Challenges in using siliceous subfossils as a tool for inferring past water level and hydroperiod in Everglades marshes

Successfully rehabilitating drained wetlands through hydrologic restoration is dependent on defining restoration targets, a process that is informed by pre-drainage conditions, as well as understanding linkages between hydrology and ecosystem structure. Paleoecological records can inform restoration goals by revealing long-term patterns of change, but are dependent on preservation of biomarkers that provide meaningful interpretations of environmental change. In the Florida Everglades, paleohydrological hind-casting could improve restoration forecasting, but frequent drying of marsh soils leads to poor preservation of many biomarkers. To determine the effectiveness of employing siliceous subfossils in paleohydrological reconstructions, we examined diatoms, plant and sponge silico-sclerids from three soil cores in the central Everglades marshes. Subfossil quality varied among cores, but the abundance of recognizable specimens was sufficient to infer 1,000–3,000 years of hydrologic change at decadal to centennial resolution. Phytolith morphotypes were linked to key marsh plant species to indirectly measure fluctuations in water depth. A modern dataset was used to derive diatom-based inferences of water depth and hydroperiod (R2 = 0.63, 0.47; RMSE = 14 cm, 120 days, respectively). Changes in subfossil quality and abundances at centennial time-scales were associated with mid-Holocene climate events including the Little Ice Age and Medieval Warm Period, while decadal-scale fluctuations in assemblage structure during the twentieth century suggested co-regulation of hydrology by cyclical climate drivers (particularly the Atlantic Multidecadal Oscillation) and water management changes. The successful reconstructions based on siliceous subfossils shown here at a coarse temporal scale (i.e., decadal to centennial) advocate for their application in more highly resolved (i.e., subdecadal) records, which should improve the ability of water managers to target the quantity and variability of water flows appropriate for hydrologic restoration.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.