A weight space-based approach to fuzzy multiple-objective linear programming

In this paper, the effects of uncertainty on multiple-objective linear programming models are studied using the concepts of fuzzy set theory. The proposed interactive decision support system is based on the interactive exploration of the weight space. The comparative analysis of indifference regions on the various weight spaces (which vary according to intervals of values of the satisfaction degree of objective functions and constraints) enables to study the stability and evolution of the basis that correspond to the calculated efficient solutions with changes of some model parameters.http://www.sciencedirect.com/science/article/B6V8S-45S9DHF-2/1/f597062363c29e9bb464a6ba6f21f0d

COTS Evaluation

This article presents an extensive literature review of the empirical studies carried out in past for evaluation and selection of components during the design phase of Component Based Software Systems (CBSS). In CBSS approach the software systems can be developed by selecting appropriate components which then are assembled to form a complete software system. These Components can be either of the two (a) COTS (Commercial-off-the-Shelf) components or (b) Inhouse built components. These components are selected based on different parameters of cost, reliability, delivery time etc. Therefore, optimal selection of the components plays a vital role in development of CBSS as it saves time and effort. Related articles appearing in the International Journals from 1992 to 2014 are gathered and are critically analyzed. Based on the review it is seen that some of the important issues have not been explored fully. Hence there is scope of improvement which paves the path for future work

Domination and Decomposition in Multiobjective Programming

During the last few decades, multiobjective programming has received much attention for both its numerous theoretical advances as well as its continued success in modeling and solving real-life decision problems in business and engineering. In extension of the traditionally adopted concept of Pareto optimality, this research investigates the more general notion of domination and establishes various theoretical results that lead to new optimization methods and support decision making. After a preparatory discussion of some preliminaries and a review of the relevant literature, several new findings are presented that characterize the nondominated set of a general vector optimization problem for which the underlying domination structure is defined in terms of different cones. Using concepts from linear algebra and convex analysis, a well known result relating nondominated points for polyhedral cones with Pareto solutions is generalized to nonpolyhedral cones that are induced by positively homogeneous functions, and to translated polyhedral cones that are used to describe a notion of approximate nondominance. Pareto-oriented scalarization methods are modified and several new solution approaches are proposed for these two classes of cones. In addition, necessary and sufficient conditions for nondominance with respect to a variable domination cone are developed, and some more specific results for the case of Bishop-Phelps cones are derived. Based on the above findings, a decomposition framework is proposed for the solution of multi-scenario and large-scale multiobjective programs and analyzed in terms of the efficiency relationships between the original and the decomposed subproblems. Using the concept of approximate nondominance, an interactive decision making procedure is formulated to coordinate tradeoffs between these subproblems and applied to selected problems from portfolio optimization and engineering design. Some introductory remarks and concluding comments together with ideas and research directions for possible future work complete this dissertation

Unified multiobjective optimization scheme for aeroassisted vehicle trajectory planning

In this work, a multiobjective aeroassisted trajectory optimization problem with mission priority constraints is constructed and studied. To effectively embed the priority requirements into the optimization model, a specific transformation technique is applied and the original problem is then transcribed to a single-objective formulation. The resulting single-objective programming model is solved via an evolutionary optimization algorithm. Such a design is unlike most traditional approaches where the nondominated sorting procedure is required to be performed to rank all the objectives. Moreover, in order to enhance the local search ability of the optimization process, a hybrid gradient-based operator is introduced. Simulation results indicate that the proposed design can produce feasible and high-quality flight trajectories. Comparative simulations with other typical methods are also performed, and the results show that the proposed approach can achieve a better performance in terms of satisfying the prespecified priority requirements

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested