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Home healthcare integrated staffing and scheduling

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年 95卷 102057-102057页

作者： Restrepo, Maria, I Rousseau, Louis-Martin Vallee, Jonathan CIRRELT Pavillon Andre Aisenstadt Montreal PQ H3T 1J4 Canada Polytech Montreal Dept Math & Genie Ind Montreal PQ H3C 3A7 Canada AlayaCare 4200 St Laurent Blvd Suite 800 Montreal PQ H2W 2R2 Canada

Workforce planning for home healthcare represents an important and challenging task involving complex factors associated with labor regulations, caregivers' preferences, and demand uncertainties. This task is done manually by most home care agencies, resulting in long planning times and suboptimal decisions that usually fail to meet the health needs of the population, to minimize operating costs, and to retain current caregivers. Motivated by these challenges, we present a two-stage stochastic programming model for employee staffing and scheduling in home healthcare. In this model, first-stage decisions correspond to the staffing and scheduling of caregivers in geographic districts. Second-stage decisions are related to the temporary reallocation of caregivers to neighboring districts, to contact caregivers to work on a day-off, and to allow under- and over-covering of demand. The proposed model is tested on real-world instances, where we evaluate the impact on costs, caregiver utilization, and service level by using different recourse actions. Results show that when compared with a deterministic model, the two-stage stochastic model leads to significant cost savings as staff dimensioning and scheduling decisions are more robust to accommodate changes in demand. Moreover, these results suggest that flexibility in terms of use of recourse actions is highly valuable as it helps to further improve costs, service level, and caregiver utilization. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Staffing and scheduling Home healthcare two-stage stochastic programming Context-free grammars

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Sustainable cold supply chain management under demand uncertainty and carbon tax regulation

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2020年 80卷 102245-102245页

作者： Babagolzadeh, Mahla Shrestha, Anup Abbasi, Babak Zhang, Yahua Woodhead, Alice Zhang, Anming Univ Southern Queensland Sch Commerce Toowoomba Qld Australia Univ Southern Queensland Sch Management & Enterprise Toowoomba Qld Australia RMIT Univ Sch Business IT & Logist Melbourne Vic Australia Univ Southern Queensland Rural Econ Ctr Excellence RECoE Toowoomba Qld Australia Univ British Columbia Sauder Sch Business Vancouver BC Canada

Increasing awareness of sustainability in supply chain management has prompted organizations and individuals to consider environmental impacts when managing supply chains. The issues concerning environmental impacts are significant in cold supply chains due to substantial carbon emissions from storage and distribution of temperature-sensitive product. This paper investigates the impact of carbon emissions arising from storage and transportation in the cold supply chain in the presence of carbon tax regulation, and under uncertain demand. A two-stage stochastic programming model is developed to determine optimal replenishment policies and transportation schedules to minimize both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The performance and robustness of the matheuristic algorithm are analyzed using test instances in various sizes. A real-world case study in Queensland, Australia is used to demonstrate the application of the model. The results highlight that higher emissions price does not always contribute to the efficiency of the cold supply chain system. Furthermore, the analyses indicate that using heterogeneous fleet including light duty and medium duty vehicles can lead to further cost saving and emissions reduction.

关键词： Sustainable cold supply chain two-stage stochastic programming Carbon tax regulations Demand uncertainty Matheuristic algorithm

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Economic scheduling of a smart microgrid utilizing the benefits of plug-in electric vehicles contracts with a comprehensive model of information-gap decision theory

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JOURNAL OF ENERGY STORAGE 2020年 32卷

作者： Sriyakul, Thanaporn Jermsittiparsert, Kittisak Mahanakorn Univ Technol Fac Business Adm Bangkok 10530 Thailand Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam Henan Univ Econ & Law MBA Sch Changsha 450046 Hunan Peoples R China

In this paper, a hybrid optimization approach is proposed to smart microgrid (SMG) operators to schedule the existing energy resources optimally to meet the load demand of the system. The proposed hybrid optimization is structured based on information-gap decision theory (IGDT) and two-stage stochastic programming to minimize the operational cost of the system in day-ahead (DA) and real-time (RT) power markets. The uncertainty of the day-ahead market price is tackled with the IGDT. The IGDT is furnished with robustness and opportunity functions leading to various risk-averse and risk-taker decision-making strategies. The problem is structured as bi-level programming, while by implementing the concept of envelop bounds, it is transferred into a single-level optimization problem. On the other hand, the uncertainties of the operation of the real-time market are modeled using scenarios with stochastic programming. The studied SMG is equipped with a photovoltaic system, a wind turbine, two microturbines (MTs), and battery storage. The results determine the scheduling of day-ahead exchange power and scheduled PEV contracts as here-and-now decisions of the optimization. However, the traded energy in the real-time market, MTs' dispatch, and the charging/discharging plan of battery storage are wait-and-see decisions made by the second stage of the programming. The results are obtained and discussed from the perspective of risk-neutral, risk-averse, and risk-taker decision-makers. As an example of performance, when the uncertainty horizon is increased by 10% in the robustness mode, the operation cost is increased by about 27.5%. In comparison, the same amount of increment of uncertainty horizon leads to cost-saving about 33.3% addressing the positive aspect of uncertainty using opportunity function.

