Integrating legacy IT assets and new commercial software components together into a flexible IT architecture is one of open challenges facing modern enterprises today. Most of previous studies focused on using re-engi...
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Integrating legacy IT assets and new commercial software components together into a flexible IT architecture is one of open challenges facing modern enterprises today. Most of previous studies focused on using re-engineering to improve the flexibility of IT architectures, rather than employing optimization theory in architecture design problem, especially the problem of component selection and re-allocation in IT architecture. Moreover, a scant amount of literature is available on considering the architectural flexibility and integration cost simultaneously. To fill in this gap, based on a modified quantitative method of measuring the relationship between couplings and cohesions in architecture, we devise a nonlinear multi-objective binary integer programming to select components from legacy candidates and commercial candidates, and to group them into services under the service-oriented architecture (SOA) environment. The customized SPEA2 algorithm is further used to solve the problem, and some managerial insights are provided based on experiments and sensitivity analysis with the model.
To deal with the multi-objective optimization problems (MOPS), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversit...
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To deal with the multi-objective optimization problems (MOPS), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail. (C) 2018 Elsevier Inc. All rights reserved.
This paper analyzes a multi-objective variant of the well-known Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP) in order to address the classical conflict between cost minimization (represen...
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This paper analyzes a multi-objective variant of the well-known Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP) in order to address the classical conflict between cost minimization (represented by the TSP) and customer waiting time minimization (represented by the TRP). By simultaneously considering different scenarios with individual travel times, uncertainty in travel data is handled. We interpret each travel time scenario as an individual objective function and introduce deterministic multi-objective counterpart models denoted as the Multi-objective TSP (MOTSP), Multi-objective TRP (MOTRP), and the combined Multi-objective TSP and TRP models (MOTSRP), respectively. Problems with and without additional deadline restrictions are considered, and the complexity status of computing the Pareto fronts of various problem variants for different underlying networks is resolved. As a particularly interesting case, we consider the MOTSRP with deadlines on a line and show that the problem is intractable even in this simple setting. Nevertheless, we propose a Dynamic programming approach that solves random instances to optimality in reasonable time. Moreover, the computational study additionally evaluates the average complexity of the Line-MOTSRP with deadlines for different numbers of scenarios. The computational study also analyzes the Pareto fronts that are generated for specifically designed extremal instances. (C) 2019 Elsevier Ltd. All rights reserved.
University timetabling has traditionally been studied as an operational problem where the goal is to assign lectures to rooms and timeslots and create timetables of high quality for students and teachers. Two other im...
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University timetabling has traditionally been studied as an operational problem where the goal is to assign lectures to rooms and timeslots and create timetables of high quality for students and teachers. Two other important decision problems arise before this can be solved: what rooms are necessary, and in which teaching periods? These decisions may have a large impact on the resulting timetables and are rarely changed or even discussed. This paper focuses on solving these two strategic problems and investigates the impact of these decisions on the quality of the resulting timetables. The relationship and differences between operational, tactical and strategic timetabling problems are reviewed. Based on the formulation of curriculum-based course timetabling and data from the Second International Timetabling Competition (ITC 2007), three new bi-objective mixed-integer models are formulated. We propose an algorithm based on the E-constraint method to solve them. The algorithm can be used to analyze the impact of having different resources available on most timetabling problems. Finally, we report results on how the three objectives rooms, teaching periods, and quality - influence one another. (C) 2017 Elsevier B.V. All rights reserved.
The nondominated frontier (NDF) of a biobjective optimization problem is defined as the set of feasible points in the objective function space that cannot be improved in one objective function value without worsening ...
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The nondominated frontier (NDF) of a biobjective optimization problem is defined as the set of feasible points in the objective function space that cannot be improved in one objective function value without worsening the other. For a biobjective mixed-binary linear programming problem (BOMBLP), the NDF consists of some combination of isolated points and open, closed, or half-open/half-closed line segments. Some algorithms have been proposed in the literature to find an approximate or exact representation of the NDF. We present a one direction search (ODS) method to find the exact NDF of BOMBLPs. We provide a theoretical analysis of the ODS method and show that it generates the exact NDF. We also conduct a comprehensive experimental study on a set of benchmark problems and show the solution quality and computational efficacy of our algorithm. (C) 2017 Elsevier B.V. All rights reserved.
