DATA-DRIVEN MODELS OF CUSTOMER BEHAVIOR TO IMPROVE OPERATIONAL EFFICIENCY IN SERVICE SYSTEMS

In 2015, on a global level, the service industry represented 66% of GDP and employed over 51% of the total working population making it one of the largest industries. Some of the sectors in this industry include finance, transportation, call centers, retail, and health care. Customers and service providers are key players in this industry. A successful transaction between these two results in a valued service for the customer and revenue to the provider. The primary objective of the providers, therefore, is to understand the customer’s needs, meet their requirements, provide quality service and achieve customer satisfaction. In this thesis, I utilize large data sets on customer-provider transactions to study two important issues. First, I build micro-demand models to predict true demand incorporating customer behavior, time and spatial dynamics. I utilize the predicted demand to optimally allocate the resources for improved operational performance. The study contexts I focus on are Bike Sharing systems and Street-Hail Taxi services. Second, I build micro models to understand the factors driving customer provided satisfaction measure on logistics service and their impact on purchase probability in E-commerce platforms. In the first chapter, I analyze the optimal allocation of bikes in a network of stations to improve ridership under non-stationarity demand and station substitution. Using large datasets on the censored trip and minute-level inventory, walking distance between stations and a stochastic model, I predict true demand at each station. Then I determine an optimal allocation of bikes across stations at the start of the day utilizing a dynamic program to maximize ridership in the network. I find the optimal policy could improve ridership and service level by 7.60% and 1.69% respectively. In the second chapter, I examine the impact of logistics performance metrics such as delivery delays, customer's promised speed of delivery, order split, etc. on logistics service ratings of sellers on an e-commerce platform. Using a large dataset of customer orders from an e-commerce platform, I find logistics ratings are negatively impacted by delivery delays, but positively impacted by faster-promised speed of delivery and total order amount paid. I also find that logistics ratings impact customer purchasing behavior positively. Lastly, I show that a reduction in delivery delay by one day can improve the average weekly sales by as much as 2.5%. In the third chapter, I study passenger demand estimation problem in Street-Hail Taxi services. I utilize large-scale datasets on GPS information of pick-ups and drop-off from New York Yellow Taxi services for this study. I first develop a stochastic model (double-ended queue) to predict passenger demand in location and time. The model allows for non-stationarity, randomness in arrivals and reneges of both drivers and passengers. Using sample path information along with Maximum Likelihood Estimation, I develop a framework to estimate true passenger demand, drivers and passengers renege rate. The predicted demand can be used to analyze the optimal timing of drivers change their shift to maximize revenue under the current status quo of delay in shift changeover. Overall, the thesis focuses on the analysis of large and granular transactional data to build micro demand models incorporating customer behavior and incorporate the models into planning to improve operational efficiency.Doctor of Philosoph

New Checkpoint and Rollback for High Availability of Mapreduce Computing

MapReduce is a programming model and an associated implementation for processing and generating large data sets, so called big data. A MapReduce job usually splits the input data-set into independent chunks which are processed by the maptasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. If an error occurs in a name node other name node will take over the failed node and continues its execution. Other than data node failure, if an error occurs during the program execution itself then there must be a detection and recovery steps to correct the error.A solution for this problem is to implement the checkpoint and rollback mechanism in the system. When memory error occurs in the MapReduce program then execution in all the data nodes will be stopped and it starts all over from the starting phase in hadoop. The proposed methodology is to detect the heap space error [10] and provide a recovery operations by employing a new checkpoint and recovery process. In order to realize this, a new phase based checkpoint and rollback is proposed versus the hadoop default configuration. Once an error occurs in hadoop, the memory size required by the program is raised then the configuration file setting is modified and then a checkpoint is set and from there next phases will be executed. In this way, the entire already completed phases are not needed to be re-executed. From the experimental results, the hadoop availability is increased to 53.22% compared to the default hadoop configuration thereby decreasing the running time of the application.Computer Scienc

DESIGN ANALYSIS AND FABRICATION OF 4-STROKE HERO-HONDA S.I ENGINE PISTON USING A356/SIC/FLYASH REINFORCED COMPOSITE MATERIAL

This paper presents the fabrication of piston using hybrid composites with aluminum matrix A356 alloy, reinforced with silicon carbide (SiC) and Fly ash. Newly formed A356/FA/SiC hybrid composites are the grouping of the two different hybrid materials. Dry sliding wear tests are conducted on pin-on disc wear testing machine and the frictional effects are analyzed. A 3D model of the Piston used in a two wheeler is designed and modeled in 3D modeling software CATIA and imported the model into Ansys. Finite ele­ment analysis is performed on the Piston using the material Aluminum 356 and to determine stresses and temperature effects

Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol

Combined noble-transition metal catalysts have been used to produce a wide range of important non-petroleum-based chemicals from biomass-derived furfural (as a platform molecule) and have garnered colossal research interest due to the urgent demand for sustainable and clean fuels. Herein, we report the palladium-modified metal–organic-framework (MOF) assisted preparation of PdCo3O4 and PdFe3O4 nanoparticles encapsulated in a graphitic N-doped carbon (NC) matrix via facile in situ thermolysis. This provides a change in selectivity with superior catalytic activity for the reductive upgrading of biomass-derived furfural (FA). Under the optimized reaction conditions, the newly designed PdCo3O4@NC catalyst exhibited highly efficient catalytic performance in the hydrogenation of furfural, providing 100% furfural conversion with 95% yield of tetrahydrofurfuryl alcohol (THFAL). In contrast, the as-synthesized Pd–Fe3O4@NC afforded a THFAL yield of 70% after an 8 h reaction with four consecutive recycling tests. Based on different characterization data (XPS, H2-TPR) for nanohybrids, we can conclude that the presence of PdCo-Nx active sites, and the multiple synergistic effects between Co3O4 and Pd(II), Co3O4 and Pd0, as well as the presence of N in the carbonaceous matrix, are responsible for the superior catalytic activity and improved catalyst stability. Our strategy provides a facile design and synthesis process for a noble-transition metal alloy as a superior biomass refining, robust catalyst via noble metal modified MOFs as precursors

Stable Scheduling Increases Productivity and Sales

Variable schedules are now the norm for part-time workers in a variety of industries including retail, where schedules typically change every day and every week, with three to seven days' notice of the next week's schedule. In recent years, these scheduling practices have come under increasing scrutiny in state attorney general offices, state and local legislatures, and the media. In retail, unstable schedules for employees have been considered an inevitable outcome of stores' need for profitability. Operations researchers have found that matching labor to incoming traffic is a key driver of retail store profitability (Perdikaki et al., 2012). At the same time, social scientists have studied the deleterious effects of variable schedules on employee wellbeing (Henly & Lambert, 2014). What has been lacking is evidence that schedules in service-sector jobs can be improved in ways that benefit both employers and employees

Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol

Combined noble-transition metal catalysts have been used to produce a wide range of important non-petroleum-based chemicals from biomass-derived furfural (as a platform molecule) and have garnered colossal research interest due to the urgent demand for sustainable and clean fuels. Herein, we report the palladium-modified metal–organic-framework (MOF) assisted preparation of PdCo3O4 and PdFe3O4 nanoparticles encapsulated in a graphitic N-doped carbon (NC) matrix via facile in situ thermolysis. This provides a change in selectivity with superior catalytic activity for the reductive upgrading of biomass-derived furfural (FA). Under the optimized reaction conditions, the newly designed PdCo3O4@NC catalyst exhibited highly efficient catalytic performance in the hydrogenation of furfural, providing 100% furfural conversion with 95% yield of tetrahydrofurfuryl alcohol (THFAL). In contrast, the as-synthesized Pd–Fe3O4@NC afforded a THFAL yield of 70% after an 8 h reaction with four consecutive recycling tests. Based on different characterization data (XPS, H2-TPR) for nanohybrids, we can conclude that the presence of PdCo-Nx active sites, and the multiple synergistic effects between Co3O4 and Pd(II), Co3O4 and Pd0, as well as the presence of N in the carbonaceous matrix, are responsible for the superior catalytic activity and improved catalyst stability. Our strategy provides a facile design and synthesis process for a noble-transition metal alloy as a superior biomass refining, robust catalyst via noble metal modified MOFs as precursors

Leaching Studies of Copper Anode Slime for Recovery of Valuable Metals

During production of Copper from ores or copper concent-rate in electro refining stage we get pure copper at cathode and impure mud at the anode which settles at the bottom of the electro refining tank and is collected at regular intervals. This is known as anode slime. During electro refining of copper anode slime is collected as a by-product. Anode slime contains valuable metals like Cu, Se, Te, Ag, Au, Ni, and platinum group metals. [1] The raw anode slime normally looks like greyish black in colour. It is very fine powder particles; particles size is about 20 mesh(BSS). The particles have smooth surfaces, round edges. Some of them are having spherical shape[1]. The composition of anode slime varies from refinery to refin-ery depending upon the various types of copper ores used at various plants. In general in anode slime the phases present are Cu2Se, Ag2Te. Ag2Se, AgCuSe, Cu2O, NiO, Cu-Ni-Sb, CuSO4.5H2O etc. These are complex in nature.[1]. It is important to process anode slime for the recovery of valuable metals present in it which finds a number of applications of these metals

Modeling, Control and Design of DC-DC Boost Converter for Grid Connected Photo-Voltaic Applications

This article presents a design and development method of a DC-DC boost converter with constant output voltage. This system has a nonlinear dynamic behavior, as it works in switch-mode. Moreover, it is exposed to significant variations which may take this system away from nominal conditions, due to changes on the load or on the line voltage at the input. From a fluctuating or a variable input voltage, boost converter is able to step up the input voltage to a higher constant dc output voltage using the Non-linear feedback controllers such as PID controller and the Sliding Mode controllers. By this technique, the output of the converter is measured and compared with a reference voltage. The differential of the compared value will be used to produce a pulse width modulation signal to control switch in the boost converter. Simulation results describe the performance of the proposed design

A Review on Comparison of Mechanical Properties of Dissimilar Steels Welded By TIG and MIG

In this review the study will be carried out on comparison of Mechanical Properties like Hardness and Strength of joints welded by TIG and MIG processes. Welding of dissimilar steels has became most common process now days in wide applications in industries. The Stainless Steel and Mild Steel joints have more applications in structural industries which provide good combination of Mechanical Properties like strength, Corrosion resistance etc. Selections of welding process for different material are difficult because of their physical and chemical properties. To obtain good quality of weld, it is necessary to select proper welding technique according to the materials selected for weldin