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Multi-objective integer programming approaches to Next Release Problem - Enhancing exact methods for finding whole pareto front

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INFORMATION AND SOFTWARE TECHNOLOGY 2022年第0期147卷 106825-106825页

作者： Dong, Shi Xue, Yinxing Brinkkemper, Sjaak Li, Yan-Fu Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands Tsinghua Univ Dept Ind Engn Beijing Peoples R China

Context: Project planning is a crucial part of software engineering, it involves selecting requirements to develop for the next release. How to make a good release plan is an optimization problem to maximize the goal of revenue under the condition of cost, time, or other aspects, namely Next Release Problem (NRP). Genetic and exact algorithms are used since it was proposed. Objective: We model NRP as bi-objective (revenue, cost) and tri-objective (revenue, cost, urgency) form, and investigate whether exact methods could solve bi-objective and tri-objective instances more efficiently. Methods: The state-of-art integer linear programming (ILP) approach to the bi-objective NRP is epsilon-constraint for finding all non-dominate solutions. To improve its efficiency, we employ CWMOIP (Constrained Weighted Multi-Objective integer programming) and I-EC (improved epsilon-constraint) for solving bi-objective instances. In tri-objective form, we introduce SolRep, an ILP method that optimizes the reference points from sampling, for finding solutions subset within a short time. NSGA-II is implemented as the evolutionary algorithm for the comparison with former methods and it adopts the seeding mechanism. Results : I-EC can find all non-dominated solutions with better performance than both epsilon-constraint and CWMOIP on all instances except for one. I-EC reduces solving time by 19.7% (large instances) and 91.5% (small instances) on average separately compared with epsilon-constraint. SolRep can find evenly distributed solutions and exceed NSGA-II illustrated by several indicators (such as HyperVolume) on tri-objective instances. And each method has its merit in the aspect of speed and number of the solutions. Conclusion: (1) The I-EC can solve all non-dominated solutions with better performance than the state-of-art exact method. (2) SolRep solves large tri-objective instances with more non-dominated solutions and solves small instances with less time compared with seeded NSGA-

关键词： Next Release Problem Multi-objective optimization integer linear programming Search-based software engineering

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On the Evaluation of the Degree of Disability of the Elderly: A Case Study 2nd

On the Evaluation of the Degree of Disability of the Elderly...

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2nd Decision Science Alliance International Summer Conference-DSA ISC

作者： Bonetta, Carlo Filippo Grossi, Luca Alfredo Righini, Giovanni Univ Verona Unita Operativa Complessa Forens Med Verona Italy Azienda Socio Sanitaria Territoriale Crema Unita Operativa Complessa Primary Care Crema Italy Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9783031782374;9783031782381

In multidimensional geriatric assessment, elderly patients are evaluated from different points of view to decide whether to accept or reject their requests for special benefits. The decision is taken in several stages and it does not currently rely on any decision support system. The need to make it more equitable and consistent gave rise to this case study, based on data relating to a retrospective cohort of patients over 67 years of age, who underwent a geriatric examination and geriatric multi-dimensional assessment. To avoid selection and evaluation bias, it was deemed appropriate to consider only patients evaluated with the same methodology and measurement scales. In a preliminary step some inconsistencies were identified in the available data-set. Then, a subset of patients in need for a re-evaluation was defined by a suitable combination of different methods: namely, threshold values related to single indicators, dominance analysis and search for inconsistent pairs. This subset of patients was re-evaluated and a tentative classification model was developed. Therefore, the outcome of the case study was a critical review of the data, the model and the decision-making process.

关键词： Decision science Classification integer linear programming

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The stochastic test collection problem: Models, exact and heuristic solution approaches

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期299卷 945-959页

作者： Douek-Pinkovich, Yifat Ben-Gal, Irad Raviv, Tal Tel Aviv Univ Dept Ind Engn IL-69978 Tel Aviv Israel

The classic test collection problem (TCP) selects a minimal set of binary tests needed to classify the state of a system correctly. The TCP has applications in various domains, such as the design of monitoring systems in engineering, communication, and healthcare. In this paper, we define the stochastic test collection problem (STCP) that generalizes the TCP. While the TCP assumes that the tests' results can be deterministically mapped into classes, in the STCP, the results are mapped to probability distributions over the classes. Moreover, each test and each type of classification error is associated with some cost. A solution of the STCP is a subset of tests and a mapping of their results to classes. The objective is to minimize the weighted sum of the tests' costs and the expected cost of the classification errors. We present an integer linear programming formulation of the problem and solve it using a commercial solver. To solve larger instances, we apply three metaheuristics for the STCP, namely, Tabu Search (TS), Cross-Entropy (CE), and Binary Gravitational Search Algorithm (BGSA). These methods are tested on publicly available datasets and shown to deliver nearly optimal solutions in a fraction of the time required for the exact solution.

