Topic
Goal programming
About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.
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TL;DR: This study formulates the QSC problem as amulti-criteria goal programming (MCGP) model, and develops a multi-population genetic algorithm (MGA) to solve the model, showing empirical comparisons demonstrate MGA outperforms to the GA.
Abstract: Internet of things (IoT) will create new opportunities to build applications that better integrate real-time state of the industry. With Web services accomplishing similar function proliferated, industrial enterprises have to choose appropriate Web services according Quality of Service (QoS) properties. It introduces the problem of QoS-oriented service composition (QSC). This study formulates the QSC problem as a multi-criteria goal programming (MCGP) model, and develops a multi-population genetic algorithm (MGA) to solve the model. MCGP not only automatically assigns high quality Web services to combine a composite service, but also finds non inferior composite services by relaxing QoS constraints to satisfy users' QoS requirements. Empirical comparisons demonstrate MGA outperforms to the GA. Moreover, the experiments indicate MGA is capable to solve the large-scale QSC problem in terms of efficiency and scalability.
33 citations
TL;DR: This paper addresses a general Goal Programming problem with linear objectives, convex constraints, and an arbitrary componentwise nondecreasing norm to aggregate deviations with respect to targets, and a dual is derived, enabling the set of optimal solutions geometrically.
Abstract: In this paper we address a general Goal Programming problem with linear objectives, convex constraints, and an arbitrary componentwise nondecreasing norm to aggregate deviations with respect to targets. In particular, classical Linear Goal Programming problems, as well as several models in Location and Regression Analysis are modeled within this framework. In spite of its generality, this problem can be analyzed from a geometrical and a computational viewpoint, and a unified solution methodology can be given. Indeed, a dual is derived, enabling us to describe the set of optimal solutions geometrically. Moreover, Interior-Point methods are described which yield an $\varepsilon$-optimal solution in polynomial time.
33 citations
TL;DR: The paper gives a systematic survey and analysis of the lexicographic GP models of RPM and the corresponding preference models are formalised and analysed with respect to target values interpretations as well as the Pareto efficiency of their solutions.
Abstract: This paper discusses connections between the multi-criteria techniques of goal programming (GP) and the reference point method (RPM). Both approaches use a certain target point in the criterion (outcome) space as a key element to model decision maker preferences. Therefore, RPM can be expressed, similarly to GP, in the modelling framework of deviational variables. The paper gives a systematic survey and analysis of the lexicographic GP models of RPM. The corresponding preference models are formalised and analysed with respect to target values interpretations as well as the Pareto efficiency of their solutions. The properties of equity among the individual achievements of solutions are also analysed with respect to the Rawlsian principle of justice.
33 citations
TL;DR: An optimization-based approach to simultaneously solve the Network Design and the Frequency Setting phases on the context of railway rapid transit networks by first applying a Corridor Generation Algorithm and then a Line Splitting Algorithm to deal with multiple line construction.
33 citations
24 Oct 2008
TL;DR: A distributed resource allocation algorithm is proposed and shown to provide good fairness and yields significantly better system throughput compared with the proportional-rate algorithm in resource-abundant situations.
Abstract: We study the problem of allocating subchannels, bits, and powers for variable rate services in an OFDM based cognitive radio (CR) system, in which available system resources are highly dynamic. In a resource-limited situation under which the nominal rate requirements of users cannot be satisfied, it is desirable to provide fair degradation among users. In a situation with abundant resources, we may choose to maximize system throughput while ensuring that user nominal rate requirements are met. The problem is formulated as a single objective nonlinear optimization problem using techniques from goal programming. A distributed resource allocation algorithm is proposed and simulation results are obtained which show that the proposed distributed algorithm provides good fairness and aggregate bit rates close to (within 8% of) optimal values.
33 citations