Essays in operations management

In the first essay, we investigate the impact of reciprocity in the dyadic supply chain. Our study is motivated by the experiences of the semiconductor and LCD industries, we investigate the impact of reciprocity in the dyadic supply chain. A notable characteristic in the above technology industries is the alternating possession of bargaining power caused by cyclical demand. We incorporate a reciprocal game in a dyadic supply channel over two periods. We investigate how a supplier is influenced and protects himself during the oversupply period by anticipating the buyer\u27s reciprocal behavior. Our results show that a supplier\u27s understanding of a buyer\u27s reciprocal behavior can mitigate double marginalization and can even fully coordinate the channel. This implies that even without a costly mechanism to resolve the double marginalization, appropriate consideration of the counterpart will increase channel efficiency. In the second essay, we consider a firm that manages a portfolio of customers placing orders that need replacement parts. The firm has both long-term and short-term customers. A long-term customer places both routine orders for routine maintenance and urgent orders due to emergency with a low margin for the firm and a short-term customer places urgent orders with a high margin for the firm. Routine orders provide stable loads and generate efficiency. Considering the increase in efficiency by routine orders, there is a trade-off between the efficiency and profitability of the order portfolio. Motivated by data provided by the company, we build an analytical model to support optimal decision making. We identify the impact of an additional urgent order to the cost embedded in the future operations. Finally, we model the mixed integer program to support the company\u27s capacity plan. We conclude with in sights provided to the firm and managerial insights for optimal customer order portfolios. In the third essay, we focus on the economic benefit of profound technology projects as milestones are achieved. Ce-Al alloy project by CMI promotes an example. The project we use replaces the current aluminum (Al) alloy with Al-cerium (Ce) alloy in an engine block and an engine head to increase the operational efficiency of the vehicle. We can expect higher fuel efficiency as well as a lower cost of production. The Ce-Al alloy development project by the Critical Material Institute (CMI) announces an achievement level at every milestone. The model keeps track of two goals, efficiency improvement and production cost reduction. Based on the progress about those two R&D tracks, the research question involves when to add capacity for production to maximize profits and how to adjust the R&D strategy to maximize benefit. The problem is modeled in a Bayesian update and a stochastic dynamic program. Insights from the model were used to estimate the economic benefit for CMI and suggestion for improvement

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions

Highly Durable and Active PtFe Nanocatalyst for Electrochemical Oxygen Reduction Reaction

Demand on the practical synthetic approach to the high performance electrocatalyst is rapidly increasing for fuel cell commercialization. Here we present a synthesis of highly durable and active intermetallic ordered face-centered tetragonal (fct)-PtFe nanoparticles (NPs) coated with a 'dual purpose' N-doped carbon shell. Ordered fct-PtFe NPs with the size of only a few nanometers are obtained by thermal annealing of polydopamine-coated PtFe NPs, and the N-doped carbon shell that is in situ formed from dopamine coating could effectively prevent the coalescence of NPs. This carbon shell also protects the NPs from detachment and agglomeration as well as dissolution throughout the harsh fuel cell operating conditions. By controlling the thickness of the shell below 1 nm, we achieved excellent protection of the NPs as well as high catalytic activity, as the thin carbon shell is highly permeable for the reactant molecules. Our ordered fct-PtFe/C nanocatalyst coated with an N-doped carbon shell shows 11.4 times-higher mass activity and 10.5 times-higher specific activity than commercial Pt/C catalyst. Moreover, we accomplished the long-term stability in membrane electrode assembly (MEA) for 100 h without significant activity loss. From in situ XANES, EDS, and first-principles calculations, we confirmed that an ordered fct-PtFe structure is critical for the long-term stability of our nanocatalyst. This strategy utilizing an N-doped carbon shell for obtaining a small ordered-fct PtFe nanocatalyst as well as protecting the catalyst during fuel cell cycling is expected to open a new simple and effective route for the commercialization of fuel cells. © 2015 American Chemical Society10911

Highly Durable and Active PtFe Nanocatalyst for Electrochemical Oxygen Reduction Reaction

Demand on the practical synthetic approach to the high performance electrocatalyst is rapidly increasing for fuel cell commercialization. Here we present a synthesis of highly durable and active intermetallic ordered face-centered tetragonal (fct)-PtFe nanoparticles (NPs) coated with a “dual purpose” N-doped carbon shell. Ordered fct-PtFe NPs with the size of only a few nanometers are obtained by thermal annealing of polydopamine-coated PtFe NPs, and the N-doped carbon shell that is <i>in situ</i> formed from dopamine coating could effectively prevent the coalescence of NPs. This carbon shell also protects the NPs from detachment and agglomeration as well as dissolution throughout the harsh fuel cell operating conditions. By controlling the thickness of the shell below 1 nm, we achieved excellent protection of the NPs as well as high catalytic activity, as the thin carbon shell is highly permeable for the reactant molecules. Our ordered fct-PtFe/C nanocatalyst coated with an N-doped carbon shell shows 11.4 times-higher mass activity and 10.5 times-higher specific activity than commercial Pt/C catalyst. Moreover, we accomplished the long-term stability in membrane electrode assembly (MEA) for 100 h without significant activity loss. From <i>in situ</i> XANES, EDS, and first-principles calculations, we confirmed that an ordered fct-PtFe structure is critical for the long-term stability of our nanocatalyst. This strategy utilizing an N-doped carbon shell for obtaining a small ordered-fct PtFe nanocatalyst as well as protecting the catalyst during fuel cell cycling is expected to open a new simple and effective route for the commercialization of fuel cells