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No Cross-Validation Required: An Analytical Framework for Regularized mixed-integer Problems

IEEE COMMUNICATIONS LETTERS 2020年第12期24卷 2868-2872页

作者： Soleimani, Behrad Khamidehi, Behzad Sabbaghian, Maryam Univ Maryland Dept ECE College Pk MD 20742 USA Univ Toronto Dept ECE Toronto ON M5S 1A1 Canada Univ Tehran Dept ECE Tehran *** Iran

This letter develops a method to obtain the optimal value for the regularization coefficient in a general mixed-integer problem (MIP). This approach eliminates the cross-validation performed in the existing penalty techniques to obtain a proper value for the regularization coefficient. We obtain this goal by proposing an alternating method to solve MIPs. First, via regularization, we convert the MIP into a more mathematically tractable form. Then, we develop an iterative algorithm to update the solution along with the regularization (penalty) coefficient. We show that our update procedure guarantees the convergence of the algorithm. Moreover, assuming the objective function is continuously differentiable, we derive the convergence rate, a lower bound on the value of regularization coefficient, and an upper bound on the number of iterations required for the convergence. We use a radio access technology (RAT) selection problem in a heterogeneous network to benchmark the performance of our method. Simulation results demonstrate near-optimality of the solution and consistency of the convergence behavior with obtained theoretical bounds.

关键词： mixed-integer programming regularization alternating method penalty function RAT selection

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On the feasibility of using canals for waste collection in Amsterdam

Transactions in Urban Data, Science, and Technology

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Transactions in Urban Data, Science, and Technology 2022年第1-2期1卷 86 - 102页

作者： Snoweria Zhang Fábio Duarte Xuezhen Guo Lenna Johnsen Ruben van de Ketterij Carlo Ratti Senseable City Lab Massachusetts Institute of Technology Cambridge MA USA Operations Research and Logistics Group Wageningen University and Research Centre Netherlands

Amsterdam, a growing city of over 800,000 people in the Netherlands, is struggling to collect waste. While residents in most districts of the city use underground bins to deposit their garbage, the historic Centrum district continues to rely on curbside collection. As such, the streets around the UNESCO-World-Heritage canals are lined with garbage bags as trash trucks rumble down roads centuries old and ill-fitted for vehicles of such size. This paper assesses the viability of moving Centrum trash collection to the canals with a fleet of tug boats and floating dumpsters. It does so by using a combination of GIS tools and integer programming to determine the quantity and optimal collection locations while ensuring that an average Centrum resident walks no farther than denizens of the other Amsterdam districts. Additionally, it proposes a schedule for emptying floating dumpsters based on one comparable to the current truck system. The results of this paper suggest that mobile trash collection using the canals is a viable solution that could reduce noise, pollution, and congestion, thus improving the quality of Amsterdam’s historic cityscape.

关键词： Waste-collection boats autonomy GIS mixed-integer programming

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A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading

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APPLIED ENERGY 2020年第0期261卷 114322-000页

作者： Nizami, M. S. H. Hossain, M. J. Amin, B. M. Ruhul Fernandez, Edstan Macquarie Univ Sch Engn Sydney NSW 2109 Australia

Bi-directional electricity trading of demand response (DR) and transactive energy (TE) frameworks allows the traditionally passive end-users of electricity to play an active role in the local power balance of the grid. Appropriate building energy management systems (BEMSs), coupled with an optimized bidding strategy, can provide significant cost savings for prosumers (consumers with on-site power generation and/or storage facility) when they participate in such bi-directional trading. This paper presents a BEMS with an optimization-based scheduling and bidding strategy for small-scale residential prosumers to determine optimal day-ahead energy-quantity bids considering the expected cost of real-time imbalance trading under uncertainty. The proposed scheduling and bidding strategy is formulated as a stochastic bi-level minimization problem that determines the day-ahead energy-quantity bids by minimizing the energy cost in the upper level considering expected cost of uncertainty, whereas a number of lower-level sub-problems ensure optimal operation of building loads and distributed energy resources (DERs) for comfort reservation, minimization of consumers' inconveniences and degradation of residential storage units. A modified decomposition method is used to reformulate the nonlinear bi-level problem as a mixed-integer linear programming (MILP) problem and solved using 'of the shelf commercial software. The effectiveness of the proposed BEMS model is verified via case studies for a residential prosumer in Sydney, Australia with real measurement data for building energy demand. The efficacy of the proposed method for overall financial savings is also validated by comparing its performance with state-of-the-art day-ahead scheduling strategies. Case studies indicate that the proposed method can provide up to 51% and 22% cost savings compared to inflexible non-optimal scheduling strategies and deterministic optimization-based methods respectively. Results also indicate th

