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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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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Graph edit distance contest: Results and future challenges

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PATTERN RECOGNITION LETTERS 2017年 100卷 96-103页

作者： Abu-Aisheh, Zeina Gauzere, Benoit Bougleux, Sebastien Ramel, Jean-Yves Brun, Luc Raveaux, Romain Heroux, Pierre Adam, Sebastien Univ Francois Rabelais Tours 64 Ave Jean Portalis F-37200 Tours France Normandie Univ UNIROUEN UNIHAVRE INSA RouenLITIS F-76000 Rouen France Normandie Univ UNICAEN CNRS ENSICAENGREYC UMR 6072 6 Bd Marechal Juin F-14050 Caen France

Graph Distance Contest (GDC) was organized in the context of ICPR 2016. Its main challenge was to inspect and report performances and effectiveness of exact and approximate graph edit distance methods by comparison with a ground truth. This paper presents the context of this competition, the metrics and datasets used for evaluation, and the results obtained by the eight submitted methods. Results are analyzed and discussed in terms of computation time and accuracy. We also highlight the future challenges in graph edit distance regarding both future methods and evaluation metrics. The contest was supported by the Technical Committee on Graph-Based Representations in Pattern Recognition (TC-15) of the International Association of Pattern Recognition (IAPR). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Graph edit distance Pattern Recognition binary linear programming Quadratic assignment Branch-and-bound

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Declarative and Mathematical programming approaches to Decision Support Systems for food recycling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 95卷 103861-103861页

作者： Chesani, Federico Cota, Giuseppe Gavanelli, Marco Lamma, Evelina Mello, Paola Riguzzi, Fabrizio Univ Bologna Dipartimento Informat Sci & Ingn Viale Risorgimento 2 I-40136 Bologna Italy Univ Parma Dipartimento Sci Matemat Fis & Informat Parco Area Sci 53-A I-43124 Parma Italy Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy

Every year about one third of the food production intended for humans gets lost or wasted. This wastefulness of resources leads to the emission of unnecessary greenhouse gas, contributing to global warming and climate change. The solution proposed by the SORT project is to "recycle'' the surplus of food by reconditioning it into animal feed or fuel for biogas/biomass power plants. In order to maximize the earnings and minimize the costs, several choices must be made during the reconditioning process. Given the extremely complex nature of the process, Decision Support Systems (DSSs) could be helpful to reduce the human effort in decision making. In this paper, we present a DSS for food recycling developed using two approaches for finding the optimal solution: one based on binary linear programming (BLP) and the other based on Answer Set programming (ASP), which outperform our previous approach based on Constraint Logic programming (CLP) on Finite Domains (CLP(FD)). In particular, the BLP and the CLP(FD) approaches are developed in (ECLPSe)-P-i, a Prolog system that interfaces with various state-of-the-art Mathematical and Constraint programming solvers. The ASP approach, instead, is developed in clingo. The three approaches are compared on several synthetic datasets that simulate the operative conditions of the DSS.

关键词： Food recycling Decision Support System Constraint Logic programming Answer Set programming binary linear programming

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A new model for the integrated vehicle-crew-rostering problem and a computational study on rosters

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JOURNAL OF SCHEDULING 2011年第4期14卷 319-334页

作者： Mesquita, Marta Moz, Margarida Paias, Ana Paixao, Jose Pato, Margarida Respicio, Ana Univ Tecn Lisboa Dept Matemat Inst Super Agron P-1349017 Lisbon Portugal Univ Tecn Lisboa Dept Matemat Inst Super Econ & Gestao P-1200781 Lisbon Portugal Univ Lisbon Fac Ciencias DEIO P-1749016 Lisbon Portugal Univ Lisbon Fac Ciencias DI P-1749016 Lisbon Portugal Ctr Invest Operac FCUL Lisbon Portugal

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle-scheduling, crew-scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to obtain feasible solutions for this binary non-linear multi-objective optimization problem is a sequential algorithm considered within a preemptive goal programming framework that gives a higher priority to the integrated vehicle-crew-scheduling goal and a lower priority to the driver rostering goals. A heuristic approach is developed where the decision maker can choose from different vehicle-crew schedules and rosters, while respecting as much as possible management's interests and drivers' preferences. An application to real data of a Portuguese bus company shows the influence of vehicle-crew-scheduling optimization on rostering solutions.

