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Design a sustainable supply chain for the textile and clothing industry with consideration of carbon emissions

IFAC-PapersOnLine 2022年第10期55卷 1687-1692页

作者： Eric Papain MEZATIO Mohammadmohsen AGHELINEJAD Lionel AMODEO Isabelle FERREIRA University of Technology of Troyes 12 Rue Marie Curie 10300 Troyes Institut français du textile et de l'habillement 270 Rue du Faubourg Croncels 10000 Troyes

The clothing industry accounts for nearly 60% of the global textile industry. It is largely responsible for the consumption of non-renewable resources. According to studies, it is responsible for nearly 10% of greenhouse gas emissions in the world (Cuc and Vidovic (2014)). The aim of this article is to propose a planning approach that integrates two decision levels (strategic and tactical), using a new mixed integer linear programming model (MILP) that integrates carbon emissions in SCMP (Supply Chain Management Planning). This approach must be used in a general way in all SCMP industries for the control of carbon flows and resources. It could be used as a decision support tool to evaluate and monitor the effects of carbon emissions. This tool offer to the industrials the best choices at different stages (selection of suppliers, plants, transportation modes, storage etc.), with lower cost and carbon emissions. For this purpose, the approach is tested on several scenarios, based on severals literature case studies in the textile and clothing sectors. The results show that carbon price uncertainty would significantly influence the economic and environmental management of the supply chain, and the repartition of the manufacturing activity (insource or outsource). Over a range of carbon price scenarios, the results show a reduction over to 36% in carbon emissions in the supply chain.

关键词： Design supply chain textile clothing industries mixed integer linear programming Economic Environmental aspect

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Rebalancing in bike sharing systems: Application to the Lisbon case study

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CASE STUDIES ON TRANSPORT POLICY 2023年 13卷

作者： Chastre, Miguel R. Andrade, Antonio R. Univ Lisbon IDMEC Inst Super Tecn Lisbon Portugal

This work presents a mathematical programming formulation for the static Bike Rebalancing Problem to meet the practical constraints faced by the operators of Bike Sharing Systems. Adaptating a mixed integer linear programming formulation, it now includes different types of vehicles and bicycles, the autonomy of each vehicle, the duration of its route and the stations that require inspection actions. An intermediate station is introduced to temporarily store bicycles, relaxing the restrictions imposed by the vehicles' capacity. After incorporating different acceleration procedures, a Branch-and-Cut scheme is run for 30 min to find optimized routes for the reposition vehicle on five real instances of the Lisbon Bike Sharing System. The computed routes resulted in a 30% to 75% decrease in total length, when compared to the real routes. This decrease is mainly attributed to the integration of all the mentioned factors in one global model, encouraging its future implementation.

关键词： Bike Sharing System Bike Rebalancing Problem Repositioning mixed integer linear programming Branch-and-Cut

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Backup Network Design Against Multiple Link Failures to Avoid Link Capacity Overestimation

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2020年第2期17卷 1254-1267页

作者： Hirano, Yuki He, Fujun Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

This paper proposes a backup network design scheme that can determine backup link capacity in practical time. The proposed scheme suppresses the required backup link capacity while providing a guaranteed level of recovery against multiple independent link failures. The conventional scheme is based on robust optimization and suffers from the problem of overestimating the backup link capacity. The proposed scheme addresses the overestimation problem by computing the probabilistic distribution function of required backup link capacity in polynomial time. We formulate the backup network design problem with the proposed scheme as a mixed integer linear programming problem to minimize the total required backup link capacity. We prove that the decision version of backup network design problem is NP-complete. Given that network size will continue to increase, we introduce a heuristic approach of simulated annealing to solve the same problem. Numerical results show that the proposed scheme requires less total backup link capacity than the conventional scheme based on robust optimization.

关键词： Optimization Probabilistic logic Capacity planning Resource management Routing mixed integer linear programming Distribution functions Backup network design multiple failures link capacity probability distribution function mixed integer linear programming NP-completeness

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Supervised-Learning-Based Optimal Thermal Management in an Electric Vehicle

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IEEE ACCESS 2020年 8卷 1290-1302页

作者： Park, Jonghyun Kim, Youngjin Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea

Due to the increasing market share of electric vehicles (EVs), the optimal thermal management (TM) of batteries has recently received significant attention. Optimal battery temperature control is challenging, requiring a detailed model and numerous parameters of the TM system, which includes fans, pumps, compressors, and heat exchangers. This paper proposes a supervised learning strategy for the optimal operation of the TM system in an EV. Specifically, for TM subsystems, individual artificial neural networks (ANNs) are implemented and trained with data obtained under normal EV driving conditions. The ANNs are then interconnected based on the physical configuration of the TM system. The trained ANNs are replicated using piecewise linear equations, which can be explicitly integrated into an optimization problem for optimal TM scheduling. This approach enables the application of a mixed-integer linear programming solver to the problem, ensuring the optimality of the solution. Simulation case studies are performed for the two operating modes of the TM system: i.e., integrated and separate modes. The case study results demonstrate that the ANN-based model successfully reflects the operating characteristics of the TM system, enabling accurate battery temperature estimation. The proposed optimal TM strategy using the ANN-based model is verified as effective in reducing the total energy consumption, while maintaining the battery temperature within an acceptable range.

