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Joint location and pricing within a user-optimized environment

EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2020年第1期8卷 61-84页

作者： Dan, Teodora Lodi, Andrea Marcotte, Patrice Univ Montreal CP 6128Succursale Ctr Ville Montreal PQ H3C 3J7 Canada Polytech Montreal POB 6079 Montreal PQ H3C 3A7 Canada

In the design of service facilities, whenever the behaviour of customers is impacted by queueing or congestion, the resulting equilibrium cannot be ignored by a firm that strives to maximize revenue within a competitive environment. In the present work, we address the problem faced by a firm that makes decisions with respect to location, service levels and prices and that takes explicitly into account user behaviour. This situation is modelled as a nonlinear mathematical program with equilibrium constraints that involves both discrete and continuous variables, and for which we propose an efficient algorithm based on an approximation that can be solved for its global optimum.

关键词： Pricing Location pricing Bilevel programming mixed-integer programming Equilibrium Queueing Nonconvex

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Management of nutrient export from diffuse sources in watersheds for environmental protection under uncertainty

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JOURNAL OF SOIL AND WATER CONSERVATION 2022年第3期77卷 305-321页

作者： Rong, Q. Su, M. Yue, W. Cai, Y. Dongguan Univ Technol Res Ctr Ecoenvironm Engn Dongguan Peoples R China Guangdong Univ Technol Inst Environm & Ecol Engn Guangzhou Peoples R China

Nutrient export from diffuse sources poses a significant threat to watersheds, and methods to support the effective management of these watersheds is essential. However, real-world nutrient flows and watershed management systems are highly complex, with a high degree of uncertainty in their descriptive information. Thus, an effective optimization method for watershed management must be developed to deal with this uncertainty, which is crucial for formulating and implementing appropriate management practices. This research presents an inexact simulation-based left-hand-side chance-constrained mixed-integer programming (ISLCCMIP) model to determine nutrient export characteristics and optimal management strategies. By introducing interval and stochastic parameters into the simulation process, the uncertain characteristics of nutrient export loads can be considered. Uncertainties and complexities in management processes can also be handled through incorporating interval parameter programming and mixed-integer programming within a left-hand-side chance-constrained programming framework. The proposed ISLCCMIP model can correlate the randomness in the simulation process and the optimization results. The East River basin in South China was selected as the case study area to apply the proposed model. The results indicated that inorganic nitrogen (N) and phosphorus (P) were the main forms for the nutrient export from diffuse sources in this basin. Five of the nine subbasins were identified as critical source areas for nutrient export. Planting areas of different crops, application amounts of chemical fertilizers, and quantities of livestock types can be optimized to achieve the maximum economic benefit under limited N and P discharge permits. Particularly, planting areas of vegetable and soybean (Glycine max L.) would be first decreased, while rice (Oryza sativa L.) and tubers would be retained, as the pollution emission standards become stricter. Decrease in the planting are

关键词： chance constrained programming interval parameter programming mixed-integer programming nutrient export from watersheds optimization of agricultural production

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FOUNDATIONS OF SEMIALGEBRAIC GENE-ENVIRONMENT NETWORKS

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JOURNAL OF DYNAMICS AND GAMES 2020年第4期7卷 253-268页

作者： Kropat, Erik Weber, Gerhard-Wilhelm Tirkolaee, Erfan Babaee Christerweg 12 D-83624 Otterfing Germany Poznan Univ Tech Fac Engn Management Poznan Poland Middle East Tech Univ Inst Appl Math Ankara Turkey Mazandaran Univ Sci & Technol Dept Ind Engn Babol Iran Istinye Univ Dept Ind & Syst Engn Istanbul Turkey

Gene-environment network studies rely on data originating from different disciplines such as chemistry, biology, psychology or social sciences. Sophisticated regulatory models are required for a deeper investigation of the unknown and hidden functional relationships between genetic and environmental factors. At the same time, various kinds of uncertainty can arise and interfere with the system's evolution. The aim of this study is to go beyond traditional stochastic approaches and to propose a novel framework of semialgebraic gene-environment networks. Foundation is laid for future research, methodology and application. This approach is a natural extension of interconnected systems based on stochastic, polyhedral, ellipsoidal or fuzzy (linguistic) uncertainty. It allows for a reconstruction of the underlying network from uncertain (semialgebraic) data sets and for a prediction of the uncertain futures states of the system. In addition, aspects of network pruning for large regulatory systems in genome-wide studies are discussed leading to mixed-integer programming (MIP) and continuous programming.

