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A novel two-stage optimization scheme for solving university class scheduling problem using binary integer linear programming

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OPERATIONS MANAGEMENT RESEARCH 2019年第3-4期12卷 173-181页

作者： Samiuddin, Jilan Haq, Mohammad Aminul Uttara Univ House 4 & 6Rd 15Sect 6 Dhaka 1230 Bangladesh

University Class Scheduling Problem (UCSP) is an inevitable task every university must go through prior to the commencement of a semester. The problem studied in this paper is decomposed into two stages - lab scheduling and theoretical class scheduling. This novel technique of decomposition of the problem not only allowed maximum number of free variables to stricter constraints found in lab scheduling, but also reduced the overall computational cost and combinatorial complexity. The mathematical model for the problem is formulated via binary integer linear programming (BILP) structure using data collected from the department considered in the study. Part of the contribution of this work also includes developing ways to recognize only the true variables while formulating the model. The model is then optimized using the simplex method with the objectives to optimize classroom utilization and faculty preferences while fulfilling additional constraints. Furthermore, the proposed method is compared to the traditional technique in which the model is optimized in a single stage with both true variables recognized and not recognized.

关键词： University class scheduling problem binary integer linear programming Timetabling Simplex method Resource allocation

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Single-stage train formation in railway marshaling stations under an extended railcar-to-track assignment policy

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 171卷

作者： Zhang, Siyu Zhao, Jun Zhang, Bojian Zhao, Jiaxi 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 DB ECO North Amer Inc Los Angeles CA 91775 USA

This paper studies a single-stage train formation problem in railway marshaling stations, aiming to efficiently assign railcars to classification tracks with one roll-in and one pull-out operation for ensuring the formation of outbound trains. Assigning railcars to classification tracks by blocks (block-to-track), by outbound trains (train-to-track), and by need (railcar-to-track) are three typical policies widely addressed in the literature. An extended railcar-to-track policy is investigated by combining the first and third policies, where railcars are preferentially assigned to their fixed-use classification tracks through a specified block-to-track scheme and then to other classification tracks if necessary, while re-humping and re-sorting operations are eliminated. We formulate the formation problem under this policy as a binary linear programming model with the objective of minimizing the total weighted cost required for train formation, including both the weighted roll-in cost and the weighted pull-out cost. A two-phase decomposition algorithm, which divides our model into a roll-in and a pull-out subproblem, is developed to improve the solving efficiency. For the roll-in subproblem, a novel group-indexed model is constructed to determine a railcar-to-track scheme with minimal total weighted roll-in cost and simplified pull-out cost. For the following pull-out subproblem, the objective is to determine a pull-out scheme that minimizes the total weighted pull-out cost. This subproblem is decomposed further into multiple simplified problems, each of which is formulated into a quadratic assignment model for each outbound train, enabling rapid solving times of this subproblem. Computational results on a set of realistic instances reveal that our algorithm outperforms two benchmark approaches, in which the roll-in subproblem is formulated respectively as a big-M and an arc-indexed model inspired by existing models, and an imitated empirical approach used in practic

关键词： Railway transportation Marshaling station Single-stage train formation Extended railcar-to-track policy binary integer linear programming Decomposition algorithm

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Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis

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ENERGY 2018年 163卷 15-26页

作者： Yahia, Z. Pradhan, A. Univ Johannesburg Dept Qual & Operat Management Johannesburg South Africa Fayoum Univ Dept Mech Engn Al Fayyum Egypt

This paper discusses an experimental study of the home appliances scheduling problem that incorporates realistic aspects. The residential load scheduling problem is solved while considering consumer's preferences. The objective function minimizes the weighted sum of electricity cost by earning relevant incentives, and the scheduling inconvenience. The objective of this study is five-fold. First, it sought to develop and solve a binary integer linear programming optimization model for the problem. Second, it examined the factors that might affect the obtained schedule of residential loads. Third, it aimed to test the performance of a developed optimization model under different experimental scenarios. Fourth, it proposes a conceptual definition of a new parameter in the problem, the so-called "flexibility ratio". Finally, it adds a data set for use in the literature on the home appliance scheduling problem, which can be used to test the performance of newly-developed approaches to the solution of this problem. This paper presents the results of experimental analysis using four factors: problem size, flexibility ratio, time slot length and the objective function weighting factor. The experimental results show the main and interaction effects, where these exist, on three performance measures: the electricity cost, inconvenience and the optimization model computation time. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Household appliances Residential load scheduling Inconvenience Consumer preferences and flexibility binary integer linear programming Experimental analysis

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Control and Data Channel Resource Allocation in OFDMA Heterogeneous Networks

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2016年第2期85卷 183-199页

作者： Saeed, Arsalan Katranaras, Efstathios Dianati, Mehrdad Imran, Muhammad Ali Univ Surrey ICS Guildford Surrey England

