A Location Based Heuristic for General Routing Problems

Bramel, Julien; Simchi‐Levi, David

doi:10.1287/opre.43.4.649

Cited by 223 publications

(124 citation statements)

References 24 publications

Supporting

Mentioning

121

Contrasting

Unclassified

Order By: Relevance

“…Moreover, clustering is important in the problem setting of vehicle routing and scheduling, see Fisher and Jaikumar (1981) and Bramel and Simchi-Levi (1995), and in the area of combined routing location, see and .…”

Section: Applicationsmentioning

confidence: 99%

Facility location models for distribution system design

Klose

Drexl

2005

European Journal of Operational Research

745

280

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The design of the distribution system is a Strategie issue for almost every Company. The Problem of locating facilities and allocating customers Covers the core topics of distribution system design. Model formulations and Solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational Performance. This paper reviews some of the contributions to the current state-of-the-art. In particular, continuous location models, network location models, mixed-integer programming models, and applications are summarized. Terms of use: Documents in EconStor may

show abstract

Section: Applicationsmentioning

confidence: 99%

Facility location models for distribution system design

Klose

Drexl

2005

European Journal of Operational Research

745

280

View full text Add to dashboard Cite

show abstract

“…General algorism, such as NN (Neural Network) [46] or ACO (Ant Colony Optimization) [47,48], based approximate solutions are also given. In the study to solve vehicle routing problem, it is well known that the method clusters the area by the number of agents and calculates each shortest path [49]. Approximation algorithms and heuristics are, therefore, developed for these problems [48,50,51].…”

Section: Optimal Assignment Problemmentioning

confidence: 99%

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

Morisawa

Hayashi

Mizuuchi

2018

Journal of Robotics

View full text Add to dashboard Cite

We present a method to allocate multiple tasks with uncertainty to heterogeneous robots using the theory of comparative advantage: an economic theory that maximizes the benefit of specialization. In real applications, robots often must execute various tasks with uncertainty and future multirobot system will have to work effectively with people as a team. As an example, it may be necessary to explore an unknown environment while executing a main task with people, such as carrying, rescue, military, or construction. The proposed task allocation method is expected to reduce the total makespan (total length of task-execution time) compared with conventional methods in robotic exploration missions. We expect that our method is also effective in terms of calculation time compared with the time-extended allocation method (based on the solution of job-shop scheduling problems). We simulated carrying tasks and exploratory tasks, which include uncertainty conditions such as unknown work environments (2 tasks and 2 robots, multiple tasks and 2 robots, 2 robots and multiple tasks, and multiple tasks and multiple robots). In addition, we compared our method with full searching and methods that maximize the sum of efficiency in these simulations by several conditions: first, 2 tasks (carrying and exploring) in the four uncertain conditions (later time, new objects appearing, disobedient robots, and shorter carrying time) and second, many types of tasks to many types of robots in the three uncertain conditions (unknown carrying time, new objects appearing, and some reasonable agents). The proposed method is also effective in three terms: the task-execution time with an increasing number of objects, uncertain increase in the number of tasks during task execution, and uncertainty agents who are disobedient to allocation orders compared to full searching and methods that maximize the sum of efficiency. Additionally, we performed two real-world experiments with uncertainty.

show abstract

“…Vehicle routing problems have also been integrated into facility location models (see Min et al [43] for a review of this work). It is worth noting that a capacitated fixed charge location model is at the heart of one of the more effective heuristics for the vehicle routing problem (Bramel and Simchi-Levi, [5]). Finally, researchers interested in the field of reverse logistics or closed-loop supply chains are utilizing extensions of the UFLP to identify desirable sites for forward distribution centers and reverse collection, sorting, recycling and remanufacturing facilities (Easwaran and Uster, [19]; Sahyouni et al [51]; and Uster et al [63]).…”

Section: Where To From Here?mentioning

confidence: 99%

What you should know about location modeling

Daskin

2008

Naval Research Logistics

197

110

View full text Add to dashboard Cite

Facility location models have been applied to problems in the public and private sectors for years. In this article, the author first presents a taxonomy of location problems based on the underlying space in which the problem is embedded. The article illustrates problems from each part of the taxonomy with an emphasis on discrete location problems. Selected recent research in the area is also discussed.

show abstract

A Location Based Heuristic for General Routing Problems

Cited by 223 publications

References 24 publications

Facility location models for distribution system design

Facility location models for distribution system design

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

What you should know about location modeling

Contact Info

Product

Resources

About