关键词： Demand response Building energy management system Distributed energy resources mixed-integer programming Bi-level optimization

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On the facet defining inequalities of the mixed-integer bilinear covering set

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2020年第3期92卷 545-575页

作者： Rahman, Hamidur Mahajan, Ashutosh Indian Inst Technol Ind Engn & Operat Res Mumbai 400076 Maharashtra India

We study the facet defining inequalities of the convex hull of a mixed-integer bilinear covering arising in trim-loss (or cutting stock) problem under the framework of disjunctive cuts. We show that all of them can be derived using a disjunctive procedure. Some of these are split cuts of rank one for a convex mixed-integer relaxation of the covering set, while others have rank at least two. For certain linear objective functions, the rank-one split cuts are shown to be sufficient for finding the optimal value over the convex hull of the covering set. A relaxation of the trim-loss problem has this property, and our computational results show that these rank-one inequalities find the lower bound quickly.

关键词： mixed-integer programming Global optimization Convex hull Disjunctive cut Split cut Split-rank

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A Decomposition Algorithm for the Two-Stage Chance-Constrained Operating Room Scheduling Problem

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IEEE ACCESS 2020年 8卷 80160-80172页

作者： Najjarbashi, Amirhossein Lim, Gino J. Univ Houston Dept Ind Engn Houston TX 77204 USA

The required time for surgical interventions in operating rooms (OR) may vary significantly from the predicted values depending on the type of operations being performed, the surgical team, and the patient. These deviations diminish the efficient utilization of OR resources and result in the disruption of projected surgery start times. This paper proposes a two-stage chance-constrained model to solve the OR scheduling problem under uncertainty. The goal is to minimize the costs associated with OR opening and overtime as well as reduce patient waiting times. The risk of OR overtime is controlled using chance constraints. Numerical experiments show that the proposed model provides a better trade-off between minimizing costs and reducing solution variability when compared to two existing models in the literature. It is also shown that the three models converge as the overtime probability threshold approaches one. Moreover, it is observed that the individual chance constraints result in the opening of fewer rooms, lower waiting times, and shorter solution times when compared to that of joint chance constraints. A decomposition algorithm is applied that solves large test instances of the OR scheduling problem, that of which is known to be NP-hard. Strong valid inequalities are derived in order to accelerate the convergence speed. The proposed approach outperformed both a commercial solver and a basic decomposition algorithm after solving all test instances with up to 89 surgeries and 20 ORs in less than 48 minutes.

关键词： Chance constraints mixed-integer programming operating room scheduling two-stage stochastic programming uncertainty

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Joint D2D Assignment, Bandwidth and Power Allocation in Cognitive UAV-Enabled Networks

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2020年第3期6卷 1084-1095页

作者： Nguyen, Huy T. Tuan, Hoang Duong Duong, Trung Q. Poor, H. Vincent Hwang, Won-Joo Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Pusan Natl Univ Sch Biomed Convergence Engn Busan 50612 South Korea

This paper considers a cognitive communication network, which consists of a flying base station deployed by an unmanned aerial vehicle (UAV) to serve its multiple downlink ground terminals (GTs), and multiple underlaid device-to-device (D2D) users. To support the GTs' throughput while guaranteeing the quality-of-service for the D2D users, the paper proposes the joint design of D2D assignment, bandwidth, and power allocation. This design task poses a computationally challenging mixed-binary optimization problem, for which a new computational method for its solution is developed. Multiple binary (discrete) constraints for the D2D assignment are equivalently expressed by continuous constraints to leverage systematic processes of continuous optimization. As a result, this problem of mixed-binary optimization is reformulated by an exactly penalized continuous optimization problem, for which an alternating descent algorithm is proposed. Each round of the algorithm invokes two simple convex optimization problems of low computational complexity. The theoretical convergence of the algorithm can be easily proved and the provided numerical results demonstrate its rapid convergence to an optimal solution. Such a cognitive network is even more desirable as it outperforms a non-cognitive network, which uses a partial bandwidth for D2D users only.