关键词： Combinatorial optimization The test collection problem integer linear programming Metaheuristics

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Optimizing the loaded train combination problem at a heavy-haul marshalling station

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年 162卷

作者： Wang, Dian Zhao, Jun Peng, Qiyuan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tran Chengdu 611756 Sichuan Peoples R China

In this paper, we address a loaded train combination problem at a heavy-haul marshalling station. This problem lies on assigning/combining loaded inbound trains to/into heavier outbound trains, and determining the actual departure time of outbound trains. By using a time-discretized modelling technique, we formulate the studied problem as a mixed integer linear programming model, to minimize the weighted sum of the total railcar dwell time and the total railcar extra transfer time. Several variable fixing rules and valid inequalities are designed to strengthen the proposed model. An iterated search algorithm, in which an intensification search and a neighborhood search are executed iteratively, is developed to solve large-scale problems efficiently. In the intensification search, the proposed model is reduced into a simpler assignment model to compute an incumbent solution, by fixing the departure time of outbound trains. In the neighborhood search, a rolling horizon heuristic is designed to improve the incumbent solution, by dividing the departure period of outbound trains into multiple overlapped shorter stages, and re-optimizing the solution in each stage while maintaining that in other stages unchanged. Different scales of instances, randomly generated from a real-world marshalling station in one of the busiest Chinese heavy-haul railways, are used to test the proposed approaches. Computational results demonstrate that our algorithm computes (near )optimal solutions (with a maximum relative gap of 1.73% only) for all the tested instances within a maximum computation time shorter than 13 min. Our approach can also find better solutions (with an average improvement rate of 3.27%) in shorter computation time, when compared to the empirical method used in practice.

关键词： Heavy-haul railway transportation Marshalling station management Loaded train combination integer linear programming Iterated search algorithm

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Lua APIs for mathematical optimization 11

Lua APIs for mathematical optimization

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Stanojević, Milan Stanojević, Bogdana University of Belgrade Faculty of Organizational Sciences Jove Ilića 154 Belgrade11000 Serbia Mathematical Institute The Serbian Academy of Sciences and Arts Kneza Mihaila 36 Belgrade11000 Serbia

In this paper we present our Lua programming language libraries that enable modeling and solving mixed integer linear and nonlinear mathematical optimization problems. On one side, these libraries provide a framework for developing algorithms written in Lua that use certain well known solvers within more complex procedures. On the other side, they facilitate the transformation of both input and output data of a mathematical programming problem, in order to compute the standardized coefficients that have to be transmitted to the solvers or prepare solution of solved problems for further data processing. Both of these use cases, together with Lua programming language simplicity, versatility and performance, make Lua a suitable programming language for use in scientific and engineering researches. © 2024 The Authors.

关键词： integer linear programming

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Construction of Guaranteeing Controls in the Simplest Export Enterprise Model 17

Construction of Guaranteeing Controls in the Simplest Export...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Sytov, Anatolii Federal Research Center Computer Science and Control of the Russian Academy of Sciences Moscow Russia

The paper considers the simplest model of an export enterprise operating under uncertainty. The set of possible values of uncertain factors is specified using a scenario tree. Optimization problems for finding guarant... 详细信息

ISBN: (纸本)9798350375718

关键词： integer linear programming

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Cost Efficient Mobile Trajectory Planning in Barrier Sweep Coverage 2024

Cost Efficient Mobile Trajectory Planning in Barrier Sweep C...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Sen, Rinku Khatua, Sunirmal Das, Rajib K. University of Calcutta West Bengal Calcutta India

ISBN: (纸本)9798400709722

The problem of detecting intrusion over a curved barrier by deploying sensors is called the barrier coverage problem. In the barrier sweep coverage problem, the objective is to visit every point on the curve at intervals of no more than a given period t called the sweep period. This is a challenging problem, requiring the determination of a tour plan for mobile devices moving at a given speed to ensure sweep coverage within the given sweep period. This paper proposes an algorithm to solve this problem, where we divide the curve C into N small segments of almost equal length. The proposed algorithm has a time complexity of O(N2) and an approximate ratio of 2, which is better than the previous best ratio of 13/3. Additionally, we presented an integer linear Program (ILP) formulation of the problem and conducted experiments to compare the proposed algorithm with the optimal solution obtained from ILP. The results indicate that our algorithm provides solutions very close to the optimal solution obtained from ILP, thereby establishing its effectiveness for solving the barrier sweep coverage problem. © 2024 Copyright held by the owner/author(s).

