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multi-objective integer programming Approaches for Solving the multi-criteria Test-suite Minimization Problem: Towards Sound and Complete Solutions of a Particular Search-based Software-engineering Problem

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2020年第3期29卷 20-20页

作者： Xue, Yinxing Li, Yan-Fu Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

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.

关键词： Regression testing search-based software engineering test-suite minimization multi-objective integer programming big-M method epsilon-constraint method CWMOIP

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Optimizing the ground intra-city express delivery network: An integrated multiple centrality assessment, multi-criteria decision-making, and multi-objective integer programming model

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JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2024年第4期28卷 525-543页

作者： Liu, Chuang Zhou, Jianjun Gan, Junwei Wu, Yuexin Huang, Yulong Shao, Jiahao Ouyang, Lingyi Chengdu Vocat & Tech Coll Ind Sch Finance & Business Chengdu Sichuan Peoples R China Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Sichuan Tourism Univ Sch Econ & Management Chengdu Sichuan Peoples R China Haikou Univ Econ Dept Gen Educ Foreign Languages Haikou Hainan Peoples R China Sichuan Tourism Univ Sch Econ & Management Chengdu 610100 Sichuan Peoples R China

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.

关键词： Fuzzy TOPSIS ground intra-city express delivery network multi-objective integer programming multiple centrality assessment optimization

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A New Gateway Selection Algorithm Based on multi-objective integer programming and Reinforcement Learning

INFOCOMMUNICATIONS JOURNAL

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INFOCOMMUNICATIONS JOURNAL 2022年第4期14卷 4-10页

作者： Alabbas, Hasanain Huszak, Arpad Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Networked Syst & Serv Budapest Hungary Al Qasim Green Univ Comp Ctr Dept Al Qasim Iraq ELKH BME Cloud Applicat Res Grp Budapest Hungary

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.

关键词： VANET gateway selection multi-objective integer programming reinforcement learning

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Decision space robustness for multi-objective integer linear programming

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ANNALS OF OPERATIONS RESEARCH 2022年第2期319卷 1769-1791页

作者： Stiglmayr, Michael Figueira, Jose Rui Klamroth, Kathrin Paquete, Luis Schulze, Britta Univ Wuppertal Sch Math & Nat Sci Wuppertal Germany Univ Lisbon Inst Super Tecn CEG IST Lisbon Portugal Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

In this article we introduce robustness measures in the context of multi-objective integer 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.

关键词： multi-objective integer programming Decision space robustness Connectedness of efficient solutions Representation Decision analysis

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An Interactive Reference Direction Algorithm For Solving multi-objective Convex Nonlinear integer programming Problems

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International Transactions in Operational Research 2001年第4期8卷 367-380页

作者： Vassilev, Vassil S. Narula, Subhash C. Gouljashki, Vassil G. Institute of Information Technologies Bulgarian Academy of Sciences Sofia 1113 Bulgaria School of Business Virginia Commonwealth University Richmond VA 23284–4000 United States

We present a learning-oriented interactive reference direction algorithm for solving multi-objective convex nonlinear integer programming 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.

关键词： Decision support system multi-objective integer programming multiple criteria Nonlinear programming Reference point Tabu search

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Urban logistics UAV vertiport location and layout planning method

Urban logistics UAV vertiport location and layout planning m...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Wang, Hairu Zhang, Qiqian Zhang, Honghai Feng, Dikun Nanjing University of Aeronautics and Astronautics(NUAA) Nanjing China

