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Coupled binary linear programming-Differential Evolution Algorithm Approach for Water Distribution System Optimization

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2014年第5期140卷 585-597页

作者： Zheng, Feifei Simpson, Angus R. Zecchin, Aaron C. Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

A coupled binary linear programming-differential evolution (BLP-DE) approach is proposed in this paper to optimize the design of water distribution systems (WDS). Three stages are involved in the proposed BLP-DE optimization method. In the first stage, the WDS that is being optimized is decomposed into trees and the core using a graph algorithm. binary linear programming is then used to optimize the design of the trees during the second stage. In the third stage, a differential evolution (DE) algorithm is utilized to deal with the core design while incorporating the optimal solutions for the trees obtained in the second stage, thereby yielding near-optimal solutions for the original whole WDS. The proposed method takes advantage of both the BLP and DE algorithms: BLP is capable of providing a global optimal solution for the trees (no loops involved) with great efficiency, and a DE is able to efficiently generate good quality solutions for the core (loops involved) with a reduced search space compared to the original full network. Two benchmark WDS case studies and one real-world case study (with multiple demand loading cases) with a number of decision variables ranging from 21-96 are used to verify the effectiveness of the proposed BLP-DE optimization approach. Results show that the proposed BLP-DE algorithm significantly outperforms other optimization algorithms in terms of both solution quality and efficiency.

关键词： Differential evolution Optimization Water distribution systems binary linear programming

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Solving the binary linear programming Model in Polynomial Time

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American Journal of Operations Research 2016年第1期6卷 1-7页

作者： Elias Munapo School of Accounting Economics and Decision Sciences North West University Mafeking South Africa

The paper presents a technique for solving the binary linear programming model in polynomial time. The general binary linear programming problem is transformed into a convex quadratic programming problem. The convex quadratic programming problem is then solved by interior point algorithms. This settles one of the open problems of whether P = NP or not. The worst case complexity of interior point algorithms for the convex quadratic problem is polynomial. It can also be shown that every liner integer problem can be converted into binary linear problem.

关键词： NP-Complete binary linear programming Convex Function Convex Quadratic programming Problem Interior Point Algorithm and Polynomial Time

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Optimal scheduling of shared autonomous electric vehicles with multi-agent reinforcement learning: A MAPPO-based approach

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NEUROCOMPUTING 2025年 622卷

作者： Tian, Jingjing Jia, Hongfei Wang, Guanfeng Huang, Qiuyang Wu, Ruiyi Gao, Heyao Liu, Chao Jilin Univ Coll Transportat Changchun 130022 Peoples R China

The advent of shared autonomous electric vehicles (SAEVs) is expected to decarbonize the transport sector and enable large-scale mobility on demand. Considering the highly dynamic nature of operation environments, it is appealing to investigate the optimal schedule decisions, comprising order-matching, relocation, and charge/ discharge with the V2G technology. Recent literature has investigated these schedule decisions with deep reinforcement learning (DRL) independently, neglecting the collaborative interactions among multiple autonomous vehicles. To this end, the fully dynamic schedule task is integrally described as a partially observable Markov decision process (POMDP) under the constraints of time-dependent demand, spatial-temporal varying electricity price, available vehicles and chargers. Then, we design a robust and extensible framework that combines multi-agent proximal policy optimization (MAPPO) and a binary linear programming (BLP) model to solve the optimization problem, in which SAEVs are regarded as agents to make decisions according to their local observations. Additionally, observation normalization and action masking are utilized to improve training efficiency. Numerical experiments are conducted with New York City yellow taxi data to simulate the real-time SAEV operating environment. These results demonstrate that the proposed MAPPO approach is expected to enhance the operators' revenue while improving the customer service rate compared to benchmark algorithms.