关键词： integer linear programming

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Optimizing Information Freshness for MEC-Enabled Cooperative Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 13127-13140页

作者： Sorkhoh, Ibrahim Assi, Chadi Ebrahimi, Dariush Sharafeddine, Sanaa Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 2W1 Canada Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 2W1 Canada Thompson Rivers Univ Dept Comp Sci Kamloops BC V2C 0C8 Canada Lebanese Amer Univ Dept Comp Sci Beirut 11022801 Lebanon

Fully automated vehicles deployed with high computational/perceptive capabilities will soon become a reality. Such capabilities enable the cooperation among vehicles and the realization of interacting autonomous driving systems. Edge computing has emerged to provide a plethora of computational services to reduce network latency. Applications at the edge that apply analytics on the sensory data are therefore indispensable for self-driving vehicles. We consider in this paper a network that interconnects vehicles to an edge server at a roadside unit. Each vehicle extracts multiple information by sampling multiple processes and sends them to the corresponding edge application. To make timely decisions, ``fresh'' information needs to be offloaded, processed, and delivered back to vehicles;in this context, we adopt a new metric called Age of Information (AoI) that has been lately used to measure the freshness of information. We seek to jointly schedule vehicles' transmission of information and schedule information processing at the edge to minimize the AoI of all processes. We mathematically formulate the problem and prove its NP-Hardness. To overcome this hardness, we propose a logic-based Benders decomposition to divide the problem into a master and several subproblems. Then, we present an exact polynomial-time solution for the subproblems, a scalable heuristic for the master, and devise a valid yet efficient Benders cut. We implement the system simulation on the well-known traffic simulator SUMO and compare the decomposition with CPLEX branch-and-cut;Although the problem is highly intricate, our method finds a near-optimal solution (maximum deviation is 7% from optimal solution) with a speedup that reaches 95%. We study the system performance by varying different system parameters.

关键词： Solid modeling Servers Sensors Task analysis Schedules Edge computing Computational modeling Autonomous vehicles edge computing task analysis edge computing cloud computing delays servers processor scheduling heuristic algorithms computational complexity integer linear programming dynamic programming logic-based benders decomposition vehicular network

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Energy-efficient high-speed train rescheduling during a major disruption

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年第0期157卷 102492-102492页

作者： Zhan, Shuguang Wang, Pengling Wong, S. C. Lo, S. M. Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Sichuan Peoples R China Tongji Univ Coll Transportat Engn Shanghai Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China Guangdong Hong Kong Macau Joint Lab Smart Cities Hong Kong Guangdong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China

Disruptions are inevitable in daily train operations, and can cause high-speed trains to deviate from their official schedules. Therefore, the efficient rescheduling of disrupted trains is critical for ensuring smooth daily operations. We sought to determine the appropriate arrival and departure times and orders of trains at each station during a disruption, and the speed profile of each train, to reduce the delay costs and energy consumption. To embed the train speed profile corresponding to energy saving into the train rescheduling problem, a space-time-speed network is applied for problem formulation. Thus, the energy-efficient train speed profile is embedded in the speed dimension in the space-time-speed hypernetwork. The detailed train speed profile between two stations is formulated as a multiple-phase optimal control model, which is solved using a pseudospectral method. Then, an integer linear programming model based on multicommodity flow is built to solve the train rescheduling problem. We decompose it via the alternating direction method of multipliers into a series of easy-to-solve shortest path subproblems. Each subproblem is then efficiently solved using a dynamic programming algorithm. Finally, the Xi'an-Chengdu high-speed railway line is used to test our model and algorithms, thereby demonstrating the trade-off between passenger-service quality and energy efficiency

关键词： High-speed railway Energy saving Train rescheduling integer linear programming

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Global Network Coordination in Densified Wireless Access Networks through integer linear programming

Global Network Coordination in Densified Wireless Access Net...

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IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Gotsis, Antonis G. Alexiou, Angeliki Univ Piraeus Dept Digital Syst Piraeus Greece

ISBN: (纸本)9781467362351

Network densification with universal resources reuse is regarded as a possible solution to increase capacity and deal with the "1000x" mobile data challenge in future 3GPP radio access networks. Interference is the major performance limitation is such networks, and global coordination of coexisting transmissions comprises the technology enabler for achieving high performance levels. In this work we consider large distributed networks of low-cost infrastructure nodes, such as small-cells or simply access points. We explore optimal and efficient strategies for selecting the serving access point for each user (access point/user pairing) and tuning the transmission power per communication link, in order to achieve uniform quality-of-service levels across the whole network. For this purpose, we reformulate the original intractable joint pairing and power optimization problem, leveraging the powerful integer linear programming optimization framework. We also propose lower-complexity coordination algorithms by decoupling the pairing and power allocation sub-problems. We evaluate the proposed algorithms performance in large dense access networks and we demonstrate how to guarantee specific rates over increased user population by infrastructure densification and network resources coordination.

关键词： Radio Access Network Densification Network Coordination Pairing Power Allocation Uniform QoS integer linear programming

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