Supply modelling of rail networks : toward a routing/makeup model

Includes bibliographical references.Supported in part by the U.S. Department of Transportation, Transportation Advanced Research Program (TARP) DOT-TSC-1058by Arjang A. Assad

Simulation and optimization techniques applied in semiconductor assembly and test operations

The importance of back-end operations in semiconductor manufacturing has been growing steadily in the face of higher customer expectations and stronger competition in the industry. In order to achieve low cycle times, high throughput, and high utilization while improving due-date performance, more effective tools are needed to support machine setup and lot dispatching decisions. In previous work, the problem of maximizing the weighted throughput of lots undergoing assembly and test (AT), while ensuring that critical lots are given priority, was investigated and a greedy randomized adaptive search procedure (GRASP) developed to find solutions. Optimization techniques have long been used for scheduling manufacturing operations on a daily basis. Solutions provide a prescription for machine setups and job processing over a finite the planning horizon. In contrast, simulation provides more detail but in a normative sense. It tells you how the system will evolve in real time for a given demand, a given set of resources and rules for using them. A simulation model can also accommodate changeovers, initial setups and multi-pass requirements easily. The first part of the research is to show how the results of an optimization model can be integrated with the decisions made within a simulation model. The problem addressed is defined in terms of four hierarchical objectives: minimize the weighted sum of key device shortages, maximize weighted throughput, minimize the number of machines used, and minimize makespan for a given set of lots in queue, and a set of resources that includes machines and tooling. The facility can be viewed as a reentrant flow shop. The basic simulation was written in AutoSched AP (ASAP) and then enhanced with the help of customization features available in the software. Several new dispatch rules were developed. Rule_First_setup is able to initialize the simulation with the setups obtained with the GRASP. Rule_All_setups enables a machine to select the setup provided by the optimization solution whenever a decision is about to be made on which setup to choose subsequent to the initial setup. Rule_Hotlot was also proposed to prioritize the processing of the hot lots that contain key devices. The objective of the second part of the research is to design and implement heuristics within the simulation model to schedule back-end operations in a semiconductor AT facility. Rule_Setupnum lets the machines determine which key device to process according to a machine setup frequency table constructed from the GRASP solution. GRASP_asap embeds a more robust selection features of GRASP in the ASAP model through customization. This allows ASAP to explore a larger portion of the feasible region at each decision point by randomizing machine setups using adaptive probability distributions that are a function of solution quality. Rule_Greedy, which is a simplification of GRASP_asap, always picks the setup for a particular machine that gives the greatest marginal improvement in the objective function among all candidates. The purpose of the third part of the research is to statistically validate the relative effectiveness of our top six dispatch rules by comparing their performance on 30 real and randomly generated data sets. Using both GRASP and our ASAP discrete event simulation model, we have (1) identified the general order of dispatch rule performance, (2) investigated the impact of having setups installed on machines at time zero on rule performance, (3) determined the conditions under which restricting the maximum number of changeover affects the rule performance, and (4) studied the factors that might simultaneously affect rule performance with the help of a common random numbers experimental design. In the analysis, the first two objectives, weighted key device shortages and weighted throughput, are used to measure outcomes.Operations Research and Industrial Engineerin

Homeostatic control : the utilitycustomer marketplace for electric power

A load management system is proposed in which the electric utility customer controls his on-site power demand to coincide with the lowest possible cost of power generation. Called Homeostatic Control, this method is founded on feedback between the customer and the utility and on customer independence. The utility has no control beyond the customer's meter. Computers located at the customer's site are continuously fed data on weather conditions, utility generating costs, and demand requirements for space conditioning, lighting, and appliances. The customer then directs the computer to schedule and control the power allotted for these functions. On-site generation by the customer can be incorporated in the system. It is argued that homeostatic control is technically feasible, that the level of control equipment sophistication can be adapted to the benefits received by the customer, that such a system would encourage the use of customer-site energy storage and energy conservation equipment, and that it represents a realistic method for allowing the customer to decide how he will use electric power during an era of increasing costs for power generation. (LCL

Three essays on delay management for passenger rail services

Railways are confronted with several problems in their daily business. One of these operational problems is delay management. Therein the question of whether a train should wait for a delayed feeder train or depart on time is addressed. Answering this question is not trivial since the determined wait-depart decision may cause serious consequences. While the majority of models in the literature usually take the decision by aiming for minimizing disturbances in the operating procedure, delay management focuses on the impact for passengers. By minimizing passenger delay, delay management differs from the other problems on the operational level and leads to different recommendations for dispatchers. This thesis puts the scope on railway delay management and its impacts for passengers. It consists of three essays: a literature review on delay management and two models that advance the research in this field. In the literature review, a new classification scheme for operational problems in railways is developed. Literature in delay management and influence from delay management on neighboring areas are discussed. The second essay proposes a stochastic dynamic programming approach taking the dynamic nature of delays and uncertainty into account. Evaluating potential recourse actions derives policies for taking dispatching decisions. The third essay considers the capacity of trains in the decision making process. Rerouting of passengers for broken connections is further assumed and spill effects for passenger streams are measured. A nonlinear model is developed and solved by linearizing it exactly and heuristically. Both approaches, from the second and third essay, are evaluated in a numerical study on real-world data from the German railway provider Deutsche Bahn. Germany possesses a rather complex and massive railway network that will require further decision support and future research

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

A Methodology to Identify Stranded Generation Facilities and Estimate Stranded Costs for Louisiana\u27s Electric Utility Industry.

The electric utility industry in the United States is currently experiencing a new and different type of growing pain. It is the pain of having to restructure itself into a competitive business. Many industry experts are trying to explain how the nation as a whole, as well as individual states, will implement restructuring and handle its numerous transition problems. . One significant transition problem for federal and state regulators rests with determining a utility\u27s stranded costs. Stranded generation facilities are assets which would be uneconomic in a competitive environment or costs for assets whose regulated book value is greater than market value. At issue is the methodology which will be used to estimate stranded costs. The two primary methods are known as Top-Down and Bottom-Up. The Top-Down approach simply determines the present value of the losses in revenue as the market price for electricity changes over a period of time into the future. The problem with this approach is that it does not take into account technical issues associated with the generation and wheeling of electricity. The Bottom-Up approach computes the present value of specific strandable generation facilities and compares the resulting valuations with their historical costs. It is regarded as a detailed and difficult, but more precise, approach to identifying stranded assets and their associated costs. This dissertation develops a Bottom-Up quantitative, optimization-based approach to electric power wheeling within the state of Louisiana. It optimally evaluates all production capabilities and coordinates the movement of bulk power through transmission interconnections of competing companies in and around the state. Sensitivity analysis to this approach is performed by varying seasonal consumer demand, electric power imports, and transmission inter-connection cost parameters. Generation facility economic dispatch and transmission interconnection bulk power transfers, specific to each set of parameters, lead to the identification of stranded generation facilities. Stranded costs of non-dispatched and uneconomically dispatched generation facilities can then be estimated to indicate, arguably, the largest portion of restructuring transition costs as the industry is transformed from its present monopolistic structure to a competitive one