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17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Flores-Carrasco, J. Morillo-Torres, D. Escobar, J. W. Linfati, R. Gatica, G. Univ Andres Bello Santiago Chile Pontificia Univ Javeriana Cali Cali Colombia Univ Valle Cali Colombia Univ Bio Bio Concepcion Chile

Waste collection is one of the most important processes in the main cities. Its current volume obliges governments to establish efficient measures to satisfy the collection offer. Over 70% of waste can be recycled. It is necessary to identify the collection centers, the routes to be followed either by minor collectors and/or vehicles, the specific day of collection, and an estimate of the volumes of waste to recycling. The problem has been mathematically modeled in the literature. However, they suffer from the differentiation of the type of waste and the day of collection. This paper presents a new variant of the classical routing problem, called the Periodic Location-Routing with Selective Recycling Problem. It considers collection centers, types of containers by-product, day of collection, and subsequent routing. Besides, two solution approaches are presented: first, a model based on mixed-integer programming, and second, a greedy constructive heuristic. Several sets of instances are proposed. Preliminary results are favorable, achieving to solve instances with up to 15 customers for the exact approach and experimenting up to 1 000 customers with the heuristics. The model and the solution technique are scalable. Copyright (C) 2021 The Authors.

关键词： Periodic Location-Routing mixed-integer programming greedy constructive heuristic waste recycling

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Impact of battery degradation on energy cost and carbon footprint of smart homes

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期209卷 1页

作者： Toosi, Hooman Ekhteraei Merabet, Adel Swingler, Andrew St Marys Univ Div Engn Halifax NS B3H 3C3 Canada Univ Prince Edward Isl Sustainable Design Engn Fac Charlottetown PE C1A 4P3 Canada

Optimal energy management in residential buildings with battery storage devices largely relies on the implementation of accurate battery degradation models. A proper wear model to use for the battery scheduling at homes should be universally applicable to different battery technologies and precisely model the impact of the major parameters that contribute to the capacity loss. In this work, a technology-agnostic battery wear model has been proposed to address the degradation caused by the irregular cycling of batteries in residential buildings. The proposed wear model provides the DoD-associated degradation for charging/discharging events between random SoCs only by employing the lifecycle curve of batteries which is usually provided by battery manufactures. This model has been formulated in the framework of mixed-integer programming (MIP) such that it can be directly incorporated into MIP optimization models to provide optimal unit commitment solutions without sacrificing other objectives of the problem. To facilitate the implementation of the nonlinear wear coefficient in MIP models, a curve-fitted version of the wear coefficient is used in the MIP problem. A comprehensive study on the costs and carbon footprint of a smart home has been carried out in this paper by producing a MIP problem that incorporates the proposed battery wear model. The problem has been solved with different approaches to minimize costs, capacity loss, and emissions of the system. The optimization results show that the application of the presented wear model managed to lower the cost, carbon footprint, and battery degradation by 78%, 30%, and 81% respectively.

关键词： Battery degradation Irregular cycling Carbon footprint mixed-integer programming Electric vehicle Energy storage system

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Locating park-and-ride facilities for resilient on-demand urbanmobility

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年第0期158卷 102557-102557页

作者： Henry, Elise Furno, Angelo El Faouzi, Nour-Eddin Rey, David Univ Gustave Eiffel Univ Lyon ENTPE LICIT UMR T9401 F-69675 Lyon France Univ Cote dAzur SKEMA Business Sch Sophia Antipolis Campus Nice France

Urban transport networks, yet essential, are frequently impacted by recurrent disruptionssuch as public transport failures, adverse weather or strikes. Flexible transit systems canbe used to limit the impacts of recurrent disruptions on urban mobility. In this study, weexamine the potential of on-demand park-and-ride systems to complement an existing transportinfrastructure and improve network resilience. We formulate a stochastic park-and-ride facilitylocation problem which captures the entire user trip chain from the origin to the destinationvia pick up and drop off nodes in a mobility network. We use a Logit model to capture users'mode choice between paths in the park-and-ride system and a reserve travel option. Stochasticscenarios are used to represent varying traffic conditions to recurrent disruptions. The goalis to maximize the expected ridership in the park-and-ride system by identifying the optimallocation of pick up and drop off facilities and accounting for users' mode choice. We develop acustomized Lagrangian relaxation algorithm to solve the resulting mixed-integer programmingproblem on large scale instances and quantify its performance through a sensitivity analysis bycomparing it against a direct mixed-integer linear programming approach. Numerical resultsare presented on realistic instances generated based on the city of Lyon, France. Our findingsshow that the proposed methodology can provide key insights to support the deployment ofpark-and-ride systems and improve network resilience by capturing a significant proportion ofusers under disrupted traffic conditions