关键词： Shared autonomous electric vehicles Optimal scheduling Multi-agent proximal policy optimization binary linear programming

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Gradient-Informed Pareto-Based Multi-Objective binary Topology Optimization

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IEEE Access 2025年 13卷 86576-86587页

作者： Lucchini, Francesco Torchio, Riccardo Alotto, Piergiorgio Università degli Studi di Padova Department of Industrial Engineering Via Gradenigo 6/A Padova 35131 Italy Università degli Studi di Padova Department of Information Engineering Via Gradenigo 6/B Padova 35131 Italy

Multi-objective topology optimization (TO) problems frequently arise in practical engineering applications, necessitating the identification of Pareto-optimal solutions. This article introduces a gradientin-formed Pareto-based multi-objective TO algorithm with binary decision variables, called GPBTO, tailored to constrained bi-objective optimization problems (CBOPs), a common scenario in engineering design. By leveraging a binary decision space and incorporating a linearization step for objectives and constraints, the method enables the use of efficient integer linear programming (ILP) techniques for evolving the decision vector. Unlike traditional weighted sum (WS) approaches, which are widely used in TO despite their known limitations, GPBTO provides an alternative that integrates gradient-based formulations while facilitating the identification of Pareto-optimal solutions. While WS remains a dominant method in TO, GPBTO represents a promising alternative for cases where Pareto-based solutions are desirable. To the best of the authors’ knowledge, this is the first attempt to integrate Pareto-based TO with binary variables and gradient information, highlighting its potential for further exploration and application. © 2013 IEEE.

关键词： binary linear programming gradient-search large-scale multi-objective optimization Pareto-optimal solutions Topology optimization (TO)

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A quantum constraint generation framework for binary linear programs

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EPJ Quantum Technology 2025年第1期12卷 1-19页

作者： Czégel, András G.-Tóth, Boglárka Department of Computational Optimization University of Szeged Árpád tér 2. Szeged 6720 Hungary

We propose a new approach to utilize quantum computers for binary linear programming (BLP), which can be extended to general integer linear programs (ILP). Quantum optimization algorithms, hybrid or quantum-only, are currently general purpose, standalone solvers for ILP. However, to consider them practically useful, we expect them to overperform the current state of the art classical solvers. That expectation is unfair to quantum algorithms: in classical ILP solvers, after many decades of evolution, many different algorithms work together as a robust machine to get the best result. This is the approach we would like to follow now with our quantum ‘solver’ solutions. In this study we wrap any suitable quantum optimization algorithm into a quantum informed classical constraint generation framework. First we relax our problem by dropping all constraints and encode it into an Ising Hamiltonian for the quantum optimization subroutine. Then, by sampling from the solution state of the subroutine, we obtain information about constraint violations in the initial problem, from which we decide which coupling terms we need to introduce to the Hamiltonian. The coupling terms correspond to the constraints of the initial binary linear program. Then we optimize over the new Hamiltonian again, until we reach a feasible solution, or other stopping conditions hold. Since one can decide how many constraints they add to the Hamiltonian in a single step, our algorithm is at least as efficient as the (hybrid) quantum optimization algorithm it wraps. We support our claim with results on small scale minimum cost exact cover problem instances. © The Author(s) 2025.

关键词： binary linear programming Constraint generation Quantum optimization

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Deriving a minimum distance-based collective preorder: a binary mathematical programming approach

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OR SPECTRUM 2012年第1期34卷 23-42页

作者： Jabeur, Khaled Martel, Jean-Marc Guitouni, Adel ISCCB Zarzouna 7021 Bizerte Tunisia Univ Laval Fac Sci Adm Ste Foy PQ G1K 7P4 Canada Def R&D Canada Valcartier Val Belair PQ G3J 1X5 Canada

Deriving the "closest" (minimal distance) collective judgment to all individual opinions is a complex aggregation problem that has been widely studied in group decision-making literature. However, most of the existing literature does not consider individual opinions expressed as partial preorders (i.e., a preference system which includes the incomparability relation). In this paper, we propose a method based on binary linear programming to derive a minimum distance-based collective preorder from individual preferences relational systems (p.r.s.). This method is threefold. First, each member determines a preorder (partial or total) over the set of alternatives. Second, an aggregation algorithm is proposed to derive at least one collective and not necessary transitive p.r.s. at minimum distance from all individual preorders. Third, a binary linear programming optimization will transform each non-transitive collective p.r.s. into a collective preorder (i.e. a transitive p.r.s.). The proposed method has three main advantages: (1) it deals with incomparability (partial preorders), (2) the relative importance of the members is explicitly considered and (3) the collective p.r.s. obtained after the aggregation step might be "exploited" according to different decision-making problematics (i.e. ranking, choice and sorting).