This paper investigates the downlink resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA) Heterogeneous Networks (HetNets) consisting of macro cells and small cells sharing the same frequency band. Dense deployment of small cells overlaid by a macro layer is considered to be one of the most promising solutions for providing hotspot coverage in future 5G networks. The focus is to devise an optimised policy for small cells' access to the shared spectrum, in terms of their transmissions, in order to keep small cell served users sum data rate at high levels while ensuring that certain level of quality of service (QoS) for the macro cell users in the vicinity of small cells is provided. Both data and control channel constraints are considered, to ensure that not only the macro cell users' data rate demands are met, but also a certain level of Bit Error Rate (BER) is ensured for the control channel information. Control channel reliability is especially important as it holds key information to successfully decode the data channel. The problem is addressed by our proposed linear binary integer programming heuristic algorithm which maximises the small cells utility while ensuring the macro users imposed constraints. To further reduce the computational complexity, we propose a progressive interference aware low complexity heuristic solution. Discussion is also presented for the implementation possibility of our proposed algorithms in a practical network. The performance of both the proposed algorithms is compared with the conventional Reuse-1 scheme under different fading conditions and small cell loads. Results show a negligible drop in small cell performance for our proposed schemes, as a trade-off for ensuring all macro users data rate demands, while Reuse-1 scheme can even lead up to 40 % outage when control region of the small cells in heavily loaded.

关键词： 5G Heterogeneous networks Small cells Inter-cell interference Resource allocation binary integer linear programming

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A mathematical programming model for computing the Fries number of a fullerene

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APPLIED MATHEMATICAL MODELLING 2015年第18期39卷 5473-5479页

作者： Salami, M. Ahmadi, M. B. Shiraz Univ Dept Math Coll Sci Shiraz Iran

A fullerene graph is a cubic 3-connected plane graph with pentagonal and hexagonal faces. The Fries number of a fullerene is the maximum number of benzene-like faces over all possible perfect matchings. The Fries number and its associated Kekule structure of a fullerene play a key role in molecular energy and stability. In this paper we propose a binary integer linear programming and a quadratic programming model for determining the Fries number of a fullerene. Moreover, interior point approach, as one of the most robust optimization techniques, is implemented to find the optimal solution of the proposed quadratic programming problem in moderate computing time. (C) 2015 Elsevier Inc. All rights reserved.

关键词： binary integer linear programming Quadratic programming problem Perfect matching Fries number Fullerenes

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A heuristic for BILP problems: The Single Source Capacitated Facility Location Problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第2期238卷 438-450页

作者： Guastaroba, G. Speranza, M. G. Univ Brescia Dept Econ & Management Brescia Italy

In the Single Source Capacitated Facility Location Problem (SSCFLP) each customer has to be assigned to one facility that supplies its whole demand. The total demand of customers assigned to each facility cannot exceed its capacity. An opening cost is associated with each facility, and is paid if at least one customer is assigned to it. The objective is to minimize the total cost of opening the facilities and supply all the customers. In this paper we extend the Kernel Search heuristic framework to general binary integer linear programming (BILP) problems, and apply it to the SSCFLP. The heuristic is based on the solution to optimality of a sequence of subproblems, where each subproblem is restricted to a subset of the decision variables. The subsets of decision variables are constructed starting from the optimal values of the linear relaxation. Variants based on variable fixing are proposed to improve the efficiency of the Kernel Search framework. The algorithms are tested on benchmark instances and new very large-scale test problems. Computational results demonstrate the effectiveness of the approach. The Kernel Search algorithm outperforms the best heuristics for the SSCFLP available in the literature. It found the optimal solution for 165 out of the 170 instances with a proven optimum. The error achieved in the remaining instances is negligible. Moreover, it achieved, on 100 new very large-scale instances, an average gap equal to 0.64% computed with respect to a lower bound or the optimum, when available. The variants based on variable fixing improved the efficiency of the algorithm with minor deteriorations of the solution quality. (c) 2014 Elsevier B.V. All rights reserved.

关键词： Single Source Capacitated Facility Location Problems binary integer linear programming Heuristic algorithms Kernel Search framework

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Design and evaluation of a model-driven decision support system for repurposing electric vehicle batteries

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EUROPEAN JOURNAL OF INFORMATION SYSTEMS 2018年第2期27卷 171-188页

作者： Klooer, Benjamin Monhof, Markus Beverungen, Daniel Braaeer, Sebastian Univ Munster ERCIS Munster Germany Univ Paderborn Paderborn Germany

The diffusion of electric vehicles suffers from immature and expensive battery technologies. Repurposing electric vehicle batteries for second-life application scenarios may lower the vehicles' total costs of ownership and increases their ecologic sustainability. However, identifying the best - or even a feasible - scenario for which to repurpose a battery is a complex and unresolved decision problem. In this exaptation research, we set out to design, implement, and evaluate the first decision support system that aids decision-makers in the automobile industry with repurposing electric vehicle batteries. The exaptation is done by classifying decisions on repurposing products as bipartite matching problems and designing two binary integer linear programs that identify (a) all technical feasible assignments and (b) optimal assignments of products and scenarios. Based on an empirical study and expert interviews, we parameterize both binary integer linear programs for repurposing electric vehicle batteries. In a field experiment, we show that our decision support system considerably increases the decision quality in terms of hit rate, miss rate, precision, fallout, and accuracy. While practitioners can use the implemented decision support system when repurposing electric vehicle batteries, other researchers can build on our results to design decision support systems for repurposing further products.

