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27th Asia and South Pacific Design Automation Conference (ASP-DAC)

作者： Cheng, Chung-Kuan Ho, Chia-Tung Holtz, Chester Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92037 USA

ISBN: (纸本)9781665421355

Packaging has become a crucial process due to the paradigm shift of More than Moore. Addressing manufacturing and yield issues is a significant challenge for modern layout algorithms. We propose to use printed circuit board (PCB) placement as a benchmark for the packaging problem. A maximum-margin formulation is devised to improve the separation between nets. Our framework includes seed layout proposals, a coordinate descent-based procedure to optimize routability, and a mixed-integer linear programming method to legalize the layout. We perform an extensive study with 14 PCB designs and an open-source router. We show that the placements produced by NSplace improve routed wirelength by up to 25%, reduce the number of vias by up to 50%, and reduce the number of DRVs by 79% compared to manual and wirelength-minimal placements.

关键词： Design automation Layout Printed circuits Manuals Packaging Benchmark testing mixed integer linear programming

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Stochastic simulations of the optimal control of a stand alone microgrid at the scale of two houses 3

Stochastic simulations of the optimal control of a stand alo...

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3rd International Conference on Smart Grid and Renewable Energy (SGRE)

作者： Rasoavonjy, Paulisimone Fanjirindratovo, Tovondahiniriko Chau, Oanh Ramiarinjanahary, Olga Dotti, Sylvain Univ Reunion Isl PIMENT Lab St Denis France Univ Toliara Phys & Environm Lab Toliara Madagascar Univ Reunion Isl CEMOI Lab St Denis France

ISBN: (纸本)9781665479080

In off-grid areas, successful use of intermittent renewable energy sources requires optimal management of power consumption. Our experimental site is the "Cirque de Mafate" on Reunion Island. Our laboratory has developed a mixed integer linear programming model which minimizes the electricity consumption of a cluster of houses. This model is deterministic. Our study focuses on the stochastic part, it aims to model, optimize and simulate the stochastic operation of an autonomous microgrid by mutualizing production and storage resources. A study for the solar resource forecasting is performed, using nonparametric methods for the estimation of probability density functions. Indeed, the prediction of the intermittent resource and the combination of production sources are the keys to the good functioning of a microgrid in autonomous mode. Three neighbouring houses are concerned. The experimentation at the scale of a house has already been performed and the results proved the performance of the system under random constraints. This paper presents the results of an experimentation for two houses. One of the strategies adopted is to aim for auto-consumption for three days if the solar forecast is pessimistic, a part of the energy is then reserved at the battery level for the following two days. The results allow to assess the performance of the system in front of random constraints and to make decisions.

关键词： mixed integer linear programming self-consumption smart grid stochastic modeling

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A Nurse Scheduling Case in a Turkish Hospital

A Nurse Scheduling Case in a Turkish Hospital

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21st International Symposium on Production Research (ISPR) - Digitizing Production System

作者： Yasan, Edanur Cesur, Tugba Aslan, Tuba Nur Kose, Rana Ezgi Konyalioglu, Aziz Kemal Beldek, Tugce Cebi, Ferhan Istanbul Tech Univ Management Engn Istanbul Turkey

ISBN: (纸本)9783030904210;9783030904203

Today, each of the hospitals provides services all day. For this reason, there is a direct relationship between the psychological and physical well-being of the nurses working in the hospital and the quality of the service they provide. In order to provide the best service, the shifts of nurses should be balanced and fair. In this study, a mathematical model is developed for the nurses working in the private hospital in Yalova to be assigned to the two shifts determined as equally as possible. In the solution of the model, mixed integer linear programming method and GAMS Optimization program are used.