关键词： Unmanned aerial vehicle (UAV)-enabled network cognitive device-to-device (D2D) communication bandwidth allocation power allocation D2D assignment mixed-integer programming

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Two-level lot-sizing with raw-material perishability and deterioration

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2020年第3期71卷 417-432页

作者： Acevedo-Ojeda, Andres Contreras, Ivan Chen, Mingyuan Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ Canada Univ Pontificia Bolivariana Secc Bucaramanga Ind Engn Fac Bucaramanga Colombia Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ H3T 2A7 Canada

In many production systems, it is common to face significant rates of product deterioration, referring to physical exhaustion, loss of functionality and volume, or even obsolescence. This deterioration property, known as perishability, prevents such products from being used after their expiration time. We present lot-sizing problems that incorporate raw-material perishability and analyse how these considerations enforce specific constraints on a set of fundamental decisions, particularly for multi-level structures. We study three variants of the two-level lot-sizing problem incorporating different types of raw-material perishability: (a) fixed shelf-life, (b) functionality deterioration, and (c) functionality-volume deterioration. We propose mixed-integer programming formulations for each of these variants and perform computational experiments with sensitivity analyses. We analyse the added value of explicitly incorporating perishability considerations into production planning problems. For this, we compare the results of the proposed formulations with those obtained by implementing a sequential approach that adapts a standard two-level lot-sizing solution with a Silver-Meal-based rolling-horizon algorithm.

关键词： Raw-material perishability lot-sizing shelf-life deterioration production planning two-level lot-sizing mixed-integer programming batch ordering

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Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 10873-10894页

作者： Akbaripour, Hossein Houshmand, Mahmoud Sharif Univ Technol Dept Ind Engn Tehran Iran

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function

关键词： Cloud manufacturing Service composition and optimal selection mixed-integer programming Landscape analysis Imperialist competitive algorithm

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A neurodynamic algorithm to optimize residential demand response problem of plug-in electric vehicle q

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NEUROCOMPUTING 2020年 405卷 1-11页

作者： Zong, Xun Wang, Hui He, Xing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China

This paper proposes a nonconvex mixed-integer programming(MIP) electricity model of household load that is classified into base load and plug-in electric vehicle (PEV). The main objective of this model is to minimize the electricity cost of domestic user by adjusting the charging and discharging strategy of PEV according to the information of real-time price(RTP). In order to settle this problem, a neurodynamic algorithm which combines feedback neural network and inertial neural subnetwork is used. In this algorithm, the feedback neural networks are given as subnetworks to find a local optimal point and the inertial neural subnetwork can obtain different local optimal points by adjusting the inertial factor in global scope. The validity and the feasible of the proposed algorithm can be proven by performance evaluation of simulation results.

关键词： Plug-in electric vehicles mixed-integer programming Inertial neural subnetwork Feedback neural network

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Application of a methodology to design a municipal waste pre-collection network in real scenarios

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WASTE MANAGEMENT & RESEARCH 2020年第1-Sup期38卷 117-128页

作者： Cavallin, Antonella Gabriel Rossit, Diego Herran Symonds, Victoria Alejandro Rossit, Daniel Frutos, Mariano Univ Nacl Sur Dept Engn Ave Alem 1253B8000FTN Bahia Blanca Buenos Aires Argentina Univ Nacl Sur INMABB CONICET Bahia Blanca Buenos Aires Argentina Univ Nacl Sur IIESS CONICET Bahia Blanca Buenos Aires Argentina

The design of efficient municipal solid waste (MSW) pre-collection networks can contribute to the global efficiency and sustainability of the reverse logistic chain of MSW in modern cities. With this aim, in this paper a comprehensive methodology that involves making decisions in several stages, from waste fraction classification to the final optimization of waste bins' location, was applied in two real cases of the city of Bahia Blanca, Argentina. This city, does not have much available data about waste generation and, therefore, an important fieldwork had to be performed for applying this methodology, involving estimating population density per block and waste generation rate per inhabitant, identifying the location of commercial and institutional buildings and also estimating its generation rate, as well as performing a characterization of the MSW from similar studies in the literature and surveys performed to make decisions. The modelling of the urban characteristics was performed in a geographic information system. In the bins' location problem, a mixed-integer optimization model was applied, seeking to minimize the investment costs, given the maximum area available and the capacity of the bins. Different scenarios were analysed, considering different collection frequencies and the maximum distance to be travelled by the user.

关键词： Municipal solid waste pre-collection network estimation of waste generation rate waste bins' location mixed-integer programming

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