关键词： binary linear programming Vehicle scheduling Crew scheduling Driver rostering Multi-objective problems

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linear programming Formulations for Computing Graph Layout Parameters

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COMPUTER JOURNAL 2015年第11期58卷 2921-2927页

作者： Gurski, Frank Univ Dusseldorf Inst Comp Sci Algorithm Hard Problems Grp D-40225 Dusseldorf Germany

Luttamguzi et al. [(2005) Integer programming Methods for Several Optimization Problems in Graph Theory. Proc. Int. Conf. Computers and Their Applications, CATA 2005, New Orleans, LA, USA, March 16-18, pp. 50-55. ISCA] have introduced a useful framework for the formulation of graph layout parameters measuring number of edges, which is applied to give linear programming formulations for computing the cut-width, minimum linear arrangement and band-width of a graph. We extend the framework of Luttamguzi et al. by the efficient formulation of sets of vertices of the same neighborhood-so-called groups-using binary linear programming (BIP) conditions. We apply our methods in order to find first BIP formulations for computing the important graph layout para-me-ters path-width, neighborhood-width, linear NLC-width, and linear clique-width. Our formulations give useful and new characterizations of the problems as well as easy-to-understand algorithms for their solution. The size of our formulations is polynomial in the size of the input graph.

关键词： graph algorithms binary linear programming graph parameters graph layout problems

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Equilibrium of Redundancy in Relational Model for Optimized Data Retrieval

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第9期32卷 1707-1721页

作者： Kojic, Nemanja M. Milicev, Dragan S. Univ Belgrade Dept Comp Sci & Engn Sch Elect Engn Belgrade Serbia

Conceptual and relational data models of online transaction processing (OLTP) applications are usually created and maintained following the principle of normalization, which implies avoidance of redundancy. Data retrieval from a disk-based normalized relational database often requires complex and inefficient queries that may cause noticeable performance issues when executed on larger volumes of data. Computer professionals sometimes intentionally trade off the strict normal form to optimize data retrieval queries through error-prone manual tuning and denormalization. We propose a fully automatic optimization approach, based on data redundancy, that relies on a formal cost-benefit model. We prove that finding the optimal level of data redundancy, for given workload statistics, is an NP-Complete optimization problem. A detailed reduction of the problem to binary linear programming is presented in the paper. The proposed optimization approach was evaluated using the TPCE benchmark for OLTP systems. The evaluation has shown that the proposed optimization approach is highly scalable, and that it can be efficiently applied to real-life relational data models.

关键词： Optimization Data models Redundancy Unified modeling language Distributed databases Indexes Relational databases optimization denormalization data redundancy binary linear programming

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Staff assignment with lexicographically ordered acceptance levels

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JOURNAL OF SCHEDULING 2018年第2期21卷 167-189页

作者： Rihm, Tom Baumann, Philipp Univ Bern Dept Business Adm Schutzenmattstr 14 CH-3012 Bern Switzerland

Staff assignment is a compelling exercise that affects most companies and organizations in the service industries. Here, we introduce a new real-world staff assignment problem that was reported to us by a Swiss provider of commercial employee scheduling software. The problem consists of assigning employees to work shifts subject to a large variety of critical and noncritical requests, including employees' personal preferences. Each request has a target value, and deviations from the target value are associated with integer acceptance levels. These acceptance levels reflect the relative severity of possible deviations, e.g., for the request of an employee to have at least two weekends off, having one weekend off is preferable to having no weekend off and thus receives a higher acceptance level. The objective is to minimize the total number of deviations in lexicographical order of the acceptance levels. Staff assignment approaches from the literature are not applicable to this problem. We provide a binary linear programming formulation and propose a matheuristic for large-scale instances. The matheuristic employs effective strategies to determine the subproblems and focuses on finding good feasible solutions to the subproblems rather than proving their optimality. Our computational analysis based on real-world data shows that the matheuristic scales well and outperforms commercial employee scheduling software.

