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A mixed rebalancing strategy in bike sharing systems

ENGINEERING OPTIMIZATION 2022年第7期54卷 1160-1177页

作者： Xu, Guoxun Xiang, Ting Li, Yanfeng Li, Jun Guo, Qiang Hainan Univ Sch Tourism Haikou Hainan Peoples R China Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China

In a bike sharing system (BSS), for various reasons, such as truck fuel load, truck capacity and high customer demand, the number of trucks deployed by the BSS operator is usually insufficient and they cannot balance all of the stations in time. To incentivize users to relocate some of the excess bikes from stations with surplus bikes to bike deficient stations, a mixed rebalancing strategy is proposed. The problem is formulated as a mixed-integer linear programming model. A hybrid genetic algorithm is developed to solve the proposed problem. Computational experiments are performed to illustrate the properties of the problem. The numerical results show that the proposed solution method can yield high-quality solutions in short computing times.

关键词： Bike rebalancing problem mixed rebalancing strategy mixed-integer linear programming hybrid genetic algorithm

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A compact quadratic model and linearizations for the minimum linear arrangement problem

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DISCRETE APPLIED MATHEMATICS 2022年 323卷 134-148页

作者： de Andrade, Rafael Castro Bonates, Tiberius de Oliveira e Campelo, Manoel Ferreira, Mardson da Silva Univ Fed Ceara Campus Pici BL 910 BR-60440900 Fortaleza CE Brazil

Given an undirected connected graph G = (V, E), the minimum linear arrangement problem (MinLA) consists in determining a permutation pi := (pi 1, ... , pi n) of the node -set V = {1, ... , n}, which minimizes the sum of edge costs |pi i - pi j| over all edges {i, j} is an element of E. We introduce a compact quadratic formulation, prove its correctness, and generate new mixed-integer linear formulations that require a very small number of variables and constraints. The idea behind the way we model permutations allows the development of strong valid constraints to strengthen the new formulations. We also adapt some cutting plane procedures from the literature to one of the new models. Computational experiments with benchmark and new instances are very encouraging and improve state-of-the-art solution approaches from the literature.(c) 2021 Elsevier B.V. All rights reserved.

关键词： Compact model Quadratic programming mixed-integer linear programming linear arrangement Valid constraints

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Two-Scale Model Predictive Control for Resource Optimization Problems With Switched Decisions

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IEEE ACCESS 2022年 10卷 57824-57834页

作者： Balaguer-Herrero, Pedro Carlos Alfonso-Gil, Jose Itzame Martinez-Marquez, Camilo Martinez-Navarro, German Orts-Grau, Salvador Segui-Chilet, Salvador Univ Jaume I UJI Ind Engn & Design Dept Castellon De La Plana 12071 Spain Univ Politecn Valencia Inst Interuniv Invest Reconocimiento Mol & Desarr Valencia 46022 Spain

Model predictive control (MPC) is widely used in resource optimization problems because it naturally deals with bounded controls and states and allows predictive information to be included. However, at each sampling instant, an optimization problem must be solved. Resource optimization problems with switching control actions naturally lead to optimization problems with integer decision variables, which are computationally costly, particularly when the number of variables is large. As a result, the approach of directly discretizing (DD) the problem to derive a mixed-integer linear program (MILP) sets fundamental limitations on the MPC sampling rate owing to the computational time required to solve the optimization problem. In this paper, we propose a two-scale optimization algorithm (TSOA) for MPC. On the first-scale, the entire prediction horizon is considered and the algorithm provides the optimal resources to be used at each interval with a constant weighting cost. This optimization problem may be cast as a linear program (LP);thus, it is computationally tractable even for a large number of variables and constraints. In the second-scale, the switching nature of the decision variable is recovered by posing an MILP to deploy the optimal resources computed in the previous scale. In this manner, the MILP is solved for a shorter time interval than the entire prediction horizon, thus reducing the number of variables in the optimization problem. The simulation results demonstrate the computational advantages of the proposed algorithm compared to direct problem discretization and optimization.