关键词： Artificial neural network battery temperature electric vehicles mixed integer linear programming piecewise linear equations supervised learning thermal management

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Risk assessment by security-constrained unit commitment for hybrid wind-thermal by pair copula approach in reserve power market: a stochastic approach

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第26期14卷 6639-6649页

作者： Panda, Rajesh Tiwari, Prashant Kumar NIT Silchar Elect Engn Silchar Assam India Motilal Nehru Natl Inst Technol Allahabad Elect Engn Dept Prayagraj UP India

High volatility renewable penetration increases the security risk of the market participants. In order to ensure the security of the reserve market under uncertainties, the authors have proposed a novel optimal bidding strategy based on bivariate pair copula-based security-constrained unit commitment economic dispatch (SCUCED) for the generation companies (GENCOs) while considering the volatility of wind power generations and load variations under different (N - k) contingencies in ancillary service market. The expected profit of GENCOs are based on the ramp-up and ramp-down optimal reserve curves in each time interval are submitted to the independent system operator. The profit of wind farms is obtained and the risks due to uncertainties with wind farms are also analysed while participating in the reserve market. Conditional value-at-risk is considered for the optimal placement of wind farms in the network. The risk-based SCUCED model is solved by mixed integer linear programming. Moreover, a factor called joint Pareto tail factor has been also developed to provide information regarding the fat tails in the lower end of the bivariate marginal distribution of different GENCOs. The effectiveness of the proposed approach has been tested on a modified IEEE 30 bus system.

关键词： wind power plants power generation economics power markets power generation scheduling linear programming risk management power system security integer programming power generation dispatch thermal power stations hybrid power systems load (electric) profitability stochastic programming risk assessment reserve power market stochastic approach market participants bivariate pair copula-based security-constrained unit commitment economic dispatch wind power generations ancillary service market wind farms conditional value-at-risk optimal placement risk-based SCUCED model optimal bidding strategy profit GENCO independent system operator mixed integer linear programming Pareto tail factor bivariate marginal distribution modified IEEE 30 bus system ramp-down optimal reserve curves ramp-up optimal reserve curves

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A methodology for the design and deployment of distributed cyber-physical systems for smart environments

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 109卷 420-430页

作者： Tanganelli, Giacomo Cassano, Luca Miele, Antonio Vallati, Carlo Univ Pisa Lungarno Pacinotti 43 I-56126 Pisa Italy Politecn Milan Pzza Leonardo Da Vinci 32 I-20133 Milan Italy

The pervasiveness and the growing processing capabilities of mobile and embedded computing systems are leading to a shift from the Internet of Things (IoT) paradigm to the Fog computing scenario where the environment is instrumented with high-performance computing in the proximity to cyber-physical systems. The design of such systems requires an accurate planning, on the one hand, to ensure that specific application requirements will be properly met at run-time, and, on the other hand, to minimize the system's monetary costs. In this paper we present a methodology for an automated design and deployment of distributed cyber-physical systems into smart environments. We propose an engine based on a mixed integer linear programming (MILP) formulation which takes in input a planimetry of the environment and a description of the applications and, based on a repository of available processing boards, identifies the cost-optimized instantiation of the processing architecture and the corresponding distribution of the application functionalities. By comparing our proposal with the existing methodologies that address similar problems we can highlight the following novelties: (i) we address a system architecture composed of heterogeneous devices, (ii) we adopt a realistic model of the environment, and (iii) we perform a joint co-exploration of architecture instantiation and applications mapping. An experimental evaluation, considering a smart office case study, demonstrates the potential of the proposed approach in minimizing the overall system monetary cost around 42% w.r.t. a baseline approach not exploiting planimetry information. Such results have been also confirmed by an extensive experimental campaign using synthetic problems, which also highlighted how the execution times of the optimization process are affordable for the design-time process. (C) 2020 Published by Elsevier B.V.

关键词： Distributed cyber-physical systems Smart environments mixed integer linear programming

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A fast solution approach to solve the generator maintenance scheduling and hydropower production problems simultaneously

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Procedia Computer Science 2022年 207卷 3808-3817页

作者： Geofrey Glangine Sara Seguin Kenjy Demeester Rio Tinto Aluminium 1955 boulevard Mellon Saguenay G7S 4L2 Quebec Canada

The Generator Maintenance Scheduling Problem (GMSP) is a problem that combines a hydropower optimization problem with a scheduling problem. Both problems are known to be hard to solve and combining them leads to an even more challenging mathematical problem. Since the hydropower production functions are nonlinear, hyperplane curve fitting is used to linearize each power production function. The goal of the GMSP is to find an optimal schedule plan to decide when to shut down generators for maintenance. Therefore, one production function needs to be formulated per generator combinations leading to a rather large number of constraints. This paper demonstrates that the complexity of the problems is linked to the number of hyperplanes selected to formulate the power production functions. To accelerate the resolution of the problem, a new heuristic based on the mean square algorithm is presented to reduce the number of hyperplanes required. This heuristic substantially reduces the number of constraints and the solving time is almost ten times faster. Numerical results show that the energy produced and the generated maintenance plannings are similar for both mathematical formulations, more precisely with one hyperplane for each generator combination versus a reduced number of hyperplanes.

