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1 |
A Study on the Optimal Inventory Allocation for Clinical Trial Supply Chains
یک مطالعه در مورد اختصاص مطلوب موجودی برای زنجیره های تأمین کارآزمایی بالینی-2021 With increasing competition in the pharmaceutical industry, pharmaceutical companies
pay more attention to improving the efficiency of clinical trial supply chains to reduce
the drug supplying cost, which takes up a considerable part of the total research and development expense. To improve the efficiency of clinical trial supply chains, this study
investigates the inventory levels of clinical drugs in each period at a distribution center
and clinics considering the reverse replenishment, transshipment, and generalized stockout cost. The inventory allocation problem in clinical trial supply chains is formulated as a
rolling horizon-based two-stage stochastic mixed-integer model where the minimal operational cost constitutes the underage cost at the production planning level of clinical trial
supply chains. An algorithm extending Benders decomposition is proposed as the solution
approach. We also derive several structural results and develop the reformulation method
and row generation strategy to improve the efficiency of the optimization process. The
effectiveness of our approach is demonstrated in the numerical experiment. Keywords: Supply chain management | Clinical trials | Inventory allocation | Stochastic programming |
مقاله انگلیسی |
2 |
A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains
یک مدل برنامه ریزی ریاضی احتمالی برای کنترل جریان کالاهای امدادی در زنجیره های تأمین بشردوستانه-2021 In emergency situations, disaster relief organizations are faced with the difficult decision of how to allocate scarce resources in an efficient manner in order to provide the best possible relief action. This paper aims to provide an analytical model that will help relief organizations in reducing human suffering following a disaster while maintaining an acceptable level of cost efficiency. A mathematical model is introduced to optimize the relief distribution problem which considers the social cost —the total sum of logistics and deprivation costs. The fuzzy nature of the deprivation cost function is addressed with possibilistic mixed integer programming with fuzzy objectives to reflect variation in deprivation costs perceptions. The model is solved using the Rolling Horizon method in a sequence of iterations. In each iteration, part of the planning horizon is modeled in detail and the rest of the time horizon is represented in an aggregated manner. The model is tested both empirically and on a case study of internal displacement in northwest Syria. Computational results showed that considering the demographic structure in affected areas and reflecting it to the deprivation cost function helped to reach better prioritization in distribution of commodities. The rolling horizon methodology is also found to be efficient in solving large scale instances and in capturing the dynamic changes in demand and supply parameters. Keywords: Humanitarian logistics | Possibilistic linear programming | Rolling horizon | Deprivation cost | Inventory allocation |
مقاله انگلیسی |
3 |
Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation
سیستم مدیریت انرژی برای ریز شبکه هیبریدی PV-باد باتری با استفاده از برنامه نویسی محدب ، مدل پیش بینی و کنترل پیش بینی افق نورد با اعتبارسنجی آزمایشی-2020 The integration of energy storage technologies with renewable energy systems can significantly reduce the
operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management
system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption
of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined
cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to
solve the optimization problem and to determine the reference values for the power that should be drawn by the
MG from the main grid using a 15 min sample time. The reference values are then fed to a lower control layer,
which uses a 1 min sample time, to determine the settings for the BESS which then ensures that the MG accurately
follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model
Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented
to demonstrate the capability of the proposed EMS. Keywords: Microgrid Energy Management | Battery Energy Storage System | Real-Time Battery Control | Convex Optimization | Model Predictive Control | Rolling Horizon Predictive Controller | Adaptive Autoregression Algorithm |
مقاله انگلیسی |
4 |
Continuous control with Stacked Deep Dynamic Recurrent Reinforcement Learning for portfolio optimization
کنترل مداوم با یادگیری تقویتی مجدد پویا عمیق انباشته برای بهینه سازی نمونه کارها-2020 Recurrent reinforcement learning (RRL) techniques have been used to optimize asset trading systems and have achieved outstanding results. However, the majority of the previous work has been dedicated to sys- tems with discrete action spaces. To address the challenge of continuous action and multi-dimensional state spaces, we propose the so called Stacked Deep Dynamic Recurrent Reinforcement Learning (SDDRRL) architecture to construct a real-time optimal portfolio. The algorithm captures the up-to-date market con- ditions and rebalances the portfolio accordingly. Under this general vision, Sharpe ratio, which is one of the most widely accepted measures of risk-adjusted returns, has been used as a performance metric. Ad- ditionally, the performance of most machine learning algorithms highly depends on their hyperparameter