关键词： Employee scheduling Staff assignment Real-world problem Goal programming binary linear programming Matheuristic

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Resiliency of Distribution Systems Incorporating Asynchronous Information for System Restoration

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IEEE ACCESS 2019年 7卷 101471-101482页

作者： Bedyao, Juan C. Xie, Jing Wang, Yubo Zhang, Xi Liu, Chen-Ching Virginia Tech Bradley Dept Elect & Comp Engn Power & Energy Ctr Blacksburg VA 24061 USA Seattle Res Ctr Pacific Northwest Natl Lab Seattle WA 98109 USA Siemens Corp Technol Princeton NJ 08540 USA

Resiliency of distribution systems under extreme operating conditions is critical, especially when the utility is not available. With the large-scale deployment of distributed resources, it becomes possible to restore critical loads with local non-utility resources. Distribution system operators (DSOs) need to determine the critical loads to be restored, considering limited resources and distribution facilities. Several studies on resiliency have been conducted for the restoration of distribution systems. However, the inherent asynchronous characteristic of the information availability has not been incorporated. With incomplete and asynchronous information, decisions may be made that result in underutilization of generation resources. In this paper, a new distribution system restoration approach is proposed, considering uncertain devices and associated asynchronous information. It uses a two-module architecture that efficiently optimizes restoration actions using a binary linear programming model and evaluates their feasibility with unbalanced optimal power flow. Networked microgrids are included in the model. The IEEE 123-node test feeder is used for validation. The results show that asynchronous messages may affect the restoration actions significantly and the impacts can be mitigated by the proposed decision support tool for the DSOs.

关键词： binary linear programming decision making with asynchronous information distribution systems resiliency three-phase unbalanced optimal power flow

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The conservation benefits of cost-effective land acquisition: A case study in Maryland

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2006年第3期79卷 305-315页

作者： Messer, Kent Donald Cornell Univ Dept Appl Econ & Management Ithaca NY 14850 USA

Economic theory asserts that to achieve maximum conservation benefits land acquisition needs to be cost effective. Yet the most common planning technique used by land conservation organizations is 'benefit-targeting' that focuses only on acquiring parcels with the highest benefits and ignores costs. Unlike most of the literature which focuses on covering problems, this research applies optimization techniques to achieve maximum aggregate conservation benefits for an ongoing land acquisition effort in the Catoctin Mountain Region in central Maryland. For this case study, optimization yields additional conservation benefits worth an estimated $3.1-$3.9 million or achieves the same level of conservation benefits but at a cost savings ranging from $0.9 to $3.5 million, depending on the initial budget size. Finally, the highest efficiencies are achieved in low budget scenarios, like those most prevalent in conservation efforts. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： reserve selection binary linear programming benefit-targeting

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Joint rolling stock rotation planning and depot deadhead scheduling in complicated urban rail transit lines

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期314卷 665-684页

作者： Wang, Dian D'Ariano, Andrea Zhao, Jun Zhan, Shuguang Peng, Qiyuan Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Anhui Peoples R China Roma Tre Univ Dept Civil Comp Sci & Aeronaut Technol Engn I-00146 Rome Italy Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China

This study investigates the joint rolling stock rotation planning and depot deadhead scheduling in complicated urban rail transit lines with multiple depots, multiple line services, and multiple compositions. The rotation planning aims at connecting the given train trips into train sequences (each of which is served by an individual rolling stock) and determining the routing plan of rolling stocks to pass through visited turnback stations while connecting train trips. The task of the depot deadhead scheduling is to determine conflict-free deadhead routes and deadhead timetables of rolling stocks between the origin/destination of each train sequence and corresponding depot. We formulate the studied problem as a generalized set partitioning-type model containing an exponential number of variables, by using a new time-space network representation and by proposing a novel modelling method for the departure-arrival headway requirement to control the number of constraints. Owing to the complexity of this model, a column generation-based algorithm is adopted to solve efficiently practical-size problems. We enhance a standard column generation (to compute tight lower bound) by further incorporating procedures of variable rounding and halted column generation, to strengthen the capability of searching for better quality solutions. Customized acceleration mechanisms are also explored to speed up the convergence of column generation and the computation of best-known integer solution. We compare our approach with three benchmark approaches and the trial-and-error-based empirical method used by rail dispatchers in practice. Computational results reveal that our approach outperforms these benchmark approaches by computing (near-)optimal solutions. Our optimized solution for a large real-world instance (computed within a practically reasonable computation time) is better than the empirical solution, in terms of all the considered objective function parts.

关键词： Transportation Rolling stock rotation planning Depot deadhead scheduling binary linear programming Column generation

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