关键词： Model-driven decision support system design science research binary integer linear programming bipartite matching problem electric vehicle battery

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Leveraging Intelligence from Network CDR Data for Interference Aware Energy Consumption Minimization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2018年第7期17卷 1568-1581页

作者： Zoha, Ahmed Saeed, Arsalan Farooq, Hasan Rizwan, Ali Imran, Ali Imran, Muhammad Ali Univ Surrey 5GIC Guildford GU2 7XH Surrey England Semafone Ltd Pannell House Guildford GU1 4HN Surrey England Univ Oklahoma Tulsa OK 74135 USA Univ Oklahoma Telecommun Tulsa OK 74135 USA Univ Glasgow Sch Engn Commun Syst Glasgow G12 8QQ Lanark Scotland Univ Glasgow Sch Engn Glasgow Coll UESTC Glasgow G12 8QQ Lanark Scotland Univ Glasgow Commun Syst Glasgow G12 8QQ Lanark Scotland

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this, we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, the proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS.

关键词： 5G heterogeneous networks small cells energy efficiency inter-cell interference resource allocation binary integer linear programming CDRs big data analytics

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Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semi-definite programming

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第15期9卷 2427-2436页

作者： Korres, George N. Manousakis, Nikolaos M. Xygkis, Themistoklis C. Lofberg, Johan Natl Tech Univ Athens Sch Elect & Comp Engn GR-15780 Athens Greece Linkoping Univ Dept Elect Engn SE-58183 Linkoping Sweden

This study presents a new approach for optimal placement of synchronised phasor measurement units (PMUs) to ensure complete power system observability in the presence of non-synchronous conventional measurements and zero injections. Currently, financial or technical restrictions prohibit the deployment of PMUs on every bus, which in turn motivates their strategic placement across the power system. PMU allocation is optimised here based on measurement observability criteria for achieving solvability of the power system state estimation. Most of the previous work has proposed topological observability based methods for optimal PMU placement (OPP), which may not always ensure numerical observability required for successful execution of state estimation. The proposed OPP method finds out the minimum number and the optimal locations of PMUs required to make the power system numerically observable. The problem is formulated as a binary semi-definite programming (BSDP) model, with binary decision variables, minimising a linear objective function subject to linear matrix inequality observability constraints. The BSDP problem is solved using an outer approximation scheme based on binary integer linear programming. The developed method is conducted on IEEE standard test systems. A large-scale system with 3120 buses is also analysed to exhibit the applicability of proposed model to practical power system cases.

关键词： phasor measurement numerical analysis state estimation linear matrix inequalities integer programming linear programming synchronised phasor measurement units power system observability power system state estimation optimal PMU placement OPP method binary semidefinite programming model BSDP model linear matrix inequality binary integer linear programming

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Weak bus-constrained PMU placement for complete observability of a connected power network considering voltage stability indices

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Protection and Control of Modern Power Systems 2020年第1期5卷 314-327页

作者： Rohit Babu Saurav Raj Biplab Bhattacharyya Department of Electrical and Electronics Engineering Lendi Institute of Engineering and TechnologyJonnadaAndhra Pradesh 535003India Department of Electrical Engineering Institute of Chemical Technology MumbaiMarathwada CampusJalna 431203India Department of Electrical Engineering Indian Institute of Technology(Indian School of Mines)DhanbadJharkhand 826004India

Phasor measurement units(PMUs)are preferred for installation at weak buses in a power ***,the weak buses need to be located and the strategic locations of PMUs identified to ensure network ***,the primary aim of this work is to identify the placements of the maximum number of PMUs installed at the weak buses in the electrical *** voltage collapse proximity indicator,line stability index,fast voltage stability index,and a new voltage stability indicator utilizing load flow measurement are used to determine the weak buses.A novel deterministic methodology based on a binary-integer linear programming model is then proposed to determine the optimal locations of *** effect of a single PMU outage considering the weak buses is also *** effectiveness of the developed approach is tested and validated on the standard IEEE 14-,118-,300-,and New England 39-bus *** obtained results are also compared to those using different weak bus methodologies.

关键词： binary integer linear programming Optimal PMU placement Observability Phasor measurement unit Voltage stability indices

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