Test-suite minimization is one key technique for optimizing the software testing process. Due to the need to balance multiple factors, multi-criteria test-suite minimization (MCTSM) becomes a popular research topic in...
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Test-suite minimization is one key technique for optimizing the software testing process. Due to the need to balance multiple factors, multi-criteria test-suite minimization (MCTSM) becomes a popular research topic in the recent decade. The MCTSM problem is typically modeled as integer linear programming (ILP) problem and solved with weighted-sum single objective approach. However, there is no existing approach that can generate sound (i.e., being Pareto-optimal) and complete (i.e., covering the entire Pareto front) Pareto-optimal solution set, to the knowledge of the authors. In this work, we first prove that the ILP formulation can accurately model the MCTSM problem and then propose the multi-objective integer programming (MOIP) approaches to solve it. We apply our MOIP approaches on three specific MCTSM problems and compare the results with those of the cutting-edge methods, namely, Nonlinearformulation_LinearSolver (NF_LS) and two multi-objective Evolutionary Algorithms (MOEAs). The results show that our MOIP approaches can always find sound and complete solutions on five subject programs, using similar or significantly less time than NF_LS and two MOEAs do. The current experimental results are quite promising, and our approaches have the potential to be applied for other similar search-based software engineering problems.
Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify...
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Optimization of an intra-city express delivery network from three to two levels is of great interest to suppliers and customers for reducing costs and improving service efficiency. One feasible solution is to identify critical nodes in the three-level network and upgrade them as transshipment facilities in the two-level one. However, traditional optimization models seldom combine empirical business data, composite metrics, and objective evaluation rules. We proposed an approach integrating empirical data, multi-criteria decision-making methods based on the real-world application of the SF Express Chengdu branch. We also developed a mathematical optimization model using statistical and operations management techniques combined with logistics expertise for a location decision. First, the appropriateness of each service point as a candidate transshipment facility is evaluated from internal and external perspectives by applying multiple centrality assessment from complex network theory and fuzzy Technique for Order Preference by Similarity to an Ideal Solution, respectively. Second, 16 candidate transshipment facilities are selected by combining these two ways. Then, a multi-objective integer programming model is built to obtain the optimal number, locations of transshipment facilities, and the corresponding service points covered by each transshipment facility. Using this multi-methodologic approach, we show that the optimized two-level network is economically feasible and simply applicable, with the total cost and average delivery time reduced by 18.41% and 6 h, respectively. This article is of practical significance and provides an important reference for optimizing ground express service networks for other large cities.
Connecting vehicles to the infrastructure and benefiting from the services provided by the network is one of the main objectives to increase safety and provide well-being for passengers. Providing such services requir...
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Connecting vehicles to the infrastructure and benefiting from the services provided by the network is one of the main objectives to increase safety and provide well-being for passengers. Providing such services requires finding suitable gateways to connect the source vehicles to the infrastructure. The major of applications with high bandwidth demand that can cause network congestion, particularly in urban areas with a highdensity vehicle. This work introduces a novel gateway selection algorithm for vehicular networks in urban areas, consisting of two phases. The first phase identifies the best gateways among the deployed vehicles using multi-objective integer programming. While in the second phase, reinforcement learning is employed to select a suitable gateway for any vehicular node in need to access the VANET infrastructure. The proposed model is evaluated and compared to other existing solutions. The obtained results show the efficiency of the proposed system in identifying and selecting the gateways.
In this article we introduce robustness measures in the context of multi-objectiveinteger linear programming problems. The proposed measures are in line with the concept of decision robustness, which considers the un...
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In this article we introduce robustness measures in the context of multi-objectiveinteger linear programming problems. The proposed measures are in line with the concept of decision robustness, which considers the uncertainty with respect to the implementation of a specific solution. An efficient solution is considered to be decision robust if many solutions in its neighborhood are efficient as well. This rather new area of research differs from robustness concepts dealing with imperfect knowledge of data parameters. Our approach implies a two-phase procedure, where in the first phase the set of all efficient solutions is computed, and in the second phase the neighborhood of each one of the solutions is determined. The indicators we propose are based on the knowledge of these neighborhoods. We discuss consistency properties for the indicators, present some numerical evaluations for specific problem classes and show potential fields of application.
We present a learning-oriented interactive reference direction algorithm for solving multi-objective convex nonlinear integerprogramming problems. At each iteration the decision-maker (DM) sets his/her preferences as...