关键词： Optimization Switches Costs Prediction algorithms Cost function Control systems Minimization Energy optimization linear programming mixed-integer linear programming model predictive control resource optimization switched controls

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Optimal design and operation of integrated microgrids under intermittent renewable energy sources coupled with green hydrogen and demand scenarios

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第65期47卷 27848-27865页

作者： Tatar, Su Meyra Akulker, Handan Sildir, Hasan Aydin, Erdal Koc Univ Dept Chem & Biol Engn TR-34457 Istanbul Turkey Bogazici Univ Dept Chem Engn TR-34342 Istanbul Turkey Gebze Tech Univ Dept Chem Engn TR-41400 Kocaeli Turkey Koc Univ Koc Univ TUPRAS Energy Ctr KUTEM TR-34450 Istanbul Turkey

Renewable energy integration into existing or new energy hubs together with Green technologies such as Power to Gas and Green Hydrogen has become essential because of the aim of keeping the average global temperature rise within 2 degrees C with regard to the Paris Agreement. Hence, all energy markets are expected to face substantial transitions world-wide. On the other hand, investigation of renewable energy systems integrated with green chemical conversion, and in particular combination of green hydrogen and synthetic methanation, is still a scarce subject in the literature in terms of optimal and simultaneous design and operation for integrated energy grids under weather intermittency and demand uncertainty. In fact, the integration of such promising new technologies has been studied mainly in the operational phase, without considering design and management simultaneously. Thus, in this work, a multi-period mixed-integer linear programming (MILP) model is formulated to deal with the aforementioned challenges. Under current carbon dioxide limitations dictated by the Paris Agreement, this model computes the best configuration of the renewable and non-renewable-based generators, their optimal rated powers, capacities and scheduling sequences from a large candidate pool containing thirty-nine different equipment simultaneously. Moreover, the effect of the intermittent nature of renewable resources is analyzed comprehensively under three different scenarios for a specific location. Accordingly, a practical scenario generation method is proposed in this work. It is observed that photovoltaic, oil co-generator, reciprocating ICE, micro turbine, and bio-gasifier are the equipment that is commonly chosen under the three different scenarios. Results also show that concepts such as green hydrogen and power-to-gas are currently not preferable for the investigated location. On the other hand, analysis shows that if the emission limits are getting tightened, it is expected tha

关键词： Optimal renewable energy integration mixed-integer linear programming Scenario generation Green hydrogen Renewable energy management

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linear Optimization Based Distribution Grid Flexibility Aggregation Augmented With OLTC Operational Flexibilities

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IEEE ACCESS 2022年 10卷 77510-77521页

作者： Majumdar, Neelotpal Sarstedt, Marcel Kluss, Leonard Hofmann, Lutz Leibniz Univ Hannover Elect Power Engn Sect Inst Elect Power Syst D-30167 Hannover Germany

Ancillary services e.g., voltage control, congestion management and frequency control, require to be compensated increasingly from the Distributed Energy Resources (DERs). DERs are predominantly wind and photo-voltaic power plants, the major share of which are installed at the distribution grid level. Therefore, the previously passive distribution grids require transformation towards a more active role. Provision of ancillary services from the distribution grid level, requires assessment of active and reactive power flexibility (PQ-flexibility) potentials. Furthermore, increased renewable penetration correlates to increased responsibility of the Distribution System Operators (DSOs) for assessing the flexibility potentials. An aggregation of distribution grid potentials, subject to technical grid constraints and technological power limitations, is termed as Feasible Operating Region (FOR) of the distribution grid. The FOR effectively serves as an interface between the DSOs and the Transmission System Operators (TSO), for flexibility exchanges and planning of ancillary services provision. The determination of the FOR is established in current research, using different algorithms e.g stochastic methods, meta-heuristic programming and mathematical optimization techniques. In this paper, an FOR determination algorithm using successive linear programming (sLP) is proposed and validated against established optimization approaches on a uniform medium voltage (MV) grid model. Comparisons reveal competitiveness with established methods and an added advantage of fast calculation times, suitable for real time assessments. Further enhancement of the FOR is proposed, by integrating discrete transformer tap-changing operational flexibilities using a successive mixed integer linear programming (sMILP). Results demonstrate an increase in the flexibility potential from the distribution grid.

