The problems of online pricing with offline data,among other similar online decision making with offline data problems,aim at designing and evaluating online pricing policies in presence of a certain amount of existin...The problems of online pricing with offline data,among other similar online decision making with offline data problems,aim at designing and evaluating online pricing policies in presence of a certain amount of existing offline data.To evaluate pricing policies when offline data are available,the decision maker can either position herself at the time point when the offline data are already observed and viewed as deterministic,or at the time point when the offline data are not yet generated and viewed as stochastic.We write a framework to discuss how and why these two different positions are relevant to online policy evaluations,from a worst-case perspective and from a Bayesian perspective.We then use a simple online pricing setting with offline data to illustrate the constructions of optimal policies for these two approaches and discuss their differences,especially whether we can decompose the searching for the optimal policy into independent subproblems and optimize separately,and whether there exists a deterministic optimal policy.展开更多
The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF model...The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF models can be inverted to estimate the important biological or climatological parameters of the earth surface such as leaf area index and albedo. The state-of-the-art of BRDF is the use of the linear kernel-driven models, mathematically described as the linear combination of the isotropic kernel, volume scattering kernel and geometric optics kernel. The computational stability is characterized by the algebraic operator spectrum of the kernel-matrix and the observation errors. Therefore, the retrieval of the model coefficients is of great importance for computation of the land surface albedos. We first consider the smoothing solution method of the kernel-driven BRDF models for retrieval of land surface albedos. This is known as an ill-posed inverse problem. The ill-posedness arises from that the linear kernel driven BRDF model is usually underdetermined if there are too few looks or poor directional ranges, or the observations are highly dependent. For example, a single angular observation may lead to an under-determined system whose solution is infinite (the null space of the kernel operator contains nonzero vectors) or no solution (the rank of the coefficient matrix is not equal to the augmented matrix). Therefore, some smoothing or regularization technique should be applied to suppress the ill-posedness. So far, least squares error methods with a priori knowledge, QR decomposition method for inversion of the BRDF model and regularization theories for ill-posed inversion were developed. In this paper, we emphasize on imposing a priori information in different spaces. We first propose a gen-eral a priori imposed regularization model problem, and then address two forms of regularization scheme. The first one is a regularized singular value decomposition method, and then we propose a retrieval method in l1 space. We show that the proposed method is suitable for solving land surface parameter retrieval problem if the sampling data are poor. Numerical experiments are also given to show the efficiency of the proposed methods.展开更多
The Belt and Road Initiative(BRI)is a massive,ambitious,long-term project initiated by the Chinese government,with participation from many other countries,to facilitate trade and improve logistics in an effort to prom...The Belt and Road Initiative(BRI)is a massive,ambitious,long-term project initiated by the Chinese government,with participation from many other countries,to facilitate trade and improve logistics in an effort to promote global economic development.In this paper,we identified the supply chain and logistics innovations linked to the BRI.These innovations include new routes and modes for global trade,new supply chain design,reduction of cross-border logistics frictions,and entrepreneurial development.Examples of some of these innovations are emerging,while new ones are being developed.These innovations can enable businesses to improve their operational performances and create economic value.At the same time,to realize the full potentials of BRI,new work processes and technologies,incentive alignment,collaborations among businesses,and optimized plan-ning are needed.This provides great opportunities for researchers to explore how to overcome barriers and achieve the full values of BRI.展开更多
One of the classical optimization models for image segmentation is the well known Markov Random Fields(MRF)model.This model is a discrete optimization problem,which is shown here to formulate many continuous models us...One of the classical optimization models for image segmentation is the well known Markov Random Fields(MRF)model.This model is a discrete optimization problem,which is shown here to formulate many continuous models used in image segmentation.In spite of the presence of MRF in the literature,the dominant perception has been that the model is not effective for image segmentation.We show here that the reason for the non-effectiveness is due to the lack of access to the optimal solution.Instead of solving optimally,heuristics have been engaged.Those heuristic methods cannot guarantee the quality of the solution nor the running time of the algorithm.Worse still,heuristics do not link directly the input functions and parameters to the output thus obscuring what would be ideal choices of parameters and functions which are to be selected by users in each particular application context.We describe here how MRF can model and solve efficiently several known continuous models for image segmentation and describe briefly a very efficient polynomial time algorithm,which is provably fastest possible,to solve optimally the MRF problem.The MRF algorithm is enhanced here compared to the algorithm in Hochbaum(2001)by allowing the set of assigned labels to be any discrete set.Other enhancements include dynamic features that permit adjustments to the input parameters and solves optimally for these changes with minimal computation time.Several new theoretical results on the properties of the algorithm are proved here and are demonstrated for images in the context of medical and biological imaging.An interactive implementation tool for MRF is described,and its performance and flexibility in practice are demonstrated via computational experiments.We conclude that many continuous models common in image segmentation have discrete analogs to various special cases of MRF and as such are solved optimally and efficiently,rather than with the use of continuous techniques,such as PDE methods,that restrict the type of functions used and furthermore,can only guarantee convergence to a local minimum.展开更多