ISBN: (纸本)9781510690585

The study addresses the siting and layout problem of unmanned aerial vehicle (UAV) vertiport in urban logistics scenarios. A multi-objective integer programming model is developed, incorporating multiple levels of facilities and multi-model drone delivery strategies. The model considers constraints such as airspace restrictions, UAV performance limitations, and vertiport conditions, with the objectives of minimizing total cost and maximizing customer satisfaction. To solve the model effectively, an improved NSGA-II algorithm is proposed. The algorithm integrates a reverse learning strategy to enhance the quality of the initial population and employs a dynamic crowding distance calculation to improve algorithm performance. Based on geographic information from Shanghai and logistics distribution data from express delivery companies, simulation experiments are conducted to validate the effectiveness of the model and algorithm. The results demonstrate that the proposed model and algorithm are effective. The multi-model UAV delivery strategy significantly optimizes both logistics costs and customer satisfaction compared to single-model strategies. Additionally, incorporating reverse learning and dynamic crowding distance in the NSGA-II algorithm markedly enhances its root-finding ratio and success ratio. In conclusion, the vertiport location selection is an essential part of the early stages of UAV logistics development. This paper has achieved success in the simulated experiment which provides valuable insights and references for achieving low-cost and high-efficiency low-altitude logistics. © 2025 SPIE.

关键词： Improved NSGA-II Algorithm multi-objective integer programming UAV Control UAV vertiport Location Urban Transportation

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An interactive approximation algorithm for multi-objective integer programs

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COMPUTERS & OPERATIONS RESEARCH 2018年第Aug.期96卷 80-90页

作者： Lokman, Banu Koksalan, Murat Korhonen, Pekka J. Wallenius, Jyrki Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey Aalto Univ Sch Business Dept Informat & Serv Econ Helsinki Finland

We develop an interactive algorithm that approximates the most preferred solution for any multi-objective integer 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.

关键词： multi-objective integer programming Approximation algorithm Interactive algorithm

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Strategic decision-making in the pharmaceutical industry: A unified decision-making framework

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COMPUTERS & CHEMICAL ENGINEERING 2018年 119卷 171-189页

作者： Marques, Catarina M. Moniz, Samuel de Sousa, Jorge Pinho Univ Porto Fac Engn Rua Dr Roberto Frias Porto Portugal INESC TEC Rua Dr Roberto Frias Porto Portugal Univ Coimbra Dept Engn Mecan Coimbra Portugal

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.

关键词： Uncertainty Strategic decisions Process design Capacity planning multi-objective integer programming Pharmaceutical industry

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Solving the bi-objective optimisation problem with periodic delivery operations using a lexicographic method

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第8期54卷 2275-2283页

作者： Liu, Cheng-Hsiang Natl Pingtung Univ Sci & Technol Dept Ind Management Neipu Taiwan

Periodic deliveries are typical in a number of real-life applications. Minimising the number of vehicles required to make deliveries to a set of customers with known delivery frequencies is called the problem of vehicle minimisation for periodic deliveries (VMPD). Catering to the welfare of vehicle drivers has now become very important. Consequently, this work integrates the vehicle load balance factor into the VMPD problem by considering both the number of vehicles required to make periodic deliveries and the load balance between vehicles. This work presents integer programming formulations and applies a lexicographic method to this bi-objective VMPD problem. This work also examines whether decomposition can significantly reduce the size and difficulty of basic integer programming formulation in order to output close-to-optimal schedules for large problems within a reasonable computational time. A greedy balancing algorithm is also proposed to use it along with a decomposed integer programming formulation to yield a satisfactory solution in a relatively short time. Computational experiments demonstrate the better competitiveness of the proposed approaches compared to that of the existing approaches.

关键词： lexicographic method periodic delivery load balancing multi-objective integer programming

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Optimization Production Scheduling of Underground Backfilling Mining Based on NSGA-II

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MINING METALLURGY & EXPLORATION 2022年第4期39卷 1521-1536页

作者： Bao, Yu Wang, Yutao Zhao, Liang Zhang, Aijuan China Univ Min & Technol Dept Comp Sci & Technol Daxue Rd Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Mine Digitizat Engn Res Ctr Minist Educ Peoples Republ China Daxue Rd Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Mine Daxue Rd Xuzhou 221116 Jiangsu Peoples R China

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.

关键词： Underground mine production scheduling Underground backfilling mine Non-dominated sorting genetic algorithm II (NSGA-II) multi-objective integer programming

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