settings. Therefore, we equipped SDDRRL with the ability to find the best possible architecture topology using an automated Gaussian Process ( GP ) with Expected Improvement ( EI ) as an acquisition function. This allows us to select the best architectures that maximizes the total return while respecting the car- dinality constraints. Finally, our system was trained and tested in an online manner for 20 successive rounds with data for ten selected stocks from different sectors of the S&P 500 from January 1st, 2013 to July 31st, 2017. The experiments reveal that the proposed SDDRRL achieves superior performance com- pared to three benchmarks: the rolling horizon Mean-Variance Optimization (MVO) model, the rolling horizon risk parity model, and the uniform buy-and-hold (UBAH) index. Keywords: Reinforcement learning | Policy gradient | Deep learning | Sequential model-based optimization | Financial time series | Portfolio management | Trading systems |
مقاله انگلیسی |
5 |
Reinforcement learning framework for freight demand forecasting to support operational planning decisions
چارچوب یادگیری تقویتی پیش بینی تقاضای حمل بار برای پشتیبانی از تصمیمات برنامه ریزی عملیاتی-2020 Freight forecasting is essential for managing, planning operating and optimizing the use of resources.
Multiple market factors contribute to the highly variable nature of freight flows, which
calls for adaptive and responsive forecasting models. This paper presents a demand forecasting
methodology that supports freight operation planning over short to long term horizons. The
method combines time series models and machine learning algorithms in a Reinforcement
Learning framework applied over a rolling horizon. The objective is to develop an efficient
method that reduces the prediction error by taking full advantage of the traditional time series
models and machine learning models. In a case study applied to container shipment data for a US
intermodal company, the approach succeeded in reducing the forecast error margin. It also allowed
predictions to closely follow recent trends and fluctuations in the market while minimizing
the need for user intervention. The results indicate that the proposed approach is an effective
method to predict freight demand. In addition to clustering and Reinforcement Learning, a
method for converting monthly forecasts to long-term weekly forecasts was developed and tested.
The results suggest that these monthly-to-weekly long-term forecasts outperform the direct long
term forecasts generated through typical time series approaches. Keywords: Freight demand forecasting | Time series | Reinforcement learning | Rolling horizon |
مقاله انگلیسی |
6 |
Reducing the computational burden of a microgrid energy management system
کاهش بار محاسباتی یک سیستم مدیریت انرژی ریز شبکه-2020 As renewable technology advances and decreases in cost, microgrids are becoming an appealing means of distributed
generation both for isolated communities and integrated with existing electrical grid systems. Due to
their small size, however, microgrids may have financial limitations which preclude them from using commercial
software to optimize control of their assets. Open-source optimization solvers are a viable alternative,
but increase computation time. This work expands on a rolling horizon optimization framework for economic
dispatch within an existing residential microgrid located in Hoover, Alabama. The microgrid has an open-source
solver requirement and a need for quick solution time on a rolling horizon as opposed to a day-ahead commitment.
We present a method of reducing integer variables by relaxation which completes two goals: reduction
in computation time for real-time operations, and reduction in daily operational cost for the microgrid. Seasonal
data for load and photovoltaic (PV) power was also collected from the microgrid to facilitate simulation testing.
Computation time was successfully reduced using multiple variations of the relaxation method, while obtaining
solution quality with operational cost similar to or better than the original model. Keywords: Microgrids | Mixed-integer programming | Energy management system | Rolling horizon |
مقاله انگلیسی |
7 |
Aggregate planning with Flexibility Requirements Profile
برنامه ریزی تجمعی با ملزومات انعطاف پذیری-2018 Demand uncertainty can cause frequent changes in production plans, which create nervousness in manufacturing companies. Traditional methods used for stabilizing production plans do not provide the adequate flexibility in production plans to handle the random demand. Flexibility Requirements Profile (FRP) is an alternative stabilizing approach, where flexible bounds are enforced on production plans in order to maintain a desired degree of flexibility. In this study, we incorporate FRP into conventional aggregate planning, which is formulated as a mixed-integer linear program with additional constraints to reflect the FRP requirements. To ascertain the effectiveness of the proposed method, several structural results are presented along with a comprehensive numerical study using a design of experiments framework with examples from automotive and textile industries. Based on production costs and production plan stability, the effectiveness of FRP-based aggregate planning is compared to traditional aggregate planning without FRP as well as to FRP planning without optimization. The results show that aggregate planning with FRP can consistently identify more stable production plans without significantly sacrificing the cost objective.