关键词： Smart microgrid Plug-in electric vehicle Information gap decision theory two-stage stochastic programming Hybrid uncertainty management

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A decomposition approach based on meta-heuristics and exact methods for solving a two-stage stochastic biofuel hub-and-spoke network problem

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JOURNAL OF CLEANER PRODUCTION 2020年第0期247卷 119176-000页

作者： Aboytes-Ojeda, Mario Castillo-Villar, Krystel K. Roni, Mohammad S. Univ Texas San Antonio Texas Sustainable Energy Res Inst One UTSA Circle San Antonio TX 78249 USA Idaho Natl Lab Software Engn & Data Analyt 2525 Fremont Ave Idaho Falls ID USA

An alternative to the production of fossil transportation fuels is the production of biofuels, specifically, bioethanol. One of the main opportunity areas is the reduction in the overall cost of biofuel. An approach to reduce this cost is to design and implement a supply chain (SC) that considers the quality-related properties of the biomass as well as the economies of scale to minimize the logistics and quality-related costs. This problem is formulated as a hub location problem, which has been classified as an NP-hard problem, therefore, meta-heuristics are a suitable approach to solve this problem. We propose a hybrid meta-heuristic solution procedure to solve large-scale instances of a two-stage stochastic biomass-to-biorefinery hub-and-spoke network problem. This solution procedure is proposed to support the large-scale production and distribution of bioethanol by considering the variability in its moisture and ash contents. The hybrid method utilizes a simulated annealing-simplex method to find an initial solution and a tabu search-simplex method to improve the solution. Numerical experimentation was performed on a realistic case study in Texas. The results show that the hybrid procedure outperforms the standard L-shaped (LS) method. The meta-heuristic combining simulated annealing and tabu search with the simplex method (SATS-SM) achieved 2.48% lower costs and required 96.57% less time, on average, when compared to the results from using the LS. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： two-stage stochastic programming Hybrid meta-heuristics Biofuels Biomass Sustainable supply chain

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Robust master planning of a socially responsible supply chain under fuzzy-stochastic uncertainty (A case study of clothing industry)

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 94卷 103715-103715页

作者： Yaghin, R. Ghasemy Sarlak, P. Ghareaghaji, A. A. Amirkabir Univ Technol Dept Text Engn Clothing Engn & Management Grp Tehran Iran

This paper addresses an integrated physical supply channel, aggregate production, and transportation planning problem through the lens of social sustainability under uncertainty. The three-echelon supply chain network includes multiple suppliers, a manufacturer and multiple distribution centres. Social-related criteria such as workers' job-security and health (i.e. working conditions) and social investment are contemplated in the supply chain under consideration. In doing so, a chance constraint mixed integer non-linear programming model is built to determine the centralized planning of procurement-production-transportation under fuzzy-stochastic uncertainty over the tactical planning horizon. Moreover, an efficient hybrid solution procedure is developed utilizing convexification and defuzzification strategies. Finally, a real-case study in clothing industry is presented to show the model applicability and the solution procedure efficiency.

关键词： Physical supply channel plan Integrated transportation and production Mathematical optimization Social sustainability Textile industry two-stage stochastic programming

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Time-Coordinated Multienergy Management of Smart Buildings Under Uncertainties

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第8期15卷 4788-4798页

作者： Sharma, Sumedha Xu, Yan Verma, Ashu Panigrahi, Bijaya Ketan Indian Inst Technol Delhi Ctr Energy Studies New Delhi 110016 India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

This paper proposes a multitimescale coordinated building energy management system (BEMS) for multienergy buildings integrated with renewable energy sources (RES). It aims to dispatch active building components in two different timescales to counteract uncertain variations in RES generation and load. In the longer timescale (hourly), fuel-cell-based microcombined heat and power and energy storage system (ESS) are dispatched before uncertainty is realized. In the 15 min timescale, ESS is redispatched to supplement the first stage decision, once the uncertainty is realized. The multitimescale coordination is achieved through a two-stage stochastic programming model. The BEMS has been developed as nonlinear receding horizon and nonlinear quadratic programming models in the first and second stages, respectively. In order to minimize carbon footprint of the building, carbon tax has been incorporated in the system operation cost. Furthermore, to prolong battery lifetime in uncertain environment, battery degradation cost has been included. Case studies depict appropriateness of the proposed method in achieving lower carbon emissions while simultaneously improving battery performance in comparison to traditional systems. Extensive simulation results demonstrate robustness and effectiveness of the proposed scheme to account for uncertainties in generation and load.