关键词： Gene-environment networks semialgebraic regression semialgebraic uncertainty geometric model forecasting uncertainty modeling mixed-integer programming continuous programming

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Structured discrete shape approximation: Theoretical complexity and practical algorithm

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COMPUTATIONAL GEOMETRY-THEORY AND APPLICATIONS 2021年 99卷

作者： Tillmann, Andreas M. Kobbelt, Leif Rhein Westfal TH Aachen Visual Comp Inst & Chair Operat Res Lehrstuhl Informat 8Ahornstr 55 D-52074 Aachen Germany Rhein Westfal TH Aachen Visual Comp Inst Lehrstuhl Informat 8Ahornstr 55 D-52074 Aachen Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Math Optimizat Univpl 2 D-38106 Braunscheig Germany

We consider the problem of approximating a two-dimensional shape contour (or curve segment) using discrete assembly systems, which allow to build geometric structures based on limited sets of node and edge types subject to edge length and orientation restrictions. We show that already deciding feasibility of such approximation problems is NP-hard, and remains intractable even for very simple setups. We then devise an algorithmic framework that combines shape sampling with exact cardinality minimization to obtain good approximations using few components. As a particular application and showcase example, we discuss approximating shape contours using the classical Zometool construction kit and provide promising computational results, demonstrating that our algorithm is capable of obtaining good shape representations within reasonable time, in spite of the problem's general intractability. We conclude the paper with an outlook on possible extensions of the developed methodology, in particular regarding 3D shape approximation tasks. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Shape approximation Discrete assembly systems Computational complexity mixed-integer programming Zometool

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State estimation in online batch production scheduling: concepts, definitions, algorithms and optimization models

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COMPUTERS & CHEMICAL ENGINEERING 2021年 146卷

作者： Avadiappan, Venkatachalam Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA

The goal of this paper is to present concepts and methods that allow us to account for real-time data in online scheduling without embedding a dynamic model. First, we discuss the key role played by the progress status of a batch, the key state in scheduling. Specifically, we show how fractional changes in the progress status necessitate the re-calculation, using real-time data, of parameters used in scheduling models (which have always been thought to be constants). Second, we present algorithms for the calculation of the progress status and the above parameters. Third, we present a state-space resource task network (RTN) formulation employing parameters calculated in real-time and show why, in this context, it should account for delays of batches under execution as optimization decisions. Finally, we show how the proposed methods lead to tractable optimization problems and can be used to address problems that cannot be solved using existing scheduling approaches. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Chemical production scheduling Real-time optimization Resource task network mixed-integer programming

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Optimal Investments to Improve Grid Resilience Considering Initial Transient Response and Long-term Restoration

Optimal Investments to Improve Grid Resilience Considering I...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Pierre, Brian J. Arguello, Bryan Garcia, Manuel J. Sandia Natl Labs Elect Power Syst Res POB 5800 Albuquerque NM 87185 USA Sandia Natl Labs Operat Res & Computat Anal POB 5800 Albuquerque NM 87185 USA

ISBN: (数字)9781728128221

ISBN: (纸本)9781728128221

This paper presents a multi-time period two-stage stochastic mixed-integer linear optimization model which determines the optimal hardening investments to improve power system resilience to natural disaster threat scenarios. The input to the optimization model is a set of scenarios for specific natural disaster events, that is based on historical data. The objective of the optimization model is to minimize the expected weighted load shed from the initial impact and the restoration process over all scenarios. The optimization model considers the initial impact of the severe event by using electromechanical transient dynamic simulations. The initial impact weighted load shed is determined by the transient simulation, which allows for secondary transients from protection devices and cascading failures. The rest of the event, after the initial shock, is modeled in the optimization with a multi-time period dc optimal power flow (DCOPF) which is initialized with the solution from the dynamic simulation. The first stage of the optimization model determines the optimal investments. The second stage, given the investments, determines the optimal unit commitment, generator dispatch, and transmission line switching during the multi-time period restoration process to minimize the weighted load shed over all scenarios. Note, an investment will change the transient simulation result, and therefore change the initialization to the DCOPF restoration model. The investment optimization model encompasses both the initial impact (dynamic transient simulation results) and the restoration period (DCOPF) of the event, as components come back online. The model is tested on the IEEE RTS-96 system.