关键词： Hydropower scheduling problem maintenance scheduling problem hydropower production functions stochastic programming mixed integer linear programming heuristic

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Towards Balancing Market Integration: Conversion Process for Balancing Energy Offers of Central-Dispatch Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第1期35卷 293-303页

作者： Marneris, Ilias G. Roumkos, Christos G. Biskas, Pandelis N. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

This paper presents a novel methodology for the conversion of balancing energy offers, submitted during the integrated scheduling process (ISP) of European central-dispatch systems, into replacement reserve (RR) standard products. The aim is to allow the integration of the balancing market of central-dispatch systems, where generating unit operating constraints and real-time dispatch lie in the responsibility of the TSO, with the pan-European balancing market mainly characterized by self-dispatch arrangements and portfolio-based market participation. To respect the diversified clearing rules of both systems, we propose a break-down of the one-shot RR clearing problem into four distinct steps: (a) a local RR quantity maximization process, where the maximum quantity of upward and downward RR balancing energy offered by each balancing service provider (BSP) is defined by the TSO subject to detailed unit operating limitations, (b) a local mandatory activation process, driven by binding unit constraints determined in the previous step, (c) a local conversion process, during which the ISP offers are converted into RR standard products, and (d) the purely economic RR clearing to obtain the activated volumes. Four different modeling approaches are constructed, the attained solution of which is compared with the one-shot techno-economic clearing benchmark currently applied in central-dispatch systems. The comparison indicates that the break-down process results coincide with the one-shot benchmark results, achieving identical social welfare and final unit dispatch, while additionally allowing less-costly Europe-wide cross-border activations of RR.

关键词： Balancing energy central dispatch mixed integer linear programming balancing market standard product trans european replacement reserves exchange

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Joint Estimation of Operational Topology and Outages for Unbalanced Power Distribution Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2020年第1期35卷 605-617页

作者： Gandluru, Anandini Poudel, Shiva Dubey, Anamika Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

An electric power distribution system is operated in several distinct radial topologies by opening and closing of system's sectionalizing and tie switches. The estimation of the system's current operational topology is a precursor to implementing any optimal control actions (during normal operation) or restorative actions (during outage condition). This paper presents a mathematical programming approach to estimate the operational topology of a three-phase unbalanced power distribution system under both outage and normal operating conditions. Specifically, a minimum weighted least absolute value estimator is proposed that uses the line flow measurements, historical/forecasted load data, and ping measurements and solves a mixed-integer linear program (MILP) to estimate the operational topology and any outaged sections simultaneously. We thoroughly validate the accuracy of the proposed approach using IEEE 123-bus and a 1069-bus three-phase multi-feeder test system with the help of numerous simulations. It is observed that the approach performs well even at high percentages of measurement errors.

关键词： Topology Network topology Power measurement Current measurement State estimation Q measurement Topology estimation three-phase power distribution system mixed integer linear programming

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Optimization of cleaning strategies for heliostat fields in solar tower plants

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SOLAR ENERGY 2020年 204卷 501-514页

作者： Picotti, Giovanni Moretti, Luca Cholette, Michael E. Binotti, Marco Simonetti, Riccardo Martelli, Emanuele Steinberg, Theodore A. Manzolini, Giampaolo Queensland Univ Technol 2 George St Brisbane Qld 4000 Australia Politecn Milan Dipartimento Energia Via Lambruschini 4A I-20156 Milan Italy

The reduction due to soiling of the optical efficiency of the heliostats in the solar field is a significant detrimental factor in concentrating solar power (CSP) plants. Artificial cleaning is required to maintain acceptable values of optical efficiency, especially in those areas where CSP tends to be economically viable, i.e. where the yearly available DNI is high and rain is scarce. The optimization of the cleaning activities is then a fundamental step to properly balance the operation and maintenance (O&M) costs of the plant with the revenue losses due to soiled heliostats. In this work the best cleaning schedule for a given solar field is computed through a mixed integer linear programming (MILP) model and compared with the results of a heuristic approach. The optical efficiency reduction is assessed for each sector of the solar field through a physical model. The MILP model accounts for the soiling impact and finds the most economical solution in terms of cleaning trucks number and number of cleanings. The optimal cleaning schedule for each sector of the solar field is obtained by minimizing the total cleaning cost (TCC), which is the sum of direct cleaning costs and monetized losses due to soiling. A few test cases are evaluated to demonstrate the strength and the applicability of the developed algorithm. The TCC improvements span between 0.7% and 19.6%, depending on the different scenarios and cost structures considered. For the case studies considered, the savings due to the MILP optimized cleaning strategy were between 927 kAU$/yr and 4744 kAU$/yr (575 k(sic)/yr and 2941 k(sic)/yr).

关键词： Heliostat cleaning optimization Total cleaning cost minimization Solar tower plant mixed integer linear programming Operation and maintenance optimization

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