In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price diffe...In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.展开更多
Graphene oxide(GO)is playing an increasing role in many technologies.However,it remains unanswered how to strategically distribute the functional groups to further enhance performance.We utilize deep reinforcement lea...Graphene oxide(GO)is playing an increasing role in many technologies.However,it remains unanswered how to strategically distribute the functional groups to further enhance performance.We utilize deep reinforcement learning(RL)to design mechanically tough GOs.The design task is formulated as a sequential decision process,and policy-gradient RL models are employed to maximize the toughness of GO.Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs.In addition,our RL approach reaches optimized functional group distributions within only 5000 rollouts,while the simplest design task has 2×10^(11)possibilities.Finally,we show that our approach is scalable in terms of the functional group density and the GO size.The present research showcases the impact of functional group distribution on GO properties,and illustrates the effectiveness and data efficiency of the deep RL approach.展开更多
Today’s supply chain is becoming complex and fragile.Hence,supply chain managers need to create and unlock the value of the smart supply chain.A smart supply chain requires connectivity,visibility,and agility,and it ...Today’s supply chain is becoming complex and fragile.Hence,supply chain managers need to create and unlock the value of the smart supply chain.A smart supply chain requires connectivity,visibility,and agility,and it needs be integrated and intelligent.The digital twin(DT)concept satisfies these requirements.Therefore,we propose creating a DT-driven supply chain(DTSC)as an innovative and integrated solution for the smart supply chain.We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept.We discuss three research opportunities in building a DTSC,including supply chain modeling,real-time supply chain optimization,and data usage in supply chain collaboration.Finally,we highlight a motivating case from JD.COM,China’s largest retailer by revenue,in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.展开更多
文摘The problems of online pricing with offline data,among other similar online decision making with offline data problems,aim at designing and evaluating online pricing policies in presence of a certain amount of existing offline data.To evaluate pricing policies when offline data are available,the decision maker can either position herself at the time point when the offline data are already observed and viewed as deterministic,or at the time point when the offline data are not yet generated and viewed as stochastic.We write a framework to discuss how and why these two different positions are relevant to online policy evaluations,from a worst-case perspective and from a Bayesian perspective.We then use a simple online pricing setting with offline data to illustrate the constructions of optimal policies for these two approaches and discuss their differences,especially whether we can decompose the searching for the optimal policy into independent subproblems and optimize separately,and whether there exists a deterministic optimal policy.
基金Supported by National Natural Science Foundation of China (Grant Nos. 10501051, 10871191)Key Project of Chinese National Programs for Fundamental Research and Development (Grant Nos. 2007CB714400, 2005CB422104)
文摘The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF models can be inverted to estimate the important biological or climatological parameters of the earth surface such as leaf area index and albedo. The state-of-the-art of BRDF is the use of the linear kernel-driven models, mathematically described as the linear combination of the isotropic kernel, volume scattering kernel and geometric optics kernel. The computational stability is characterized by the algebraic operator spectrum of the kernel-matrix and the observation errors. Therefore, the retrieval of the model coefficients is of great importance for computation of the land surface albedos. We first consider the smoothing solution method of the kernel-driven BRDF models for retrieval of land surface albedos. This is known as an ill-posed inverse problem. The ill-posedness arises from that the linear kernel driven BRDF model is usually underdetermined if there are too few looks or poor directional ranges, or the observations are highly dependent. For example, a single angular observation may lead to an under-determined system whose solution is infinite (the null space of the kernel operator contains nonzero vectors) or no solution (the rank of the coefficient matrix is not equal to the augmented matrix). Therefore, some smoothing or regularization technique should be applied to suppress the ill-posedness. So far, least squares error methods with a priori knowledge, QR decomposition method for inversion of the BRDF model and regularization theories for ill-posed inversion were developed. In this paper, we emphasize on imposing a priori information in different spaces. We first propose a gen-eral a priori imposed regularization model problem, and then address two forms of regularization scheme. The first one is a regularized singular value decomposition method, and then we propose a retrieval method in l1 space. We show that the proposed method is suitable for solving land surface parameter retrieval problem if the sampling data are poor. Numerical experiments are also given to show the efficiency of the proposed methods.