关键词： Power system dynamics optimization mixed-integer programming power system resilience power system transients cascading failures power system restoration

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Onshore wind farm-Reliability centered cable routing

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 196卷

作者： Zarkovic, Sanja Duvnjak Shayesteh, Ebrahim Hilber, Patrik KTH Royal Inst Technol Sch Elect Engn & Comp Sci Dept Electromagnet Engn Stockholm Sweden Swedish Natl Grid Stockholm Sweden

Designing an onshore wind farm is a complex planning process that requires various stages to be completed. The prime focus of this work is to assist planners and experts in finding the optimal cable layout of the onshore wind farm. The optimization algorithm is designed using mixed integer linear programming (MILP). The MILP algorithm takes into account system reliability, power transfer capacities and power quality issue. The novelty in this optimization algorithm is to simultaneously minimize cable installation cost and the cost of lost energy production and therefore maximize the reliability of the system. Additionally, the algorithm supports the optimal selection among different cable options, with different features, prices and capacities. By calculating voltage increase at the point of connection (POC), power quality issue is considered as well. The designed algorithm provides optimal results for four different wind farm layouts. Every layout is tested for three different case scenarios, where different number and type of cables are considered. The results show that more cable options contribute in lowering the total costs. Moreover, cables with higher capacity can help in improving the power quality issue.

关键词： Distribution system distribution system planning cable routing wind farm mixed-integer programming power system reliability

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Distributionally robust facility location with bimodal random demand

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COMPUTERS & OPERATIONS RESEARCH 2021年 134卷

作者： Shehadeh, Karmel S. Sanci, Ece Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Univ Bath Sch Management Bath Avon England

In this paper, we consider a decision-maker who wants to determine a subset of locations from a given set of candidate sites to open facilities and accordingly assign customer demand to these open facilities. Unlike classical facility location settings, we focus on a new setting where customer demand is bimodal, i.e., display, or belong to, two spatially distinct probability distributions. We assume that these two distributions are ambiguous, and only their mean values and ranges are known. Therefore, we construct a scenario-wise ambiguity set with two scenarios corresponding to the demand's two distinct distributions. Then, we formulate a distributionally robust facility location (DRFL) model that seeks to find the number and locations of facilities to open that minimize the fixed cost of opening facilities and the worst-case (maximum) expectation of transportation and unmet demand costs. We take the worst-case expectations over all possible demand distributions residing in the scenario-wise ambiguity set. We propose a decomposition-based algorithm to solve our min-max DRFL model and derive lower bound inequalities that accelerate the algorithm's convergence. In a series of numerical experiments, we demonstrate our approach's superior computational and operational performance compared with the stochastic programming approach and a distributionally robust approach that does not consider the demand's bimodality. Our results draw attention to the need to consider the multi-modality and ambiguity of the demand distribution in many strategic real-world problems.

关键词： Facility location Distributionally robust optimization Bimodal demand mixed-integer programming Decomposition-based algorithm

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Multicast Traffic Throughput Maximization through Joint Dynamic Modulation and Coding Schemes Assignment, and Transmission Power Control in Wireless Sensor Networks

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SENSORS 2021年第10期21卷 3411页

作者： Ostrowski, Bartlomiej Pioro, Michal Tomaszewski, Artur Warsaw Univ Technol Inst Telecommun PL-00665 Warsaw Poland

The paper concerns multicast packet traffic throughput maximization in multi-hop wireless sensor networks with time division multiple access to radio channel. We assume that the modulation and coding schemes (MCSs) that are used by the (broadcasting) nodes as well as the transmission power of the nodes are adjustable. This leads to the main research question studied in this paper: to what extent traffic throughput can be increased by proper MCSs assignment and transmission power control (TPC) at the nodes? To answer this question, we introduce mixed-integer programming formulations for joint MCSs assignment and TPC optimization, together with a solution algorithm. Finally, we present a numerical study illustrating the considerations of the paper. The numerical results show a significant gain being achieved by proper MCSs assignment, which is further increased by applying TPC.

关键词： wireless sensor networks multicast traffic throughput maximization transmission scheduling TDMA transmission power control IoT MCS mixed-integer programming

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A general framework and optimization models for the scheduling of continuous chemical processes

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AICHE JOURNAL 2021年第10期67卷

作者： Wu, Yaqing Maravelias, Christos T. Univ Wisconsin Madison Dept Chem & Biol Engn Madison WI USA Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ 08544 USA

We present a framework for the formulation and solution of continuous process scheduling problems. We focus on modeling transient operations such as startups, shutdowns, and transitions between steady states. First, we show how the concept of processing tasks can be generalized to represent continuous processes, including their transient operations. Second, we discuss how to systematically calculate the parameters describing material consumption/production and utility consumption during transient operations. Finally, we present new mixed-integer linear programming formulations for the scheduling of continuous chemical production.

关键词： continuous production mixed-integer programming transient operations

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