Capacity-Driven Pricing Mechanism in Special Service Industries

We propose a capacity driven pricing mechanism for several service industries in which the customer behavior, the price demand relationship, and the competition are significantly distinct from other industries. According our observation, we found that the price demand relationship in these industries cannot be modeled by fitted curves; the customers would neither plan in advance nor purchase the service strategically; and the competition would be largely local. We analyze both risk neutral and risk aversion pricing models and conclude the proposed capacity driven model would be the optimal solution under mild assumptions. The resulting pricing mechanism has been implemented at our industrial partner with positive results since 2005

Ancillary Service Capacity Optimization for Both Electric Power Suppliers and Independent System Operator

Ancillary Services (AS) in electric power industry are critical to support the transmission of energy from generators to load demands while maintaining reliable operation of transmission systems in accordance with good utility practice. The ancillary services are procured by the independent system operator (ISO) through a process called the market clearing process which can be modeled by the partial equilibrium from the ends of ISO. There are two capacity optimization problems for both Market participants (MP) and Independent System Operator (ISO). For a market participant, the firm needs to determine the capacity allocation plan for various AS to pursue operating revenue under various uncertainties which can never be accurately estimated. We thereby employ a heuristic named “resource reservation” to suggest two types of bids, the regular and the must-win for a market participant to pursue higher expected revenue and satisfactory performance in terms of revenue under the worst case scenario. Meanwhile, the ISO, needs to determine the total amount of capacity required to guarantee the overall reliability of the transmission system. Our numerical experiment is based on our industrial partner’s operational data and the simulation result suggests that our proposed methods would greatly outperform the deterministic methods in terms of the profitability for a market participant and the ISO’s entire system’s reliability

Capacity Planning with Financial and Operational Hedging in Low‐Cost Countries

The authors of this paper outline a capacity planning problem in which a risk-averse firm reserves capacities with potential suppliers that are located in multiple low-cost countries. While demand is uncertain, the firm also faces multi-country foreign currency exposures. This study develops a mean-variance model that maximizes the firm’s optimal utility and derives optimal utility and optimal decisions in capacity and financial hedging size. The authors show that when demand and exchange rate risks are perfectly correlated, a risk- averse firm, by using financial hedging, will achieve the same optimal utility as a risk-neutral firm. In this paper as well, a special case is examined regarding two suppliers in China and Vietnam. The results show that if a single supplier is contracted, financial hedging most benefits the highly risk-averse firm when the demand and exchange rate are highly negatively related. When only one hedge is used, financial hedging dominates operational hedging only when the firm is very risk averse and the correlation between the two exchange rates have become positive. With both theoretical and numerical results, this paper concludes that the two hedges are strategic tools and interact each other to maximize the optimal utility

Re-Solving Stochastic Programming Models for Airline Revenue Management

We study some mathematical programming formulations for the origin-destination model in airline revenue management. In particular, we focus on the traditional probabilistic model proposed in the literature. The approach we study consists of solving a sequence of two-stage stochastic programs with simple recourse, which can be viewed as an approximation to a multi-stage stochastic programming formulation to the seat allocation problem. Our theoretical results show that the proposed approximation is robust, in the sense that solving more successive two-stage programs can never worsen the expected revenue obtained with the corresponding allocation policy. Although intuitive, such a property is known not to hold for the traditional deterministic linear programming model found in the literature. We also show that this property does not hold for some bid-price policies. In addition, we propose a heuristic method to choose the re-solving points, rather than re-solving at equally spaced times as customary. Numerical results are presented to illustrate the effectiveness of the proposed approach

Spatial analysis of housing markets using the hedonic approach with geographic information systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1994.Includes bibliographical references (leaves 181-188).by Lijian Chen.Ph.D

Planning and coordinated response mechanism of economic and ecological services in urban expansion

Against the backdrop of urban sustainable development around the world, how to coordinate both economic growth and ecological benefits in urban space becomes an important problem. Therefore, this study simulated and predicted the spatiotemporal changes in urban economy and ecosystem service value (E.S.V.) equivalent ratio under the current policies by 2030, and analysed how adjusting planning policies influences economy and ecology. This process was based on the future land use simulation (F.L.U.S.) model of coupled neural network, and on methods assessing the spatial changes in ecosystem services and land economy. This study aims to analyse urban land economy and E.S.V., and assess how China’s land spatial planning guides and promotes high-quality urban economic development. Results show that artificial intelligence (A.I.) simulation can forecast the results of spatial planning policies of national lands, to make policy-making more forwardlooking. The guidance of planning policies on urban expansion accelerates the increase in economic value of urban residential and commercial lands, thereby promoting the economic growth. However, adjusted planning policies may lead to ecological destruction. So, this study provides model verifications and path guidance to realise coordinated sustainable development between economy and ecology, serving as an important reference to formulating proper policies for urban development

MicroRNA-595 promotes osteogenic differentiation of bone marrow mesenchymal stem cells by targeting HMGA2

Purpose: To investigate the effect of miR-595 on osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs).Methods: Human BMSCs were osteogenically differentiated, and protein expression of alkaline phosphatase (ALP), osteocalcin (OCN), and Runt-related transcription factor 2 (RUNX2) were evaluated by western blot. Expression of miR-595 was measured by quantitative reverse transcription (qRT-PCR). The effect of miR-595 on viability of BMSCs was determined by MTT assay. Osteogenic differentiation of BMSCs was assessed by ALP and Alizarin red S (ARS) staining. The target gene of miR-595 was predicted by TargetScan analysis and validated by luciferase activity assay.Results: MiR-595 expression was higher in osteogenically differentiated BMSCs than in undifferentiated BMSCs (p < 0.01). Osteogenic ALP, OCN, and RUNX2 were also upregulated (p < 0.01). MiR-595 expression increased the viability of BMSCs, mineralized bone matrix formation, and ALP activity. High mobility group AT-hook 2 (HMGA2) expression was lower in osteogenically differentiated BMSCs and was found to be a target of miR-595. Overexpression of HMGA2 attenuated the miR-595-induced increase in cell viability, ALP activity, mineralized bone matrix formation, and osteogenic gene expression in BMSCs.Conclusion: The miR-595/HMGA2 axis is involved in osteogenic differentiation of BMSCs suggesting that it is a promising therapeutic target for osteoporosis

PSO-GA Based Resource AllocationStrategy for Cloud-Based SoftwareServices with Workload-Time Windows

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make run time decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services.The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods.publishedVersio