基金We would like to acknowledge support from NSFC 71991462
文摘The Belt and Road Initiative(BRI)is a massive,ambitious,long-term project initiated by the Chinese government,with participation from many other countries,to facilitate trade and improve logistics in an effort to promote global economic development.In this paper,we identified the supply chain and logistics innovations linked to the BRI.These innovations include new routes and modes for global trade,new supply chain design,reduction of cross-border logistics frictions,and entrepreneurial development.Examples of some of these innovations are emerging,while new ones are being developed.These innovations can enable businesses to improve their operational performances and create economic value.At the same time,to realize the full potentials of BRI,new work processes and technologies,incentive alignment,collaborations among businesses,and optimized plan-ning are needed.This provides great opportunities for researchers to explore how to overcome barriers and achieve the full values of BRI.
基金This research was supported in part by NSF awards No.CMMI-1200592 and CBET-0736232.
文摘One of the classical optimization models for image segmentation is the well known Markov Random Fields(MRF)model.This model is a discrete optimization problem,which is shown here to formulate many continuous models used in image segmentation.In spite of the presence of MRF in the literature,the dominant perception has been that the model is not effective for image segmentation.We show here that the reason for the non-effectiveness is due to the lack of access to the optimal solution.Instead of solving optimally,heuristics have been engaged.Those heuristic methods cannot guarantee the quality of the solution nor the running time of the algorithm.Worse still,heuristics do not link directly the input functions and parameters to the output thus obscuring what would be ideal choices of parameters and functions which are to be selected by users in each particular application context.We describe here how MRF can model and solve efficiently several known continuous models for image segmentation and describe briefly a very efficient polynomial time algorithm,which is provably fastest possible,to solve optimally the MRF problem.The MRF algorithm is enhanced here compared to the algorithm in Hochbaum(2001)by allowing the set of assigned labels to be any discrete set.Other enhancements include dynamic features that permit adjustments to the input parameters and solves optimally for these changes with minimal computation time.Several new theoretical results on the properties of the algorithm are proved here and are demonstrated for images in the context of medical and biological imaging.An interactive implementation tool for MRF is described,and its performance and flexibility in practice are demonstrated via computational experiments.We conclude that many continuous models common in image segmentation have discrete analogs to various special cases of MRF and as such are solved optimally and efficiently,rather than with the use of continuous techniques,such as PDE methods,that restrict the type of functions used and furthermore,can only guarantee convergence to a local minimum.
基金supported in part by the Joint Funds of the National Natural Science Foundation of China(U2066214)in part by Shanghai Sailing Program(22YF1414500)in part by the Project(SKLD22KM19)funded by State Key Laboratory of Power System Operation and Control.
文摘In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.
基金This work used the Extreme Science and Engineering Discovery Environment(XSEDE)Bridges system,which is supported by National Science Foundation(Fund number:ACI-1548562)The authors acknowledge support from the Alfred P.Sloan Foundation and the National Science Foundation(Fund Number:DMREF-2119276).
文摘Graphene oxide(GO)is playing an increasing role in many technologies.However,it remains unanswered how to strategically distribute the functional groups to further enhance performance.We utilize deep reinforcement learning(RL)to design mechanically tough GOs.The design task is formulated as a sequential decision process,and policy-gradient RL models are employed to maximize the toughness of GO.Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs.In addition,our RL approach reaches optimized functional group distributions within only 5000 rollouts,while the simplest design task has 2×10^(11)possibilities.Finally,we show that our approach is scalable in terms of the functional group density and the GO size.The present research showcases the impact of functional group distribution on GO properties,and illustrates the effectiveness and data efficiency of the deep RL approach.
基金The authors are grateful for the financial support from the National Key R&D Program of China(Grant No.2018YFB1700600).
文摘Today’s supply chain is becoming complex and fragile.Hence,supply chain managers need to create and unlock the value of the smart supply chain.A smart supply chain requires connectivity,visibility,and agility,and it needs be integrated and intelligent.The digital twin(DT)concept satisfies these requirements.Therefore,we propose creating a DT-driven supply chain(DTSC)as an innovative and integrated solution for the smart supply chain.We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept.We discuss three research opportunities in building a DTSC,including supply chain modeling,real-time supply chain optimization,and data usage in supply chain collaboration.Finally,we highlight a motivating case from JD.COM,China’s largest retailer by revenue,in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.