In this paper, we introduce some qualification conditions for a linear vector semi-infinite programming problem. The new qualification conditions are used for development of necessary conditions for weak efficient, is...
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In this paper, we introduce some qualification conditions for a linear vector semi-infinite programming problem. The new qualification conditions are used for development of necessary conditions for weak efficient, isolated efficient and epsilon-efficient solutions of such a problem. Sufficient conditions for the existence of such solutions are also provided. In addition, a new general scalarization technique for solving the reference problem is presented. Finally, we propose a type of Wolf dual problem and examine weak\strong duality relations.
The question we address is how robust solutions react to changes in the uncertainty set. We prove the location of robust solutions with respect to the magnitude of a possible decrease in uncertainty, namely when the u...
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The question we address is how robust solutions react to changes in the uncertainty set. We prove the location of robust solutions with respect to the magnitude of a possible decrease in uncertainty, namely when the uncertainty set shrinks, and convergence of the sequence of robust solutions. In decision making, uncertainty may arise from incomplete information about people's (stakeholders, voters, opinion leaders, etc.) perception about a specific issue. Whether the decision maker (DM) has to look for the approval of a board or pass an act, they might need to define the strategy that displeases the minority. In such a problem, the feasible region is likely to unchanged, while uncertainty affects the objective function. Hence the paper studies only this framework. (c) 2018 The Authors. Published by Elsevier Ltd.
We present an integral approach to solving multiple criteria decision problems in sequences of intelligence, modeling, choice and review phases, often iterated, to identify the most preferred decision variant. The app...
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ISBN:
(纸本)9783319655451;9783319655444
We present an integral approach to solving multiple criteria decision problems in sequences of intelligence, modeling, choice and review phases, often iterated, to identify the most preferred decision variant. The approach taken is human-centric, with the user taking the final decision being a sole and sovereign actor in the decision making process. To ensure generality, no assumption about the Decision Maker preferences or behavior is made. Likewise, no specific assumption about the underlying formal model is made. The intended goal of the approach is to lower the cognitive barrier related to unsupported use of multicriteria methodologies in day-to-day practice. We present successful application of this approach to a number of practical problems.
Considering the restriction of physical resource and the environment, we propose a new broadcast path algorithm based on genetic algorithm and ideal point model. The proposed algorithm combines multiple constrains and...
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ISBN:
(纸本)9783037851371
Considering the restriction of physical resource and the environment, we propose a new broadcast path algorithm based on genetic algorithm and ideal point model. The proposed algorithm combines multiple constrains and utilizes the advantages of genetic algorithm in multiple objective programming. Simulation results show that the proposed algorithm reveal better performance on efficiency resource utilization.
Recently, a novel approach (to be referred to as CEU) was introduced for the frequently arising problem of combining the conflicting criteria of equity and utilitarianism. This paper provides additional insights into ...
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Recently, a novel approach (to be referred to as CEU) was introduced for the frequently arising problem of combining the conflicting criteria of equity and utilitarianism. This paper provides additional insights into CEU and assesses its added value for practice by comparing it with a commonly used extended goal programming (EGP) approach. The comparison comprises the way of balancing equity and utilitarianism, the number and spacing of solutions, discrete versus continuous nature, method-specific parameters, distance to the Pareto front, and computational effort. CEU balances between equity and utilitarianism in a way that is basically different from using a convex combination of these two criteria. Moreover, CEU's parameter has an intuitive interpretation. The set of solutions generated by CEU is smaller and more widely spaced than EGP's set of solutions, which can be an advantage for the decision maker. CEU generates solutions on the Pareto front of the decision maker's n-criteria problem. However, CEU's way of balancing equity and utilitarianism causes a (small) distance to the Pareto front of the associated bicriteria problem on the aggregate criteria. Reporting this distance will support the decision maker to assess whether the achieved balance is worth its price. Using CEU may require a larger computational effort than using EGP.
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