In the multi agent system, negotiation between different agents becomes more and more complex when the distributed tasks need to be allocated among them. Usually, this process has been done in the centralized way wher...
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In the multi agent system, negotiation between different agents becomes more and more complex when the distributed tasks need to be allocated among them. Usually, this process has been done in the centralized way where agents have all the information of both the tasks and the environment. However, in the real application such as satellite scheduling problem or production scheduling problem, not all the information can be acquired, some of them even cannot be known. Therefore, in this paper, we propose a multi agent negotiation model for distributed task allocation in a priority based environment. This process is modeled as a finite Markov decisionprocess (FMDP), and a coordinated negotiation protocol is also suggested to support the model. The experiments show that tasks are allocated in an efficient way, and it can also reduce the communication cost.
This paper presents an assessment model for research and development (R&D) projects considering uncertainty in their overall life cycle. Technology maturity and competitive advantages were considered and a Balance...
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This paper presents an assessment model for research and development (R&D) projects considering uncertainty in their overall life cycle. Technology maturity and competitive advantages were considered and a Balanced Scorecard (BSC) model was developed to evaluate R&D projects. Based on the multilevel evaluation framework, an evidence reasoning approach has been developed to aggregate the numerical data and qualitative information with uncertainty. The expected utility is applied to describe preferences of decision-makers in the decisionprocess. A case study in the context of automobile R&D projects is given to illustrate the methods.
In this paper, we present a decision-makingprocess that uses our proposed quasi-oppositional comprehensive learning particle swarm optimizers (QCLPSO) to solve multi-period portfolio problem. Multi-stage stochastic f...
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In this paper, we present a decision-makingprocess that uses our proposed quasi-oppositional comprehensive learning particle swarm optimizers (QCLPSO) to solve multi-period portfolio problem. Multi-stage stochastic financial optimization takes order with portfolio in ever-changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization. It brings together all major financial-related decision in a single consistent structure and integrates investment strategies, liability decisions and savings strategies in an all-around fashion. The objective function is classical return-variance function. The performance of our algorithm is demonstrated by optimizing the allocation of cash and various stocks in SSE 180 Index. Experiments are conducted to compare performance of the portfolio optimized by different objective functions with PSO and genetic algorithm (GA) in the terms of efficient frontiers.
When the case information is described in natural language and not complete, to deal with problems of case retrieval in Case-Based Reasoning (CBR), Extension theory is introduced to aid it. Firstly, the case index is ...
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When the case information is described in natural language and not complete, to deal with problems of case retrieval in Case-Based Reasoning (CBR), Extension theory is introduced to aid it. Firstly, the case index is created based on the Extension model. Secondly, the main characteristics of the case are formalized to store in the case base to realize the data compression. Finally, case retrieval algorithm is designed. The basic and the advanced retrieve strategy supplement each other. This algorithm lowers the request of the professional level and improves the efficiency of retrieval. The approach is of practical significance in problem solving, such as auto fault diagnosis.
Consumers' online information seeking process consists of two alternatelyjoining behaviors: intra-site visit and cross-site transfer. Current studiesmainly focused on the intra-site visit behavior while cross-site...
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This paper proposes a scheduling strategy which divides OLAM (Online Analytical Mining) tasks into query tasks, mining tasks and updating tasks in order to improve the efficiency of Bl (Business Intelligence) systems....
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This paper proposes a scheduling strategy which divides OLAM (Online Analytical Mining) tasks into query tasks, mining tasks and updating tasks in order to improve the efficiency of Bl (Business Intelligence) systems. OLAM mechanism is considered as the seamless integration of OLAP (Online Analytical processing) and data mining on data cubes, according to which the scheduling strategy focuses on. This paper emphasizes the interaction of OLAP, data mining and data cube updating, illustrates the details of the OLAM scheduling strategy and demonstrates its efficiency by performing it in a financial Bl system.
This paper proposes, for the first time, a new issue called utility paradox which can not be solved effectively by the existing methods in evidence theory. A utility analysis method considering the subjective preferen...
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This paper proposes, for the first time, a new issue called utility paradox which can not be solved effectively by the existing methods in evidence theory. A utility analysis method considering the subjective preference of decision maker is presented to solve the utility paradox problem. First, the degrees of belief given by experts are transformed into the utility beliefs of decision makers by a utility-belief function. The utility beliefs determine decision makers' action. Second, the rule of combination in evidence theory is employed to combine the utility beliefs. This paper gives the properties the utility-belief function should have, then a specific function is given and its validation is proved. Experiment shows the method is valid to solve the utility paradox problem.
This paper considers the problem of uniform parallel machine scheduling with unequal release dates so as to minimize makespan. This problem is proved to an NP-hard problem. Heuristics in existence for the problem are ...
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ISBN:
(纸本)9781424425020
This paper considers the problem of uniform parallel machine scheduling with unequal release dates so as to minimize makespan. This problem is proved to an NP-hard problem. Heuristics in existence for the problem are analyzed, and then we present an improved algorithm. The performance of the algorithms by experiment is also analyzed. The heuristic is further extended by applying the method of variable neighborhood search, which is used for improving the quality of the solutions obtained by the original heuristics.
This paper considers how to design coordination strategies to achieve the coordination in decentralized supply chain, considering pricing, inventory and transportation cost simultaneously under the price sensitive env...
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ISBN:
(纸本)9781424425020
This paper considers how to design coordination strategies to achieve the coordination in decentralized supply chain, considering pricing, inventory and transportation cost simultaneously under the price sensitive environment. Based on a supplier-Stackelberg game structure, we analyze the non-cooperation decision model and design the algorithm. Then we propose the cooperation decision model and design the algorithm. Revenue sharing and price discount strategies are used to coordinate the decentralized supply chain. The experimental results show that both revenue sharing and price discount strategy can efficiently make the coordination of the supplier and the retailer.
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