Charles E. Noon

Matchup Scheduling with Multiple Resources, Release Dates and Disruptions

This paper considers the rescheduling of operations with release dates and multiple resources when disruptions prevent the use of a preplanned schedule. The overall strategy is to follow the preschedule until a disruption occurs. After a disruption, part of the schedule is reconstructed to match up with the preschedule at some future time. Conditions are given for the optimality of this approach. A practical implementation is compared with the alternatives of preplanned static scheduling and myopic dynamic scheduling. A set of practical test problems demonstrates the advantages of the matchup approach. We also explore the solution of the matchup scheduling problem and show the advantages of an integer programming approach for allocating resources to jobs.

A geographic information system-based modeling system for evaluating the cost of delivered energy crop feedstock.

The use of Geographic Information Systems (GIS) for understanding the geographic context of bioenergy supplies is discussed and a regional-scale, GIS-based modeling system for estimating potential biomass supplies from energy crops is described. While GIS models can capture geographic variation that may influence biomass costs and supplies, GIS models are not likely to handle uncertainty well and are often limited by the lack of spatially explicit data. The presented modeling system estimates the costs and environmental implications of supplying specified amounts of energy crop feedstock across a state. The system considers where energy crops could be grown, the spatial variability in their yield, and transportation costs associated with acquiring feedstock for an energy facility. The modeling system was used to estimate potential switchgrass costs and supplies in eleven US states. Transportation costs increased with increased facility demand and were lowest in Iowa, North Dakota and South Dakota and highest in South Carolina, Missouri, Georgia, and Alabama. Farmgate feedstock costs were lowest in Alabama, North Dakota and South Dakota and highest in Iowa and Nebraska. Across the eleven states, delivered feedstock costs ranged from

An efficient transformation of the generalized traveling salesman problem

33 to

GIS-based biomass resource assessment with BRAVO.

55/dry tonne to supply a facility requiring 100,000 tonne/yr. Delivered feedstock costs for a 630,000 tonne/yr facility ranged from

A Lagrangian Based Approach for the Asymmetric Generalized Traveling Salesman Problem

36 to

The effect of location and facility demand on the marginal cost of delivered wood chips from energy crops: A case study of the state of Tennessee

58/dry tonne.

Understanding the impact of variation in the delivery of healthcare services.

AbstractThe Generalized Traveling Salesman Problem (GTSP) is a useful model for problems involving decisions of selection and sequence. The problem is defined on a directed graph in which the nodes have been pregrouped into m mutually exclusive and exhaustive nodesets. Arcs are defined only between nodes belonging to different nodesets with each arc having an associated cost. The GTSP is the problem of finding a minimum cost m-arc directed cycle which includes exactly one node from each nodeset. In this paper, we show how to efficiently transform a GTSP into a standard asymmetric Traveling Salesman Problem (TSP) over the same number of nodes. The transformation allows certain routing problems which involve discrete alternatives to be modeled using the TSP framework. One such problem is the heterogenous Multiple Traveling Salesman Problem (MTSP) for which we provide a GTSP formulation.

GIS-Based Analysis of Marginal Price Variation with an Application in the Identification of Candidate Ethanol Conversion Plant Locations

Abstract The Biomass Resource Assessment Version One (BRAVO) system is a computer-based decision support system to assist the Tennessee Valley Authority (TVA) in estimating the costs for supplying wood fuel to any one of its 12 coal-fired power plants. The task of estimating wood fuel costs is particularly challenging due to the fact that wood fuel is not an established commodity. In contrast to conventional fossil fuels, geographic proximity to supply is a main determinant in the cost of wood fuel at a power plant. As a consequence, BRAVO is being developed within a Geographic Information System (GIS) platform. The GIS platform allows for the efficient analysis of transportation networks so that accurate estimates of hauling distances and costs can be determined. The system is designed to estimate the total purchase and transportation costs of three types of wood fuel under various levels of demand. This comprehensive system includes information on all possible wood fuel supply points, demand points and product movement costs. With this information, the system can estimate the total cost to supply a particular power plant with a desired quantity of wood fuel and can be used to evaluate “what if?” scenarios related to supply. An analysis of nine TVA plants revealed significant variation in biomass fuel resources within the region. This result suggests that a plant-based approach to biomass resource assessment is crucial to determining the economic feasibility of co-firing.

A Regional-Scale GIS-Based Modeling System for Evaluating the Potential Costs and Supplies of Biomass from Biomass Crops

This paper presents an optimal approach for the asymmetric Generalized Traveling Salesman Problem (GTSP). The GTSP is defined on a directed graph in which the nodes are grouped into m predefined, mutually exclusive and exhaustive sets with the arc set containing no intraset arcs. The problem is to find a minimum cost m-arc directed cycle which includes exactly one node from each set. Our approach employs a Lagrangian relaxation to compute a lower bound on the total cost of an optimal solution. The lower bound and a heuristically determined upper bound are used to identify and remove arcs and nodes which are guaranteed not to be in an optimal solution. Finally, we use an efficient branch-and-bound procedure which exploits the multiple choice structure of the node sets. We present computational results for the optimal approach tested on a series of randomly generated problems. The results show success on a range of problems with up to 104 nodes.

Spatial data visualization in healthcare: supporting a facility location decision via GIS-based market analysis

Cost-supply curves for delivered wood chips from short rotation woody crops were calculated for 21 regularly spaced locations spanning the state of Tennessee. These curves were used to systematically evaluate the combined effects of location and facility demand on wood chip feedstock costs in Tennessee. The cost-supply curves were developed using BRAVO, a GIS-based decision support system which calculates marginal cost of delivering wood chips to a specific location given road network maps and maps of farm-gate prices and supplies of wood chips from short rotation energy crops. Marginal costs of delivered chips varied by both facility location in the state and facility demand. Marginal costs were lowest in central Tennessee, unless the facility demand was greater than 2 700 000 dry Mg/y (3 000 000 dry t/y) in which case west Tennessee was the lowest cost region. Marginal costs rose rapidly with increasing facility demand in the mountainous eastern portion of the state. Transportation costs accounted for 18–29% of the delivered cost and ranged between