关键词： Nurse scheduling Shift scheduling mixed integer linear programming

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Energy-Aware Blocking Hybrid Flow Shop Scheduling Problem with Sequence-depend Setup Times and Machine Speed Levels

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Procedia Computer Science 2025年 253卷 1134-1143页

作者： Ahmed Missaoui Cemalettin Ozturk Barry O’Sullivan Insight Centre for Data Analytics University College Cork Ireland Process Energy and Transport Engineering Munster Technological University Cork Ireland

The industry sector has the second largest energy demand after electricity generation. Given the high trend of energy prices, scarcity of its supply due to political instability and dependency on fossil fuels for its production makes energy the primary challenge for the manufacturing sector to stay competitive. Therefore, in addition to novel equipment technologies that use less energy, energy-efficient scheduling has also been a priority for manufacturing companies. In this study, the Hybrid Flowshop Scheduling Problem with Blocking Constraints and Sequence-Depend Setup Times (BHFS-SDST) is investigated for the minimization of makespan and total energy consumption (TEC). First, a novel bi-objective mixed integer linear programming (MILP) model is formulated and solved through augmented epsilon constraints. Then, a novel multi-objective approach based on Iterated Greedy meta-heuristic is developed for larger instances. Efficiency and the scalability of the proposed approaches are tested with small, medium, and large instances. Computational experiments show the effectiveness of developed methods in solving the BHFS-SDST problem.

关键词： Hybrid flowshop blocking constraint Sequence-depend setup times Makespan Energy consumption mixed integer linear programming Iterated greedy

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Vehicle routing and scheduling problem with order acceptance for pharmaceutical refrigerated logistics

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APPLIED SOFT COMPUTING 2024年 164卷

作者： Lee, Seung Jae Kim, Byung Soo Incheon Natl Univ Dept Ind & Management Engn 119 Acad Ro Incheon 22012 South Korea

This paper studies a delivery problem in a pharmaceutical cold chain. In this problem, each customer orders multiple pharmaceutical products and requires a due date for each order. Each product has sensitive characteristics of storage temperature range and shelf life. A refrigerated vehicle routing and scheduling problem with order acceptance is formulated as a mixed-integer-linear-programming model. In the model, the acceptance of each pharmaceutical order and the route and schedule of a limited number of refrigerated vehicles is simultaneously determined to maximize the total profit. A hybrid genetic algorithm embedding an insertion heuristic with modification operation is proposed. The performance of the proposed algorithm is evaluated and verified by comparing other metaheuristics. Managerial insights about various experimental environments for the order acceptance are suggested by conducting a sensitivity analysis.

关键词： Vehicle routing Scheduling Pharmaceutical cold chain mixed integer linear programming Genetic algorithm

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Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

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COMPUTERS & OPERATIONS RESEARCH 2024年 167卷

作者： Sugianto, Willy Chandra Kim, Byung Soo Incheon Natl Univ Dept Ind & Management Engn 119 Acad Ro Incheon 22012 South Korea

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence- dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

关键词： Scheduling Additive manufacturing Multi-trip vehicle routing problem mixed integer linear programming Variable neighborhood search Heuristic

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An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons

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RENEWABLE ENERGY 2024年 228卷

作者： La Fata, Alice Brignone, Massimo Procopio, Renato Bracco, Stefano Delfino, Federico Barbero, Giulia Barilli, Riccardo Univ Genoa Elect Elect & Telecommun Engn & Naval Architecture Genoa Italy Renantis Milan Italy

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/ plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched;at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the

关键词： Energy Management System Electricity Market Ancillary Services Renewable Energy Sources mixed integer linear programming

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A bi-objective optimization approach for carbon capture and storage supply chain network combining with pricing policies: Economic and social aspects

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JOURNAL OF CLEANER PRODUCTION 2024年 434卷

作者： Derakhti, Arman Gonzalez, Ernesto D. R. Santibanez Univ Talca Fac Engn Curico Chile Univ Talca Ind Engn Dept Niches Km 1 Curico Chile Univ Talca Fac Engn CES4 0 Niches Km 1 Curico Chile