keywords: Production |Planning stability |Nervousness |Rolling horizon planning |Flexibility bounds |
مقاله انگلیسی |
8 |
Impact of shelf life on the trade-off between economic and environmental objectives: A dairy case
تاثیر عمر طاقچه ای روی سبک و سنگین کردن بین اهداف اقتصادی و محیطی: یک مورد لبنیاتی-2018 Food manufacturers introduce more environmentally friendly processes to account for increasing sustainability concerns. However, these processes often go along with a reduction of product shelf life, limiting the delay of sales to future periods with higher prices. We develop a framework to analyze the impact of shelf life on the trade-off between economic and environmental performance of two types of dairy products. Since the differences in shelf life have their key impact at the tactical planning level, we develop an optimization model for this aggregation level. Its objectives reflect profit and relevant environmental indicators. A rolling horizon scheme is used to deal with price uncertainty, using Eurex futures as price predictors. Our framework uses these tactical planning results for strategic decisions on product and process selection. A real-life case study contrasts traditional milk powders against novel milk concentrates. Concentrates require less energy in processing, but have a shorter shelf life. Results show that powders offer a potential profit benefit of up to 34.5%. However, this economic value of shelf life is subject to a priori perfect price knowledge. If futures are used as price predictors, the value of shelf life is reduced to only 1.1%. The economic value of shelf life is therefore not a strong argument against the substitution of powders with more environmentally friendly concentrates. We also show that two objectives, profit and eutrophication potential, are sufficient to capture trade-offs in the case. Several product mixes are determined that omit powders and perform well with regard to profit and environment.
keywords: Perishability |Sustainability |Multi-objective optimization |Objective reduction |Dairy industry |
مقاله انگلیسی |
9 |
Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty
شناسایی دقیق الگوهای جریان ترافیکی هواپیما در مناطق ترمینال Metroplex تحت عدم اطمینان تقاضا-2017 Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or
more airports. Due to the spatial proximity and operational interdependency of the air
ports, Metroplex airspaces are characterized by high complexity, and current system struc
tures fail to provide satisfactory utilization of the available airspace resources. In order to
support system-level design and management towards increased operational efficiency in
such systems, an accurate depiction of major demand patterns is a prerequisite. This paper
proposes a framework for the robust identification of significant air traffic flow patterns in
Metroplex systems, which is aligned with the dynamic route service policy for the effective
management of Metroplex operations. We first characterize deterministic demand through
a spatio-temporal clustering algorithm that takes into account changes in the traffic flows
over the planning horizon. Then, in order to handle uncertainties in the demand, a
Distributionally Robust Optimization (DRO) approach is proposed, which takes into
account demand variations and prediction errors in a robust way to ensure the reliability
of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one
day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is
applied to Time Based Flow Management (TBFM) data from the New York Metroplex.
The framework and results are validated by Subject Matter Experts (SMEs).
Keywords: Multi-airport system | Terminal area operation | Air traffic demand | Air traffic management | Distributionally robust optimization |
مقاله انگلیسی |
10 |
Managing congestion in supply chains via dynamic freight routing: An application in the biomass supply chain
مدیریت تراکم در زنجیره تامین از طریق مسیریابی حمل و نقل پویا: یک برنامه در زنجیره تامین زیستی-2017 This paper manages congestion in the supply chain via dynamic freight routing and using
multi-modal facilities in different time periods of a year. The proposed mixed integer non
linear program (MINLP) model captures the trade-offs that exists between investment,
transportation, and congestion management decisions. A linear approximation of the pro
posed MINLP model is then solved using a hybrid Benders-based rolling horizon algorithm.
The performance of the algorithm is tested on a case study that uses data from the
Southeast USA biomass supply chain network. Extensive numerical experiments provide
managerial insights to manage congestion from the biomass supply chain network.
Keywords:Biomass supply chain network design|Dynamic freight routing|Facility congestion|Benders decomposition|Rolling horizon heuristic |
مقاله انگلیسی |