Finding optimum neighbor for routing based on multi-criteria, multi-agent and fuzzy approach

被引:9
作者
Bandyopadhyay, Susmita [1 ]
Bhattacharya, Ranjan [1 ]
机构
[1] Jadavpur Univ, Dept Prod Engn, Kolkata 700032, W Bengal, India
关键词
Multi-agent based system; Multi-criteria decision analysis; PROMETHEE; Fuzzy theory; Routing; HOLONIC MANUFACTURING SYSTEMS; GENETIC ALGORITHM; ARCHITECTURE; MANAGEMENT; OPTIMIZATION; DESIGN; POWER;
D O I
10.1007/s10845-013-0758-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a hierarchical multi-agent based routing has been introduced. In dynamic situations, the previously planned entire optimum path may not stay optimum over time. Thus the approach in this paper routes a job to the next optimum neighboring node from the current position, instead of deciding over the entire path before the journey begins. Whenever there is a need to choose the next optimum node for routing or whenever a job enters the system, the master agent calls the worker agents. The worker agents run in parallel and return the results to the master agent. The worker agents are killed after their tasks are completed. The master agent takes decision based on the data delivered by the worker agents through a multi-criteria decision analysis technique known as PROMETHEE. A total of five worker agents are used for seven criteria and fuzzy approach is applied in a fuzzy shortest path algorithm performed by a worker agent and in fuzzy weight calculation in PROMETHEE. Three examples with three different kinds of networks have been used to show the effectiveness of the entire approach. The motivation of the idea introduced in this paper has come from the mating behavior of a spider known as Tarantula where the female spider sometimes eats the male spider just after mating.
引用
收藏
页码:25 / 42
页数:18
相关论文
共 59 条
[1]   Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning [J].
Aissani, N. ;
Bekrar, A. ;
Trentesaux, D. ;
Beldjilali, B. .
JOURNAL OF INTELLIGENT MANUFACTURING, 2012, 23 (06) :2513-2529
[2]   A new multi-agent system framework for tacit knowledge management in manufacturing supply chains [J].
Al-Mutawah, Khalid ;
Lee, Vincent ;
Cheung, Yen .
JOURNAL OF INTELLIGENT MANUFACTURING, 2009, 20 (05) :593-610
[3]   GenCLOn: An ontology for city logistics [J].
Anand, Nilesh ;
Yang, Mengchang ;
van Duin, J. H. R. ;
Tavasszy, Lori .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (15) :11944-11960
[4]   An agent-based parallel approach for the job shop scheduling problem with genetic algorithms [J].
Asadzadeh, Leila ;
Zamanifar, Kamran .
MATHEMATICAL AND COMPUTER MODELLING, 2010, 52 (11-12) :1957-1965
[5]   An artificial negotiating agent modeling approach embedding dynamic offer generating and cognitive layer [J].
Bahrammirzaee, Arash ;
Chohra, Amine ;
Madani, Kurosh .
NEUROCOMPUTING, 2011, 74 (16) :2698-2709
[6]   Agent-based guided local search [J].
Barbucha, Dariusz .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (15) :12032-12045
[7]  
Brans J.-P., 2005, PROMETHEE MEHTOD, P200
[8]   Tropos: An agent-oriented software development methodology [J].
Bresciani, P ;
Perini, A ;
Giorgini, P ;
Giunchiglia, F ;
Mylopoulos, J .
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 2004, 8 (03) :203-236
[9]  
BURRAFATO P, 2002, P 4 INT BI C WORKSH
[10]   Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems [J].
Chen, Bo ;
Cheng, Harry H. ;
Palen, Joe .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2009, 17 (01) :1-10