Optimization Models to Integrate Production and Transportation Planning for Biomass Co-Firing in Coal-Fired Power Plants

Co-firing biomass is a strategy that leads to reduced greenhouse gas emissions in coal-fired power plants. Incentives such as production tax credit (PTC) are designed to help power plants overcome the financial challenges faced during the implementation phase. Decision makers at power plants face two big challenges. The first challenge is identifying whether the benefits from incentives such as PTC can overcome the costs associated with co-firing. The second challenge is identifying the extent to which a plant should co-fire in order to maximize profits. We present a novel mathematical model that integrates production and transportation decisions at power plants. Such a model enables decision makers evaluate the impacts of co-firing on the system performance and the cost of generating renewable electricity. The model presented is a nonlinear mixed integer program which captures the loss in process efficiencies due to using biomass, a product which has lower heating value as compared to coal; the additional investment costs necessary to support biomass co-firing; as well as savings due to PTC. In order to solve efficiently real-life instances of this problem we present a Lagrangean relaxation model which provide upper bounds and two linear approximations which provide lower bounds for the problem in hand. We use numerical analysis to evaluate the quality of these bounds. We develop a case study using data from nine states located in the southeast region of USA. Via numerical experiments we observe that: (a) Incentives such as PTC do facilitate renewable energy production. (b) The PTC should not be “one size fits all”. Instead, tax credits could be a function of plant capacity, or the amount of renewable electricity produced. (c) There is a need for comprehensive tax credit schemes to encourage renewable electricity production and reduce GHG emissions

Effects Of RFID Technology On Efficiency And Profitability In Retail Supply Chains

Ten years have passed since Wal-Mart’s public announcement about its RFID technology adoption plan in 2003. Some large competitors of Wal-Mart in the U.S. retail industry jumped on the trend of RFID technology adoption. However, there has been a slowdown of RFID technology adoption since 2008. Many U.S. retailers do not consider adopting RFID technology because of the uncertainty of return on investment and the lack of business cases demonstrating its profitability or efficiency. This study investigates whether RFID companies have better financial performance ratios in the U.S. retail supply chains. RFID retailers have significantly lower days-in-inventory and lower per-employee costs. Compared with pre-RFID, the RFID retailers do not improve profit ratios after they adopted it, but their days-in-inventory ratio and sales efficiency improve significantly. Panel data regression analyses show that inventory management efficiency does impact gross margins, but the impact of cost efficiency is negligible. RFID retailers have a positive relationship with gross margin increases. In summary, it could be stated that introducing RFID improves inventory management efficiency but we do not know yet if RFID technology adoption also contributes to profitability in U.S. retail industry

Framework for Accommodating Emerging Autonomous Vehicles

69A3551747117In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, vehicles may expend extra energy as they try to form platoons. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this project, we develop a simulation-optimization framework to tackle the challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. The system is simulated with and without platooning to quantify the energy savings. The optimization model is turned off when assessing the system's performance without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this project, inspired by Tadakuma and colleagues' work. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multivehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system-wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically

Subset Selection In Multiple Linear Regression: A New Mathematical Programming Approach

We address the so-called subset selection problem in multiple linear regression where the objective is to select a minimal subset of predictor variables without sacrificing any explanatory power. A new mathematical programming model is proposed for this purpose. A parametric solution of this model yields a number of efficient subsets. To obtain this solution, we repeatedly use an exact or one of two heuristic algorithms. The subsets generated in this way are compared with the ones generated by several standard procedures. The results suggest that, in most cases, our approach finds subsets that compare favorably against the standard procedures (in terms of generally accepted measures such as adjusted R 2 and Mallow's C p ). Keywords: Heuristics; Mathematical programming; Multivariate statistics; Regression. 1. Introduction A common challenge for a regression analyst is the selection of the best subset from a set of predictor variables in terms of some specified criterion. Histori..

Risk Based HazMat Routing In a Supply Chain Network

Hazardous Material (HazMat) transportation is a heavily regulated process due to safety issue and detrimental environmental impact of potential incidents. Route selection is an important part of HazMat supply chain transportation planning. Industries worldwide must adhere with the regulations and transport code to ship HazMat. This paper introduces several HazMat transportation risk factors with regard to social, environmental, and sustainability features. Using these risk factors, the study demonstrates network risk of HazMat transportation from a point of origin to the destination as compared to non-hazardous material. While there are added risks of HazMat transport, transportation cost is higher due to finding safety route and avoid high risk zone and the likelihood of accidents in a route. Like others, HazMat routing is selected primarily based on shortest distance and shortest transport time to minimize the cost of shipment. In addition to those factors, HazMat routing also depends on risk factors associated with routes. This paper proposes a risk quantification method and integrating that into the route selection process. A case study demonstrates the proposed routing