Leximin Asymmetric Multiple Objective Distributed Constraint Optimization Problem

2017 ◽  
Vol 34 (1) ◽  
pp. 49-84 ◽  
Author(s):  
Toshihiro Matsui ◽  
Hiroshi Matsuo ◽  
Marius Silaghi ◽  
Katsutoshi Hirayama ◽  
Makoto Yokoo
Keyword(s):  
Optimization Problem ◽  
Multiple Objective ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Distributed Constraint Optimization Problem

A Two-Phase Complete Algorithm for Multi-Objective Distributed Constraint Optimization

2014 ◽  
Vol 18 (4) ◽  
pp. 573-580
Author(s):  
Alexandre Medi ◽  
◽  
Tenda Okimoto ◽  
Katsumi Inoue ◽  
◽  
...  
Keyword(s):  
Branch And Bound ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Branch And Bound Algorithm ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Multi Objective ◽  
Multi Agent ◽  
Distributed Constraint Optimization Problem

A Distributed Constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Many application problems in multi-agent systems can be formalized as DCOPs. However, many real world optimization problems involve multiple criteria that should be considered separately and optimized simultaneously. A Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) is an extension of a mono-objective DCOP. Compared to DCOPs, there exists few works on MO-DCOPs. In this paper, we develop a novel complete algorithm for solving an MO-DCOP. This algorithm utilizes a widely used method called Pareto Local Search (PLS) to generate an approximation of the Pareto front. Then, the obtained information is used to guide the search thresholds in a Branch and Bound algorithm. In the evaluations, we evaluate the runtime of our algorithm and show empirically that using a Pareto front approximation obtained by a PLS algorithm allows to significantly speed-up the search in a Branch and Bound algorithm.

A Distributed Autonomous Approach to Developing a Disaster Evacuation Assist System

2015 ◽  
Vol 10 (6) ◽  
pp. 1081-1090 ◽  
Author(s):  
Yasuki Iizuka ◽  
◽  
Katsuya Kinoshita ◽  
Kayo Iizuka ◽  
◽  
...  
Keyword(s):  
Completion Time ◽  
Optimization Problem ◽  
Ad Hoc ◽  
The Other ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Emergency Situations ◽  
Agent Simulation ◽  
Multi Agent ◽  
Distributed Constraint Optimization Problem

In times of disaster, or other emergency situations, it is essential for people to be evacuated in a smooth manner. Evacuation must be performed promptly and safely. It is necessary to avoid generating a secondary disaster at the time of evacuation. However, this is not easy to realize, because people often tend to panic when faced with disaster, crowding the evacuation passageways of buildings. On the other hand, people do not attempt to evacuate themselves from danger when the normalcy bias has occurred. Therefore, evacuation guidance is very important. However, it is impossible to guide all evacuees through authorities such as disaster countermeasure offices. To deal with this issue, the authors propose a system that provides optimal evacuation guidance autonomously without central server. The system works on the mobile devices of evacuees, performs distributed calculations using the framework of the distributed constraint optimization problem on ad-hoc communication, and does not need a central server. In the experiment using multi-agent simulation, for the case where the evacuees can receive evacuation guidance from this system, the evacuation completion time decreased. This paper presents an overview and the evaluation results of the prototype of the disaster evacuation assistance system.

Smart system to generate the optimal authorized bounding volume

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alia Belkaïd ◽  
Abdelkader Ben Saci ◽  
Ines Hassoumi
Keyword(s):  
Constraint Satisfaction ◽  
Optimization Problem ◽  
Generation Process ◽  
Constraint Optimization ◽  
Content Type ◽  
Distributed Constraint Optimization ◽  
Bounding Volume ◽  
Target Values ◽  
Distributed Constraint Optimization Problem ◽  
Overall Functioning

PurposeThe overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine, and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence (AI) in accomplishing this task and solving the problem.Design/methodology/approachThis paper provides a method of computational design in generating the optimal authorized bounding volume which uses the best target values of morphological urban rules. It examines an intelligent system, adopting the multi-agent approach, which aims to control and increase urban densification by optimizing morphological urban rules. The process of the system is interactive and iterative. It allows collaboration and exchange between the machine and the designer. This paper is adopting and developing a new approach to resolve the distributed constraint optimization problem in generating the authorized bounding volume. The resolution is not limited to an automatic volume generation from urban rules, but also involves the production of multiple optimal-solutions conditioned both by urban constraints and relevance chosen by the designer. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the AI in accomplishing this task and solving the problem. The human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction. This paper shows and analyses interactions with the machine during the building generation process. It presents different cases of application and discusses the relationship between relevance and constraints satisfaction. This topic can inform a chosen urban densification strategy by assisting a typology of the optimal authorized bounding volume.FindingsThe human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction with fitness function.Originality/valueThe resolution of the distributed constraint optimization problem is not limited to an automatic generation of urban rules, but involves also the production of multiple optimal ABV conditioned both by urban constraints as well as relevance, chosen by the designer.

Multiple-Depot Vehicle Routing Problems as a Distributed Constraint Optimization Problem

2011 ◽  
Vol 66-68 ◽  
pp. 1033-1038 ◽  
Author(s):  
Xing Ming Lei ◽  
Chang Feng Xing ◽  
Ling Wu ◽  
Yun Feng Wen
Keyword(s):  
Vehicle Routing ◽  
Optimization Problem ◽  
Constraint Optimization ◽  
Multi Agent Systems ◽  
Routing Problem ◽  
Distributed Constraint Optimization ◽  
Distributed Reasoning ◽  
Multi Agent ◽  
Multiple Depot ◽  
Distributed Constraint Optimization Problem

The Distributed Constraint Optimization Problem (DCOP) is able to model a variety of distributed reasoning tasks that arise in multi-agent systems, and widely applying to distribute programming, scheduling and resource allocation etc. In order to solve the multi-depot vehicle routing problem (MDVRP) in distributed manner, this article show how DCOP can be used to model the MDVRP, and using existing various DCOP algorithm solve it base on Frodo simulation platform. We have evaluated the performances of various DCOP algorithms on an existing MDVRP.

scholarly journals Towards Improving the Expressivity and Scalability of Distributed Constraint Optimization Problems

2018 ◽  
Author(s):  
William Yeoh
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Constraint Optimization ◽  
Scheduling Problems ◽  
Distributed Constraint Optimization ◽  
Coordination Problems ◽  
Multi Agent ◽  
Distributed Constraint Optimization Problem ◽  
Constraint Optimization Problems ◽  
Agent Coordination

Constraints have long been studied in centralized systems and have proven to be practical and efficient for modeling and solving resource allocation and scheduling problems. Slightly more than a decade ago, researchers proposed the distributed constraint optimization problem (DCOP) formulation, which is well suited for modeling distributed multi-agent coordination problems. In this paper, we highlight some of our recent contributions that are aiming towards improved expressivity of the DCOP model as well as improved scalability of the accompanying algorithms.

Sign in / Sign up

Export Citation Format

Share Document