He subsequently provided evidence that self-re-exciting neural networks were implicated in establishing the respiratory rhythm. While at McGill, Ben initiated a number of highly original cross-disciplinary studies concerning the physiological bases of learning, memory and attention. He returned to NIMR in 1966 to head the Division of Physiology and Pharmacology, where he continued his investigations of visual perception. Ben was an ingenious experimenter and devised a number of mechanical and electronic devices for the statistical analysis of nerve cell activity at a time when digital computers were largely unavailable for biological work. In his 1968 book, The uncertain nervous system , he expressed his view that the interdisciplinary nature of central neurophysiology required of those who studied it a knowledge of classical physiology, experimental psychology, applied mathematics and electronic engineering. His broad view of the subject inspired a generation of students.

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