关键词： Sensitivity Transformers Optimization Voltage Optimization methods linear programming Monte Carlo methods Ancillary services flexibility aggregation feasible operating region stochastics meta-heuristics successive linear programming mixed-integer linear programming

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Bi-objective optimization for a multi-period COVID-19 vaccination planning problem

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2022年 110卷 102617-102617页

作者： Tang, Lianhua Li, Yantong Bai, Danyu Liu, Tao Coelho, Leandro C. Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 611756 Sichuan Peoples R China Univ Laval Canada Res Chair Integrated Logist CIRRELT Laval PQ Canada

This work investigates a new multi-period vaccination planning problem that simultaneously optimizes the total travel distance of vaccination recipients (service level) and the operational cost. An optimal plan determines, for each period, which vaccination sites to open, how many vaccination stations to launch at each site, how to assign recipients from different locations to opened sites, and the replenishment quantity of each site. We formulate this new problem as a bi-objective mixed-integer linear program (MILP). We first propose a weighted-sum and an epsilon-constraint methods, which rely on solving many single-objective MILPs and thus lose efficiency for practical-sized instances. To this end, we further develop a tailored genetic algorithm where an improved assignment strategy and a new dynamic programming method are designed to obtain good feasible solutions. Results from a case study indicate that our methods reduce the operational cost and the total travel distance by up to 9.3% and 36.6%, respectively. Managerial implications suggest enlarging the service capacity of vaccination sites can help improve the performance of the vaccination program. The enhanced performance of our heuristic is due to the newly proposed assignment strategy and dynamic programming method. Our findings demonstrate that vaccination programs during pandemics can significantly benefit from formal methods, drastically improving service levels and decreasing operational costs. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： COVID-19 Vaccination planning Multi-period location-allocation Multi-objective optimization mixed-integer linear programming

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Evolutionary neural network for learning of scalable heuristics for pickup and delivery problems with time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期169卷

作者： Jun, Sungbum Lee, Seokcheon Dongguk Univ Dept Ind & Syst Engn Seoul 04620 South Korea Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

In this paper, we address the pickup and delivery problem with time windows (PDP-TW) and heterogenous vehicles for minimisation of total tardiness by learning heuristics from a given set of solutions. In order to extract scalable heuristics from optimal or best feasible solutions, we propose a machine-learning (ML)-based approach called ENSIGHT (Evolutionary Neural network with Scalable Information for Generation of Heuristics for Transportation). ENSIGHT consists of three phases: solution generation, interpretation of solutions, and improvement of heuristics by an evolutionary neural network (ENN). First, a set of optimal or best feasible solutions for the training set of problem instances is acquired by using the proposed mathematical model. Second, as for the process interpreting those solutions, an approach for transforming them into training data by way of scalable input attributes as well as output discretisation is followed. Third, the ENN improves the learned heuristics by an evolutionary parameter optimisation process for minimization of total tardiness. To verify the performance of the proposed ENSIGHT, we conducted experiments and the results of which showed that it outperforms other ML techniques and the current dispatching rules (DRs). Moreover, the approach was demonstrated to be effective in learning scalable heuristics based on combined scalable inputs and discretisation as well as an evolutionary improvement process.

关键词： Pickup and delivery problem Machine learning Evolutionary neural network Scalable information mixed-integer linear programming

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MILP-based discrete sizing and topology optimization of truss structures: new formulation and benchmarking

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第10期65卷 1-25页

作者： Brutting, Jan Senatore, Gennaro Fivet, Corentin Swiss Fed Inst Technol Lausanne EPFL Struct Xplorat Lab Passage Cardinal 13b CH-1700 Fribourg Switzerland Swiss Fed Inst Technol Lausanne EPFL Appl Comp & Mech Lab Lausanne Switzerland

Discrete sizing and topology optimization of truss structures subject to stress and displacement constraints has been formulated as a mixed-integer linear programming (MILP) problem. The computation time to solve a MILP problem to global optimality via a branch-and-cut solver highly depends on the problem size, the choice of design variables, and the quality of optimization constraint formulations. This paper presents a new formulation for discrete sizing and topology optimization of truss structures, which is benchmarked against two well-known existing formulations. Benchmarking is carried out through case studies to evaluate the influence of the number of structural members, candidate cross sections, load cases, and design constraints (e.g., stress and displacement limits) on computational performance. Results show that one of the existing formulations performs significantly worse than all other formulations. In most cases, the new formulation proposed in this work performs best to obtain near-optimal solutions and verify global optimality in the shortest computation time.

