Agnes Galambosi

Linear quadratic dynamic programming for water reservoir management

Abstract Dynamic programming (DP) is applied in order to determine the optimal management policy for a water reservoir by modeling the physical problem via a linear quadratic (LQ) structure. A simplified solution to the LQ tracking problem is provided under mild assumptions. The model presents an aggregated multicriteria decision making problem where flood control, hydroelectric power, and water demand have to be satisfied: Simultaneously the energy production is to be maximized, the mismatch of water demand minimized, and the water release should not cause flooding. The system constraints are basically the conservation of mass within the reservoir system, and the minimum and the maximum allowable limits for the water release and the reservoir level. The stochastic variables consist of the water inflow from the reservoir drainage basin precipitation, and evaporation. The Tenkiller Ferry dam on the Illinois River basin in Oklahoma is analyzed as a case study.

When Does RFID Make Business Sense for Managing Supply Chain

Radio frequency identification (RFID) is believed to change how supply chains operate today. While RFID’s promise for improved inventory visibility and automation in inventory management is making many supply chain players hopeful for increased sales and reduced operating costs, these benefits do come at a cost and involve risks. This article presents a financial returns analysis that captures RFID’s costs and benefits, and quantifies the financial risks of implementing RFID for various business sizes and products with different unit profits to understand when RFID makes business sense. More precisely, the returns analysis is performed using an econometric model to understand how break-even sales volumes, unit profits, tag prices, return on investment, and risks vary between a manufacturer and a retailer in a supply chain. The results are extended to multiproduct cases as well. A sensitivity analysis is also performed to understand the returns in pessimistic and optimistic scenarios.

Nonlinear and dynamic programming for epidemic intervention

This paper studies an optimal control problem for a discrete-time, deterministic Susceptible/Infective/Susceptible (SIS) epidemic with a finite time horizon. The problem involves finding the minimum of an objective function of a controlled process subject to the constraints of limited resources. Control sequences are obtained through two alternative methods: nonlinear programming (NLP) and dynamic programming (DP). The NLP approach is restricted to problems of low dimension and, therefore, by this algorithm, one must be satisfied with an optimization over a restrictive subspace of the possible control sequences, whereas DP has the capability of finding an optimal control sequence over a richer domain. The advantage of NLP is that it requires less processing resources and is therefore capable of providing approximate solutions to larger problems than the DP method. However, our results show that the DP solutions, because they are exact over a relatively long time horizon, can result in considerable reduction in cost. It is also found that for our SIS epidemic, the character of the NLP and DP solutions are considerably different.

Evaluation and analysis of daily atmospheric circulation patterns of the 500 HPA pressure field over the southwestern USA

Abstract Daily atmospheric circulation patterns (CPs) are defined and analyzed on the basis of the 500 hPa pressure field for the purpose of describing and then later generating local hydroclimatological variables such as precipitation under possible climate change over the southwestern USA. To obtain the CPs we first use a so-called objective or automated clustering method, namely, principal component analysis (PCA) coupled with K-means clustering algorithm. To obtain a set of CP types that are more distinguishable and so more useful for our purpose we follow two possible ways: (1) reduce subjectively the number of CPs from PCA coupled with K-means clustering analysis by aggregating the types on the basis of the precipitation producing characteristics, (2) perform K-means clustering analysis with fewer types. Thus we have three different cluster systems: original types from the result of PCA coupled with K-means clustering (8 or 9 types depending on the season), types from the K-means clustering analysis with fewer types (5 or 6 types in each season) and the subjectively aggregated types (3 types in any seasons). We compare them from the point of view of information content for precipitation modeling. An analysis is made for these types for comparison: statistical properties of these patterns are evaluated and analyzed using first observed data, and then General Circulation Model (GCM) outputs for 1 × CO 2 and 2 × CO 2 scenarios to estimate climate change effects. On the basis of the historical circulation pattern catalogue and observed precipitation data in Arizona, simple calculations are provided to find the “precipitation-producing” types of each system in each season. Three indices are evaluated using the same observed precipitation data from ten Arizona stations in order to measure objectively the information content of each type in the three cluster systems. It turns out that the larger number of types in a given season gives higher information content as we expected.

FUZZIFIED EFFECT OF ENSO AND MACROCIRCULATION PATTERNS ON PRECIPITATION: AN ARIZONA CASE STUDY

A fuzzy rule-based model (FRBM) is developed to analyse local monthly precipitation events conditioned on macrocirculation patterns and El Nino-Southern Oscillation (ENSO). A case study in Arizona is presented to illustrate the methodology. The inputs of the FRBM are those Southern Oscillation Index (SOI) values which have high absolute lag correlation with monthly Arizona precipitation and the frequencies of all circulation patterns (CPs) in a given month; the output of the model is an estimate of local monthly precipitation. After analysing the basic properties of the precipitation events, fuzzy rules are constructed, and then the results are interpreted and compared with those of a multivariate linear regression model. Using two goodness-of-fit criteria, first, the root mean squared error (RMSE) and then the correlation between the model results and the observed values, the FRBM is found to perform better than the multiple linear regression model for the Arizona case investigated. The results show that the FRBM can provide a good basis for future work to downscale general circulation model results to study local precipitation under climate change. The results of using only SOI lags or CP frequencies as inputs, which are also presented here, clearly show how much the results are improved using both inputs jointly instead of only one. Copyright

A multi-objective fuzzy classification of large scale atmospheric circulation patterns for precipitation modeling

A multi-objective fuzzy rule-based classification (MOFRBC) technique is applied in order to cluster and classify daily large scale atmospheric circulation patterns (CPs) and analyze the relationship between the CPs and local precipitation. The methodology is illustrated by means of an Arizona case study. For this purpose, three indices are calculated to measure the information content of the clustering method in terms of predicted precipitation. A thorough sensitivity analysis is provided to gain more understanding on the robustness of MOFRBC model. Furthermore, it is shown that extending the daily premises to two-day and three-day sequences of CPs improves the information content of the classification. The results are also compared with the original subjective clustering. For the Arizona case study MOFRBC seems to be a competitive technique with the advantage that the physical aspects can be better represented by fuzzy rules (which tend to mimic the human way of decision making) than by objective methods.

Lampshade Game for lean manufacturing

The purpose of this article is to introduce a new simulation game named the ‘Lampshade Game’ that can be helpful in educating students and industry professionals on lean manufacturing principles. The Lampshade Game is an active learning tool that is capable of demonstrating the advantages and disadvantages of some of the key principles of lean manufacturing (such as quality focus, pull system, flexible manufacturing and visual organisation) in comparison to craft and mass manufacturing by manually simulating the production of lampshades for each of the process types. The game enables the comparison of these three manufacturing methods using different operational and financial metrics such as inventory levels, manufacturing cycle time, customer fill rate, production yield and cost and profit. This article describes the game, its objectives, and shares experiences from several classroom implementations including an analysis of pre- and post-surveys.