关键词： Building energy management energy storage systems (ESSs) microcombined heat and power (mCHP) receding horizon renewable energy sources (RES) two-stage stochastic programming

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End-of-life vehicles allocation management under multiple uncertainties: An interval-parameter two-stage stochastic full-infinite programming approach

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RESOURCES CONSERVATION AND RECYCLING 2016年第0期114卷 1-17页

作者： Simic, Vladimir Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11000 Serbia

One of the main negative consequences of uncontrolled export of used vehicles from the European Union to developing countries is resource shortage for major players of European vehicle recycling systems. The resource scarcity puts serious pressure on vehicle recycling managers. An effective end-of-life vehicles (ELVs) allocation management is considered vital for mitigating the effect of the growing export of used vehicles. This paper proposes an interval-parameter two-stage stochastic full-infinite programming model for end-of-life vehicles allocation management under multiple uncertainties. A case study is conducted in order to demonstrate the potentials and applicability of the proposed model. Influences of parameter uncertainty on model solutions are thoroughly investigated. The developed model can efficiently handle uncertainties expressed as functional intervals, probability distributions and conventional crisp intervals. It is able to reduce risk of ELV management system failure due to the possible constraints violation. The formulated model can take into account connections of modeling parameters and their impact factors, thus reflecting external uncertainties of ELV management systems. It can provide a flexible ELV allocation management schemes adjustable with the variations in prices of secondary metals and end-of-life vehicles. The proposed model is able to reflect trade-off between conflicting waste management system revenues and the associated penalties for violating ELV allocation targets, thus providing a valuable insight for decision makers. (C) 2016 Elsevier B.V. All rights reserved.

关键词： End-of-life vehicle Full-infinite programming two-stage stochastic programming Interval programming Uncertainty

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An Incentive-Based Settlement Mechanism for Participation of Flexible Demands in Day-ahead Markets 2

An Incentive-Based Settlement Mechanism for Participation of...

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2nd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Zalzar, Shaghayegh Bompard, Ettore Francesco Politecn Torino Dept Energy Turin Italy

ISBN: (纸本)9781728111568

The recent energy policy trends towards expanding the penetration of variable renewable energy sources in electricity generation sector brings about new short/long term challenges for operation/planning of power systems. Due to the significant variability and uncertainty of electricity production by these resources, system operators may require additional flexibility resources to continuously balance supply and demand and to preserve power system security under high penetration of renewables. In this paper, the participation of flexible demands in day-ahead market under high share of renewables (in particular wind generation) is modelled through a two-stage stochastic program which takes into account the day-ahead prediction error of wind production. Demand-side flexibility is modelled as load shifting capability, without affecting the total electricity demand per day. An incentive payment mechanism to settle the flexible demands in the day-ahead market is proposed which provides additional revenue for consumers and motivates them to provide flexibility. The incentive rate is set by the system operator, while the accepted consumers to receive this incentive are selected through the market mechanism, according to the additional value of the provided flexibility compared to the cost of providing the incentives. Finally, the maximum incentive which can be paid to flexibility providers under different levels of demand-side flexibility is investigated on the conceptual test case of this study.

关键词： demand-response flexibility variable renewable energy sources two-stage stochastic programming incentive

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A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed,China

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Journal of Arid Land 2012年第4期4卷 390-398页

作者： Yue HUANG Xi CHEN YongPing LI AnMing BAO YongGang MA College of Urban and Environmental Sciences Peking University Beijing 100871 China StateKey Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China MOE Key Laboratory of Regional Energy Systems Optimization Sino-Canada Resources and Environmental ResearchAcademy North China Electric Power University Beijing 102206 China Xinjiang Remote Sensing Center Urumqi 830011 China

This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.

关键词： optimization two-stage stochastic programming uncertainty water resources management hydrological model Kaidu-Konqi watershed Tarim River Basin

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Sustainable location routing problem with customers and suppliers matching under stochastic demands

Sustainable location routing problem with customers and supp...

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International Conference on Industrial Engineering and Systems Management (IESM)

作者： Liu, Ming Liu, Xin Tongji Univ Sch Econ & Management Shanghai Peoples R China

ISBN: (纸本)9781728115665

Capacitated location routing problem (CLRP) considers the plant location problem of tactical level and the vehicle routing problem of operational level simultaneously. Most existing works on CLRP investigate the deterministic problem settings, while it is usually hard to exactly estimate the customer demands due to some factors. This paper studies a sustainable CLRP with customers and suppliers matching under stochastic demands, where each vehicle starts from a plant and distribute product to customers and load material at a supplier then drive back to the original plant. The problem is to determine the candidate locations to open and the route of each vehicle, in order to minimize the weighted sum of construction cost and the expectation of fuel consumption. For the problem, a two-stage stochastic programming formulation is first proposed, and then a sample average approximation (SAA) method is developed. A case study shows the applicability of the proposed solution method.

关键词： Location routing problem customers and suppliers matching energy consumption two-stage stochastic programming

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