关键词： Structural optimization Truss Sizing optimization Topology optimization mixed-integer linear programming Gurobi

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Resiliency-Driven Multi-Step Critical Load Restoration Strategy Integrating On-Call Electric Vehicle Fleet Management Services

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IEEE TRANSACTIONS ON SMART GRID 2022年第4期13卷 3118-3132页

作者： Erenoglu, Ayse Kubra Sancar, Semanur Terzi, Idil Su Erdinc, Ozan Shafie-Khah, Miadreza Catalao, Joao P. S. Fatih Sultan Mehmet Vakyf Univ Dept Elect Elect Engn TR-34445 Istanbul Turkey Yildiz Tech Univ Dept Elect Engn TR-34220 Istanbul Turkey Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

In order to enhance the restoration capability of the distribution system during emergency conditions, a resiliency-driven critical load restoration strategy is propounded in this paper. Electric vehicles (EVs) are considered for the grid-support services to deal with challenges on such occasions, in order to maintain the power supply continuity of critical loads by reducing the number of outage periods. The collaboration between fleet operator and distribution system operator is considered in the proposed scheme, making it possible to direct available EVs to the damaged areas. The random characteristic of the seismic event is captured by generating numerous hazard scenarios using a probabilistic approach with the Monte Carlo Simulation (MCS) technique. Afterwards, the unavailability of overhead distribution branches is determined within the fragility curve concept. Besides, the uncertainties caused by EV mobility are considered by performing learning-based analyses for forecasting the location and amount of EVs in the related zone. The obtained data is processed as input parameters in a mixed-integer linear programming (MILP) framework-based stochastic model. Besides, the conceptually developed interfaces for all stakeholders in the proposed scheme are described in detail for bridging the gap between the theoretical background of the concept and practical real-world implementation.

关键词： Resilience Earthquakes Uncertainty Substations Hurricanes Routing Maintenance engineering Electric vehicles forecasting mixed-integer linear programming optimization resiliency restoration strategy

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A Comparison of Two mixed-integer linear Programs for Piecewise linear Function Fitting

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INFORMS JOURNAL ON COMPUTING 2022年第2期34卷 1042-1047页

作者： Warwicker, John Alasdair Rebennack, Steffen Karlsruhe Inst Technol Inst Operat Res Stochast Optimizat D-76185 Karlsruhe Raden Wurttembe Germany

The problem of fitting continuous piecewise linear (PWL) functions to discrete data has applications in pattern recognition and engineering, amongst many other fields. To find an optimal PWL function, the positioning of the breakpoints connecting adjacent linear segments must not be constrained and should be allowed to be placed freely. Although the univariate PWL fitting problem has often been approached from a global optimisation perspective, recently, two mixed-integer linear programming approaches have been presented that solve for optimal PWL functions. In this paper, we compare the two approaches: the first was presented by Rebennack and Krasko [Rebennack S, Krasko V (2020) Piecewise linear function fitting via mixed-integer linear programming. INFORMS J. Comput. 32(2):507-530] and the second by Kong and Maravelias [Kong L, Maravelias CT (2020) On the derivation of continuous piecewise linear approximating functions. INFORMS J. Comput. 32(3):531-5461 Both formulations are similar in that they use binary variables and logical implications modelled by big-M constructs to ensure the continuity of the PWL function, yet the former model uses fewer binary variables. We present experimental results comparing the time taken to find optimal PWL functions with differing numbers of breakpoints across 10 data sets for three different objective functions. Although neither of the two formulations is superior on all data sets, the presented computational results suggest that the formulation presented by Rebennack and Krasko is faster. This might be explained by the fact that it contains fewer complicating binary variables and sparser constraints. Summary of Contribution: This paper presents a comparison of the mixed-integer linear programming models presented in two recent studies published in the INFORMS Journal on Computing. Because of the similarity of the formulations of the two models, it is not clear which one is preferable. We present a detailed comparison of the two

关键词： piecewise linear function mixed-integer linear programming function fitting

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