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We present a learning-oriented interactive reference direction algorithm for solving multi-objective convex nonlinear integerprogramming problems. At each iteration the decision-maker (DM) sets his/her preferences as aspiration levels of the objective functions. The modified aspiration point and the solution found at the previous iteration define the reference direction. Based on the reference direction, we formulate a mixed-integer scalarizing problem with specific properties. By solving this problem approximately, we find one or more integer solutions located close to the efficient surface. At some iteration (usually at the last iteration), the DM may want to solve the scalarizing problem to obtain an exact (weak) efficient solution. Based on the proposed algorithm, we have developed a research-decision support system that includes one exact and one heuristic algorithm. Using this system, we illustrate the proposed algorithm with an example, and report some computational results. International Federation of Operational Research Societies 2001.
We develop an interactive algorithm that approximates the most preferred solution for any multi-objectiveinteger program with a desired level of accuracy, provided that the decision maker's (DM's) preferences...
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We develop an interactive algorithm that approximates the most preferred solution for any multi-objectiveinteger program with a desired level of accuracy, provided that the decision maker's (DM's) preferences are consistent with a nondecreasing quasiconcave value function. Using pairwise comparisons of the DM, we construct convex cones and eliminate the inferior regions that are close to being dominated by the cones in addition to the regions dominated by the cones. The algorithm allows the DM to change the desired level of accuracy during the solution process. We test the performance of the algorithm on a set of multi-objective combinatorial optimization problems. It performs very well in terms of the quality of the solution found, the solution time, and the required preference information. (C) 2018 Elsevier Ltd. All rights reserved.
The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to ta...
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The implementation of efficient strategic decisions such as process design and capacity investment under uncertainty, during the product development process, is critical for the pharmaceutical industry. However, to tackle these problems the widely used multi-stage/scenario-based optimization formulations are still ineffective, especially for the first-stage (here-and-now) solutions where uncertainty has not yet been revealed. This study extends the authors' previous work addressing the stochastic product-launch planning problem, by developing a new multi-objective integer programming model, embedded in a unified decision-making framework, to obtain the final design strategy that "maximizes" productivity while considering the decision-maker preferences. An approximation of the efficient Pareto-front is determined, and a subsequent Pareto solutions analysis is made to guide the decision process. The developed approach clearly identifies the process designs and production capacities that "maximize" productivity as well as the most promising solutions region for investment. Moreover, a good balance between investment and capacity allocation was achieved. (C) 2018 Elsevier Ltd. All rights reserved.
This paper aims to provide a scientific approach that indicates the need to focus on renewable energypotential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy tech-nologies a few yea...
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This paper aims to provide a scientific approach that indicates the need to focus on renewable energypotential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy tech-nologies a few years ago. Accordingly, the issue of determining the best renewable resources for thecountry has been brought to the agenda in recent years. The issue was considered to be a limited multi-objective optimization problem allowing us to achieve a reliable result. However, the parameter of eachresource was considered as a range value, rather than traditionally expressed as an exact value, and amulti-objective decision problem was developed with an interval coefficient. This method allows us toobtain more accurate and reliable results without the need to resort to normalization methods used toeliminate unit differences. According to the results of this study, the most convenient alternatives forTurkey are hydro, wind, and solar power. Thefindings also support decision policies aimed at reachingtargets for the electricity sector in 2023, as put forth by the Ministry of Energy and Natural Resources(MENR). (c) 2021 Elsevier Ltd. All rights reserved
In the green backfilling mining of underground coal mines, gangue in a coal seam is used to replace and fill goafs. However, the gas drainage time changes in the amount of gangue output and order in the extraction seq...
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In the green backfilling mining of underground coal mines, gangue in a coal seam is used to replace and fill goafs. However, the gas drainage time changes in the amount of gangue output and order in the extraction sequence of stope blocks, thereby changing the production output. A multi-objective integer programming model was proposed to solve this based on an improved non-dominated sorting genetic algorithm. The results show a feasible gangue filling rate can be found and the extraction sequence for a long-term planning time is optimized. It increases the planned annual output by 26% and reduces equipment idle time.
In this paper, we present an improved methodology to compute the omega-primality of a numerical semigroup. The approach is based on exploiting the structure of the problem on a resolution method for optimizing a linea...
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In this paper, we present an improved methodology to compute the omega-primality of a numerical semigroup. The approach is based on exploiting the structure of the problem on a resolution method for optimizing a linear function over the set of efficient solutions of a multiple objectiveinteger linear programming problem. The numerical experiments show the efficiency of the proposed technique compared to the existing methods.
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