关键词： Park-and-ride On-demand mobility Resilience mixed-integer programming Lagrangian relaxation

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Interpreting clusters via prototype optimization

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2022年 107卷 102543-102543页

作者： Carrizosa, Emilio Kurishchenko, Kseniia Marin, Alfredo Morales, Dolores Romero Univ Seville Inst Matemat Seville Spain Copenhagen Business Sch Dept Econ Frederiksberg Denmark Univ Murcia Dept Estadist & Invest Operat Murcia Spain

In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analysis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely and distinctively as possible, i.e., the explanation is fulfilled by as many as possible individuals of the corresponding cluster, true positive cases, and by as few as possible individuals in the remaining clusters, false positive cases. We assume that a dissimilarity between the individuals is given, and propose distance-based explanations, namely those defined by individuals that are close to its so-called prototype. To find the set of prototypes, we address the biobjective optimization problem that maximizes the total number of true positive cases across all clusters and minimizes the total number of false positive cases, while controlling the true positive rate as well as the false positive rate in each cluster. We develop two mathematical optimization models, inspired by classic Location Analysis problems, that differ in the way individuals are allocated to prototypes. We illustrate the explanations provided by these models and their accuracy in both real-life data as well as simulated data. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Machine Learning Interpretability Cluster Analysis Prototypes mixed-integer programming

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Operating room planning with multiple downstream units

Operating room planning with multiple downstream units

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Andam, Arian Doulabi, Hossein Hashemi Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781665419703

Because of the importance of operating room management in hospitals, many researchers have attempted to develop mathematical programming models to use the available time in operating rooms as efficiently as possible. However, almost all researchers have considered only a single downstream unit in operating room planning. In this paper, we have developed a mixed-integer programming model for an operating room planning problem, which addresses multiple downstream units including wards, and ICUs. The proposed model allocates the patients to different operating rooms over a planning horizon while minimizing the sum of the opening cost of operating rooms, overtimes, and the cost of refusing patients, and the waiting cost of patients. The proposed model also addresses some other side features such as time windows for surgeries. We carried out some computational results and have performed an extensive sensitivity analysis on various cost parameters and also the capacity of each downstream. The computational results demonstrated that the proposed model is reliable and optimally solves instances with 315 patients in two minutes.

关键词： Operating room management multiple downstream units mixed-integer programming

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A matheuristic for the robust integrated airline fleet assignment, aircraft routing, and crew pairing problem

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COMPUTERS & OPERATIONS RESEARCH 2022年 137卷 105551-105551页

作者： Ben Ahmed, Mohamed Hryhoryeva, Maryia Hvattum, Lars Magnus Haouari, Mohamed Molde Univ Coll Fac Logist POB 2110 NO-6402 Molde Norway Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar

We address an integrated airline scheduling problem that combines three airline planning processes: fleet assignment, aircraft routing, and crew pairing. For a given daily flight schedule, the problem requires simultaneously assigning aircraft and crews to each scheduled flight, taking into account aircraft maintenance restrictions and crew work rules. We propose to solve this complex problem of integrated flight planning while taking into account robustness considerations. In this regard, robustness is achieved by restricting tight connections in the schedule and increasing the number of connections where crews follow the aircraft. We formulate the problem using a very large-scale, yet compact, mixed-integer programming model, and we propose a matheuristic consisting of a decomposition approach and a proximity search algorithm. Computational experiments carried out on real instances from a major airline and having up to 14,014 itineraries, 646 flights, and 202 aircraft provide evidence of the proposed approach's efficacy. In particular, we find that the average deviation from a conservative bound is at most equal to 0.6%.

