Fuzzy Constraint-Based Agent Negotiation

Conflicts between two or more parties arise for various reasons andperspectives. Thus, resolution of con-flicts frequently relies on some form of negotiation. Thispaper presents a general problem-solving framework for modeling multi-issue multilateral negotiationusing fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraintsatisfaction problem (DFCSP). Fuzzy constrains are thus used to naturally represent each agent''sdesires involving imprecision and human conceptualization, particularly when lexical imprecision andsubjective matters are concerned. On the other hand, based on fuzzy constraint-basedproblem-solving, our approach enables an agent not only to systematically relax fuzzy constraints togenerate a proposal, but also to employ fuzzy similarity to select the alternative that is subjectto its acceptability by the opponents. This task of problem-solving is to reach an agreement thatbenefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the dealmore quickly since their search focuses only on the feasible solution space. An application tomultilateral negotiation of a travel planning is provided to demonstrate the usefulness andeffectiveness of our framework.

FUZZY RELIABILITY-BASED OPTIMUM DESIGN OF LAMINATED COMPOSITES

In conventional reliability-based optimum design of structures, the reliability is computed based on the probability assumption, and the design objective and constraint are considered as deterministic ones. In many cases, however, there exist some fuzzy factors in the design objective and constraint functions. In this paper, the fuzzy reliability and fuzzy constraint are introduced to establish the fuzzy reliability-based optimum model for laminated composites. The examples show that the method developed in this paper can give consideration to both the reliability demand and the economic aspect of a structure. Since the fuzzy reliability-based optimum design is a ＇soft＇ design, the present results provide a new methodology for the effective evaluation of structural reliability and bring us an alternative optimum design idea.

Multi-Objective Fuzzy Optimum Design Based on Reliability for Offshore Jacket Platforms

In consideration of the fuzzy constraint boundary and through analysis of structural reliability, a model of structural fuzzy optimum design is established based on reliability for offshore jacket platforms. According to the characteristics of offshore jacket platforms, the tolerance coefficient of the constraint boundary is determined with the fuzzy optimization method. The optimum level cut set lambda*, which is the intersection of the fuzzy constraint set and fuzzy objective set, is determined with the bound search method, and then the fuzzy optimum solution to the fuzzy optimization problem is obtained. The central offshore platform SZ36-1 is designed with the fuzzy optimum model based on reliability; the results are compared with those from deterministic optimum design and fuzzy optimum design. The tendency of design variables in the above three methods and its reasons are analyzed. The results of an example show that the fuzzy optimum design based on reliability is stable and reliable.

Predictive Control in Fuzzy Dynamic Environment

This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Compared to the standard quadratic objective function, with the fuzzy decision-making approach, the designer has more freedom in specifying the desired process behavior.

Method for solving fully fuzzy linear programming problems using deviation degree measure

A new fully fuzzy linear programming （FFLP） problem with fuzzy equality constraints is discussed. Using deviation degree measures, the FFLP problem is transformed into a crisp 6-parametric linear programming （LP） problem. Giving the value of deviation degree in each constraint, the 6-fuzzy optimal solution of the FFLP problem can be obtained by solving this LP problem. An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict： to improve the values of the objective function and to decrease the values of the deviation degrees. A numerical example is solved to illustrate the proposed method.

Multimodal processes optimization subject to fuzzy operation time constraints:declarative modeling approach

We present an extension of the resource-constrained multi-product scheduling problem for an automated guided vehicle(AGV) served flow shop, where multiple material handling transport modes provide movement of work pieces between machining centers in the multimodal transportation network(MTN). The multimodal processes behind the multi-product production flow executed in an MTN can be seen as processes realized by using various local periodically functioning processes. The considered network of repetitively acting local transportation modes encompassing MTN's structure provides a framework for multimodal processes scheduling treated in terms of optimization of the AGVs fleet scheduling problem subject to fuzzy operation time constraints. In the considered case, both production takt and operation execution time are described by imprecise data. The aim of the paper is to present a constraint propagation(CP) driven approach to multi-robot task allocation providing a prompt service to a set of routine queries stated in both direct and reverse way. Illustrative examples taking into account an uncertain specification of robots and workers operation time are provided.

Impact of industrial virtual power plant on renewable energy integration

An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant(IVPP)has been proposed to deal with such problems.This study demonstrates an IVPP model to man age resources in an eco-i ndustrial park,including en ergy storage systems,dema nd resp onse(DR)resources,and distributed energies.In addition,fuzzy theory is used to cha nge the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizi ng the daily ben efits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity gen erated in side IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three win d-level scenarios dem on strate the efficie nt syn ergies betwee n IVPP resources.The validatio n results indicate that IVPP can optimize the supply and demand resources in in dustrial parks,thereby decarbonizing the power systems.

Fuzzy-Constrained Graph Pattern Matching in Medical Knowledge Graphs

The research on graph pattern matching(GPM) has attracted a lot of attention. However, most of the research has focused on complex networks, and there are few researches on GPM in the medical field. Hence, with GPM this paper is to make a breast cancer-oriented diagnosis before the surgery. Technically, this paper has firstly made a new definition of GPM, aiming to explore the GPM in the medical field, especially in Medical Knowledge Graphs(MKGs). Then, in the specific matching process, this paper introduces fuzzy calculation, and proposes a multi-threaded bidirectional routing exploration(M-TBRE) algorithm based on depth first search and a two-way routing matching algorithm based on multi-threading. In addition, fuzzy constraints are introduced in the M-TBRE algorithm, which leads to the Fuzzy-M-TBRE algorithm. The experimental results on the two datasets show that compared with existing algorithms, our proposed algorithm is more efficient and effective.

Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system.Mobile energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability.However,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of MESS.This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power networks.The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the system.With fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of MESS.Therefore,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme.Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model.The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion.Besides,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.