Carbon capture and storage (CCS) technology and pricing policies are two mitigation strategies that attempt to tackle the carbon emission problem. Nonetheless, the uncertainty of public reaction to CCS infrastructure installation has been noted as a significant issue that may lead to either cancelations, delays, or influence the deployment cost of CCS infrastructure. Although these strategies are solutions to reduce carbon emissions, the entire supply chain itself needs to align with sustainability policy. While some previous studies have addressed mitigation strategies or the combination of social objective and CCS technology, the impact of social objectives on reservoir openness has not been explored. This article contributes to the literature by illustrating the effect of social objectives on reservoir openness and the total cost of the CCS supply chain. Through the development of a two-stage mixed-integer linear programming model, we optimize the supply chain network design. In the first stage, a single-objective model minimizes capture, transportation, and sequestration costs along with CO2 emission costs to the atmosphere, considering various carbon tax prices. In the second stage, a bi-objective model maximizes social acceptance towards CO2 injection wells (reservoirs) to analyze the impact of cultural dimensions. Our findings reveal that maximizing the social objective leads to increased costs compared to the economic optimum, and it significantly influences the openness of reservoirs based on cultural dimensions. Furthermore, we demonstrate that an increase in the CO2 tax enhances the amount of captured carbon and the complexity of the pipeline network in the supply chain. This insight has implications for governments and decision-makers who can encourage industries to adopt CCS technology by setting appropriate carbon tax prices. To validate the model, we apply it to a case study in the European Union cement industry, one of the major carbon emitters among

关键词： Carbon capture and storage mixed integer linear programming Supply chain Social acceptance Carbon tax policy

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On the optimized design of next-generation wind farms

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期291卷 862-870页

作者： Fischetti, Martina Vattenfall BA Wind Havneholmen 29 DK-1561 Copenhagen Denmark

A transformation from fossil fuels to renewable energy has been ongoing in recent years, driven by growing environmental and sustainability demands from customers and society. An increased use of renewable sources can, in particular, help during climate change, which is a very sensitive topic right now. Because demand for green energy is increasing, while competition is also growing, innovation and optimization are of key importance in this business. In particular, because the offshore wind energy market is based on an auction system: the company that can construct and operate the farm with the lowest subsidies will win the auction and capitalize on its investment. It is therefore extremely important to both minimize costs and increase profits at the design phase of new farms. In this paper we will illustrate how Operational Research techniques can help companies be more competitive on the market. To be specific, we address two of the main design challenges arising in the design of new offshore wind farms, the optimal allocation of wind turbines (to minimize interference between them and position-related costs) and their electrical interconnection. We show that a synergic use of mixed integer programming models and heuristic methods in the so-called matheuristic framework can be used to solve both problems efficiently. The optimization tools that we will describe have been used also for the design of Hollande Kust Zuid, the first offshore wind farm in the world to be constructed subsidy-free. We report results on a number of real instances, showing the impact of these techniques in decreasing costs and increasing profitability, with average gains of more than (sic) 10 M for each wind farm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization OR in energy OR in environment and climate change Wind farm design mixed integer linear programming

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Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第4期36卷 3701-3714页

作者： Zhao, Changfei Wan, Can Song, Yonghua Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Univ Macau State Key Lab Internet Things Smart City Taipa Macau Peoples R China

Adequate reserves are urgently needed to hedge against wind power forecasting uncertainties in power systems. Traditional reserve quantification sequentially acquires statistical features of wind power and then determines reserve amounts. This paper establishes a novel integrated probabilistic forecasting and decision (IPFD) methodology to simultaneously optimize the wind power prediction intervals (PIs) and probabilistic reserve quantification. Upward and downward reserve quantities are defined to cover the wind power forecasting uncertainties within the PIs. A cost function evaluating the reserve provision payment and deficit penalty is elaborated to realize cost-benefit trade-offs of reserve decision. Nonparametric wind power PIs are constructed based on extreme learning machine, which minimizes the reserve cost function subject to eligible target coverage probability constraint. The confidence level and quantile proportions associated with wind power PIs can be jointly tuned to reduce the operational cost of reserves. Benefited from extreme learning machine, the IPFD model is reformulated as a mixed integer linear programming problem. A feasible region tightening strategy that shrinks the large constant coefficients and eliminates the redundant binary variables is proposed to accelerate model training. Numerical experiments based on actual wind power data demonstrate the remarkable cost-effective advantages of the IPFD based reserve quantification, as well as the high computational efficiency for online application.

关键词： Wind power generation Forecasting Probabilistic logic Uncertainty Stochastic processes Wind forecasting Upper bound Operating reserve prediction interval probabilistic forecasting wind power machine learning mixed integer linear programming

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