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.
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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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.
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 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.
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.
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences f...
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Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decisionprocess of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN.
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