In recent years, supply chains are more prone to disruptions. The impact on performance depends the system's ability discover and then recover after disruption has occurred. this paper, we proposed a new method integrate probabilistic assessment of risks into Risk Exposure Index (REI) approach previously by Simchi-Levi et al. measure chain resiliency analyzing worst-case CVaR (WCVaR) total lost sales under We show that optimal strategic inventory positioning strategy in model can be fully...

This paper studies how to schedule medical appointments with time-dependent patient no-show behavior and random service times. The problem is motivated by our of independent datasets from countries in two continents that unanimously identify a significant time-of-day effect on show-up probabilities. We deploy distributionally robust model, which minimizes the worst-case total expected costs waiting provider’s idling overtime, optimizing scheduled arrival times patients. model challenging...

Spatiotemporal data fusion provides an efficacious strategy for addressing gaps within time series datasets. This approach significantly enhances the feasibility of large-scale remote sensing applications by, example, enabling creation seamless Data Cubes (SDC). Nevertheless, strict input requirements and low computational efficiency current methods severely limit practicality SDC production. In this study, we propose efficient spatiotemporal method, Fast Variation-based Fusion (FastVSDF)...

It is widely believed that a little flexibility added at the right place can reap significant benefits for operations. Unfortunately, despite extensive literature on this topic, we are not aware of any general methodology be used to guide managers in designing sparse (i.e., slightly flexible) and yet efficient We address issue using distributionally robust approach model performance stochastic system under different process structures. use dual prices obtained from related conic program...

Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based framework. The aim ScienceEarth to store, manage, process large-scale in cloud-based cluster-computing environment. platform consists following three main parts: ScienceGeoData, ScienceGeoIndex, ScienceGeoSpark. ScienceGeoData stores manages data. ScienceGeoIndex an index query system, spatial based on quad-tree Hilbert curve which...

The frequency of coastal flood damages is expected to increase significantly during the twenty-first century as sea level rises in floodplain. Coastal digital elevation model (DEM) data describing topography are essential for assessing future flood-related and understanding impacts sea-level rise. Shuttle Radar Topography Mission (SRTM) Advanced Spaceborne Thermal Emission Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) currently most accurate freely available DEM data....

The fusion of spatiotemporal data provides the possibility to improve both spatial and temporal resolution remote sensing data. Nevertheless, performance current methods is affected by several aspects, e.g., (1) retrieval abrupt land cover changes, (2) recovery detailed information, (3) need reduce side effects related differences between sensors. Concerning above this study proposes use a Variation-based Spatiotemporal Data Fusion (VSDF) method. In VSDF, an abundant variation classification...

Recent advances in cloud-based remote sensing platforms have revoluted the routines for big data (RSBD) analysis. However, it is challenging to make user-defined algorithms reusable RSBD applications if not pre-implemented platforms, especially legacy written with specific programming languages and libraries. In recent years, emergence of containerization, which core feature cloud native, provided effective solutions port environment. this research, we present a novel approach deploy...

Problem definition: This paper addresses an appointment scheduling problem involving multiple sequential servers using a distributionally robust optimization (DRO) approach. Two decisions are optimized: the schedule for patient visits and adjusting policy to rebalance customers’ waiting time across servers. Methodology/results: We formulate in sequential-servers systems conic optimization, incorporating service correlations. find that traditional cost-minimization approach results imbalanced...

A mixture of multinomial logits (mixed logit (MMNL)) generalizes the model, which is commonly used in modeling market demand to capture consumer heterogeneity. Although extensive algorithms have been developed literature learn MMNL models, theoretical results are limited. Built on Frank-Wolfe (FW) method, we propose a new algorithm that learns both weights and component-specific parameters with provable convergence guarantees for an arbitrary number mixtures. Our utilizes historical choice...

In optimization problems, decisions are often made in the face of uncertainty that might arise form random costs or benefits. “Distributionally Robust Linear and Discrete Optimization with Marginals,” Louis Chen, Will Ma, Karthik Natarajan, David Simchi-Levi, Zhenzhen Yan study a robust bound linear discrete problems which objective coefficients set admissible joint distributions is assumed to be specified only up marginals. They provide primal-dual formulation for this problem, process,...

We develop a data-driven approach for the multiproduct pricing problem, using theory of representative consumer in discrete choice. establish set mathematical relationships between product prices and demand each system, including that outside option. provide identification conditions to recover underlying model show that, with sufficient experiments, can identify (more precisely, associated perturbation function model) accurately up constant shift given tolerance level. This holds even when...

Silver ion (Ag+) is one of the most common heavy metal ions that cause environmental pollution and affect human health, therefore, its detection great importance in field analytical chemistry. Here, we report an 8-nucleotide (nt) minidumbbell DNA-based sensor (M-DNA) for Ag+ detection. The contained a unique reverse wobble C·C mispair minor groove, which served as binding site Ag+. M-DNA could achieve limit 2.1 nM sense real samples with high accuracy. More importantly, exhibited advantages...

The human thermal stress indices and datasets are vital for promoting public health reducing negative environmental impacts as global climate change extreme meteorological events increase. current generally use an instantaneous or average value to describe which cannot reflect the distribution of comfort conditions over time, there no global-scale with both 0.1° higher spatial resolution hourly temporal available yet. A novel metric, Thermal Stress Duration (TSD), is proposed represent...

Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an retailer may consolidate from same consumer. We investigate how long should hold consumer’s before sending them to third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize holding time balance cost and potential delay in delivery. Methodology/results: model problem as Markov decision process....

The corresponding physical model was established by using a tunnel, and the appropriate fire source power selected. minimum export momentum calculated method, outlet jet velocity of air curtain is given as umin = 14m/s. change concentration CO, visibility temperature in same environment under influence piston wind analyzed CFD simulation software different curtain, comparison at speed changes with time simultaneously, to study effect on spread characteristics flue gas tunnel fire. results...

Today, remote sensing (RS) data are already regarded as "big data." Developments in computer science have made it possible to explore the potential treasure within big data, but only limited research has use of technology due gaps techniques between and sensing. In this research, we analyzed full processing flow from perspective both proposed a modular framework. Computation ready (CRD), dynamic type for computation based on analysis (ARD), is connect two main modules framework, module...

Problem definition: This paper studies an appointment system where a finite number of customers are scheduled to arrive in such way that (1) the expected waiting time each individual customer cannot exceed given threshold; and (2) times set as early possible (without breaking constraint). Methodology/results: First, we show that, under service-level constraint, prospective schedule can be obtained from sequential scheduling approach. In particular, next based on previous customers. Then, use...