A General Approach of Quality Cost Management Suitable for Effective Implementation in Software Systems

Investments in quality are best quantified by implementing and managing quality cost systems. A review of various opinions coming from practitioners and researchers about the existent quality cost models reveals a set of drawbacks (e.g. too theoretical and too close to ideal cases; too academic, with less practical impact; too much personalized to particular business processes, with difficulties in extrapolating to other cases; not comprising all dimensions of a business system). Using concepts and tools in quality management theory and practice and algorithms of innovative problem solving, this paper formulates a novel approach to improve practical usability, comprehensiveness, flexibility and customizability of a quality cost management system (QCMS) when implementing it in a specific software application. Conclusions arising from the implementation in real industrial cases are also highlighted.Quality Costs, Performance Planning and Monitoring, Quality Management, Quality Cost Software

A General Approach of Quality Cost Management Suitable for Effective Implementation in Software Systems

Investments in quality are best quantified by implementing and managing quality cost systems. A review of various opinions coming from practitioners and researchers about the existent quality cost models reveals a set of drawbacks (e.g. too theoretical and too close to ideal cases; too academic, with less practical impact; too much personalized to particular business processes, with difficulties in extrapolating to other cases; not comprising all dimensions of a business system). Using concepts and tools in quality management theory and practice and algorithms of innovative problem solving, this paper formulates a novel approach to improve practical usability, comprehensiveness, flexibility and customizability of a quality cost management system (QCMS) when implementing it in a specific software application. Conclusions arising from the implementation in real industrial cases are also highlighted

Study of SOA Component Dynamic Scheduling Based on Mobile Agent Coalition

Service-oriented components differ greatly with the traditional ones in the Service-Oriented Architecture. The ways of scheduling components seamlessly according to the agile computing needs to fit the e-business requirements is the key technology in the highly distributed, paralleled environment. In this paper, Based on the Multi-Agent Coalition, a new service-oriented component dynamic scheduling model is proposed, including the Multi-Agent Organization to schedule and coordinate the component assembly, the design of virtual execution task list table and self-learning algorithm, the definition of the Services component model, and the mechanism of collaboration Agents to search, discovery, concurrent schedule, dynamic assembly when execution in an heterogeneous network environment. To a large extent, the thesis solves the traditional problem of over-emphasis on centralized control logic, which leads to lacking flexibility in e-Business computing presently, and helps e-business service-oriented components become more adaptive, mobility and intelligence

Pseudo-NK: an Enhanced Model of Complexity

This paper is based on the acknowledgment that NK models are an extremely usefu l tool in order to represent and study the complexity stemming from interactions among components of a system. For this reason NK models have been applied in many domains, such as Organizational Sciences and Economics, as a simple and powerful tool for the representation of complexity. However, the paper suggests that NK suffers from un-necessary limitations and difficulties due to its peculiar implementation, originally devised for biological phenomena. We suggest that it is possible to devise alternative implementations of NK that, though maintaining the core aspects of the NK model, remove its major limitations to applications in new domains. The paper proposes one such a model, called pseudo-NK (pNK) model, which we describe and test. The proposed model appears to be able to replicate most, if not all, the properties of standard NK models, but also to offer wider possibilities. Namely, pNK uses real-valued (instead of binary) dimensions forming the landscape and allows for gradual levels of interaction among components (instead of presence-absence). These extensions provide the possibility to maintain the approach at the original of NK (and therefore, the compatibility with former results) and extend the application to further domains, where the limitations posed by NK are more striking.NK model, Simulation models, Complexity, Interactions

MASCEM: Optimizing the performance of a multi-agent system

The electricity market sector has suffered massive changes in the last few decades. The worldwide electricity market restructuring has been conducted to potentiate the increase in competitiveness and thus decrease electricity prices. However, the complexity in this sector has grown significantly as well, with the emergence of several new types of players, interacting in a constantly changing environment. Several electricity market simulators have been introduced in recent years with the purpose of sup-porting operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents a new, enhanced version of MASCEM (Multi-Agent System for Competitive Electricity Markets), an electricity market simulator with over ten years of existence, which had to be restructured in order to be able to face the highly demanding requirements that the decision support in this field requires. This restructuring optimizes the performance of MASCEM, both in results and execution time.info:eu-repo/semantics/publishedVersio

Improved Convergence Rates for Distributed Resource Allocation

In this paper, we develop a class of decentralized algorithms for solving a convex resource allocation problem in a network of $n$ agents, where the agent objectives are decoupled while the resource constraints are coupled. The agents communicate over a connected undirected graph, and they want to collaboratively determine a solution to the overall network problem, while each agent only communicates with its neighbors. We first study the connection between the decentralized resource allocation problem and the decentralized consensus optimization problem. Then, using a class of algorithms for solving consensus optimization problems, we propose a novel class of decentralized schemes for solving resource allocation problems in a distributed manner. Specifically, we first propose an algorithm for solving the resource allocation problem with an $o(1/k)$ convergence rate guarantee when the agents' objective functions are generally convex (could be nondifferentiable) and per agent local convex constraints are allowed; We then propose a gradient-based algorithm for solving the resource allocation problem when per agent local constraints are absent and show that such scheme can achieve geometric rate when the objective functions are strongly convex and have Lipschitz continuous gradients. We have also provided scalability/network dependency analysis. Based on these two algorithms, we have further proposed a gradient projection-based algorithm which can handle smooth objective and simple constraints more efficiently. Numerical experiments demonstrates the viability and performance of all the proposed algorithms