关键词： Group decision-making binary relations Preferences aggregation Distance measure binary linear programming Members' relative importance

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Precise Worst-Case Blocking Time of Tasks Under Priority Inheritance Protocol

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IEEE TRANSACTIONS ON COMPUTERS 2021年第11期70卷 1901-1913页

作者： Faldella, Eugenio Loreti, Daniela Univ Bologna Dept Comp Sci & Engn I-40126 Bologna Italy

The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed for more sophisticated protocols, the problem is indeed complex in case of the Priority Inheritance Protocol, even restricting the attention to uniprocessor systems, non-nested resource accesses, and non-self-suspending tasks. Besides a very simple method leading in general to loose upper bounds, only one algorithm of exponential complexity has been so far reported in literature to tighten such bounds. In this article, we describe a novel approach which, leveraging an operational research technique for modeling the problem, computes the same tight bounds in polynomial time. We then discuss the scenarios in which, assuming no conditional statements in the tasks' code, the computed bounds derive from an actually impossible blocking chain, and we refine the initial model to more precisely compute the worst-case blocking times for any task set in any possible operating condition.

关键词： Task analysis Protocols Real-time systems Upper bound Computational modeling Complexity theory linear programming Hard real-time multitasking applications exclusive access resources basic priority inheritance protocol binary linear programming

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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On the energy cost of robustness for green virtual network function placement in 5G virtualized infrastructures

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COMPUTER NETWORKS 2017年 125卷 64-75页

作者： Marotta, Antonio D'Andreagiovanni, Fabio Kassler, Andreas Zola, Enrica Karlstad Univ Univ Gatan 2 S-65188 Karlstad Sweden Sorbonne Univ Univ Technol Compiegne CNRS Heudiasyc UMR 7253 CS 60319 F-60203 Compiegne France Univ Politecn Cataluna C Jordi Girona 1-3 ES-08034 Barcelona Spain

Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. The model explicitly provides for robustness to unknown or imprecisely formulated resource demand variations, powers down unused routers, switch ports and servers, and calculates the energy optimal VNF placement and network embedding also considering latency constraints on the service chains. We propose both exact and heuristic methods. Our experiments were carried out using the virtualized Evolved Packet Core (vEPC), which allows us to quantitatively assess the trade-off between energy cost, robustness and the protection level of the solutions against demand uncertainty. Our heuristic is able to converge to a good solution in a very short time, in comparison to the exact solver, which is not able to output better results in a longer run as demonstrated by our numerical evaluation. We also study the degree of robustness of a solution for a given protection level and the cost of additional energy needed because of the usage of more computing and network elements. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Virtualization binary linear programming Robust optimization Network function virtualization (NW) EPC 5G

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Design of Resilient Sensor Networks Balancing Resilience and Efficiency

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International Journal of Disaster Risk Science 2024年第1期15卷 107-115页

作者： Sergey N.Vecherin Kiril D.Ratmanski Luke Hogewood Igor Linkov Cold Regions Research and Engineering Laboratory Engineering Research and Development CenterHanoverNH 03755USA Environmental Laboratory Engineering Research and Development CenterVicksburgMS 39180USA

In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk *** a disaster situation,accurate sensing information is the key to efficient recovery *** general,resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality,but resilient design may require redundancy and could increase *** this article,we describe a method based on binary linear programming for sensor network design balancing efficiency with *** application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both twoand three-dimensional *** method provides optimal sensor placement,taking into account critical functionality and a desired level of resilience and considering sensor type and *** problem formulation,resilience requirements,and application of the optimization algorithm are described in *** of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent *** tasks are successfully solved by the described method,which can be applied to strengthen the resilience of sensor networks by *** proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

关键词： binary linear programming Optimal sensor placement Redundant networks Resilience and efciency Resilient sensor networks

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