关键词： mixed-integer programming Compact formulation Robustness Proximity search

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Stochastic optimization models for location and inventory prepositioning of disaster relief supplies

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2022年第0期144卷

作者： Shehadeh, Karmel S. Tucker, Emily L. Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Clemson Univ Dept Ind Engn Clemson SC USA

We consider the problem of preparing for a disaster season by determining where to open warehouses and how much relief item inventory to preposition in each. Then, after each disaster, prepositioned items are distributed to demand nodes during the post-disaster phase, and addi-tional items are procured and distributed as needed. There is often uncertainty in the disaster level, affected areas' locations, the demand for relief items, the usable fraction of prepositioned items post-disaster, procurement quantity, and arc capacity. To address uncertainty, we propose and analyze two-stage stochastic programming (SP) and distributionally robust optimization (DRO) models, assuming known and unknown (ambiguous) uncertainty distributions. The first and second stages correspond to pre-and post-disaster phases, respectively. We also propose a model that minimizes the trade-off between considering distributional ambiguity and following distributional belief. We obtain near-optimal solutions of our SP model using sample average approximation and propose a computationally efficient decomposition algorithm to solve our DRO models. We conduct extensive experiments using a hurricane season and an earthquake as case studies to investigate these approaches computational and operational performance.

关键词： Uncertainty modeling Facility location Inventory prepositioning Humanitarian logistics Stochastic optimization mixed-integer programming

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bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition

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INFORMS JOURNAL ON COMPUTING 2022年第6期34卷 2908-2929页

作者： Liu, Yanchao Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA

This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process and develops an efficient search algorithm that is able to solve practical instances of the MIP model faster than commercial solvers. The formulation is novel for it directly maximizes the Gini reduction, an effective split selection criterion that has never been modeled in a mathematical program for its nonconvexity. The proposed approach differs from other optimal classification tree models in that it does not attempt to optimize the whole tree;therefore, the flexibility of the recursive partitioning scheme is retained, and the optimization model is more amenable. The approach is implemented in an open-source R package named bsnsing. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared with trees trained by other decision tree codes including the rpart, C50, party, and tree packages in R. Compared with other optimal decision tree packages, including DL8.5, OSDT, GOSDT, and indirectly more, bsnsing stands out in its training speed, ease of use, and broader applicability without losing in prediction accuracy.

关键词： classification trees mixed-integer programming statistical computing R

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Optimizing fuel consumption on inland waterway networks: Local search heuristic for lock scheduling *

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2022年 109卷 102580-102580页

作者： Golak, Julian Arthur Pawel Defryn, Christof Grigoriev, Alexander Maastricht Univ Sch Business & Econ POB 616 NL-6200 MD Maastricht Netherlands Hamburg Univ Technol Inst Algorithms & Complex Hamburg Germany

Fuel consumption and CO 2 emission are among the main criteria to assess the environmental and economical impact of vessels on inland waterways. Both criteria, however, are directly affected by the vessels' sailing speed. In this paper, we present a mathematical programming formulation of the speed optimization problem, which aims at minimizing the aggregated fuel consumption on an inland waterway network. The network can consist of multiple river segments, connected by a set of locks, without restrictions on the configuration. To allow scalability towards realistic waterway networks, we also propose a local-search based heuristic to optimize the speed for individual vessels. We evaluate the effectiveness of the heuristic by comparing it to solving the exact mathematical programming formulation. For all computational experiments, we make use of real AIS data from a section of the Dutch river network. We observe that the heuristic is able to construct a high quality solution in realistic problem settings within reasonable amount of computation time.

关键词： AIS Data Inland waterway operations Local search heuristic mixed-integer programming

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Recent advances and challenges in optimization models for expansion planning of power systems and reliability optimization

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COMPUTERS & CHEMICAL ENGINEERING 2022年 165卷

作者： Cho, Seolhee Li, Can Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Purdue Univ Davidson Sch Chem Engn 480 Stadium Mall Dr W Lafayette IN 47907 USA

Optimization models for expansion planning of power systems aim to determine capacities, investment timing, and location of power systems to satisfy the power demands while minimizing the total cost. The models have become complex in recent years to reflect both regulations on conventional energy sources and the increasing penetration of renewable energy sources (RES). This paper reviews the basic concepts and optimization models for expansion planning of power systems. We first explain the definition and features of generation expansion planning (GEP), transmission expansion planning (TEP), and generation and transmission expansion planning (GTEP). To address the computational challenges of large-scale expansion planning problems, we review several simplifications including temporal and spatial aggregation, and decomposition methods. This paper also addresses power system reliability defined as the probability of satisfying the load demand while withstanding failures of components. The goal of this paper is to provide a research overview, discuss trends in expansion planning of power systems, and suggest directions for future research.

关键词： Power systems Optimization Expansion planning mixed-integer programming Reliability

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