Daniel P. Connors

A queueing network model for semiconductor manufacturing

We develop an open queueing network model for rapid performance analysis of semiconductor manufacturing facilities. While the use of queueing models for performance evaluation of manufacturing systems is not new, our approach differs from others in the detailed ways in which we model the different tool groups found in semiconductor wafer fabrication, as well as the way in which we characterize the effect of rework and scrap on wafer lot sizes. As an application of the model, we describe a method for performing tool planning for semiconductor lines. The method is based on a marginal allocation procedure which uses performance estimates from the queueing network model to determine the number of tools needed to achieve a target cycle time, with the objective being to minimize overall equipment cost.

Workforce optimization: identification and assignment of professional workers using constraint programming

Matching highly skilled people to available positions is a high-stakes task that requires careful consideration by experienced resource managers. A wrong decision may result in significant loss of value due to understaffing, underqualification or overqualification of assigned personnel, and high turnover of poorly matched workers. While the importance of quality matching is clear, dealing with pools of hundreds of jobs and resources in a dynamic market generates a significant amount of pressure to make decisions rapidly. We present a novel solution designed to bridge the gap between the need for high-quality matches and the need for timeliness. By applying constraint programming, a subfield of artificial intelligence, we are able to deal successfully with the complex constraints encountered in the field and reach near-optimal assignments that take into account all resources and positions in the pool. The considerations include constraints on job role, skill level, geographical location, language, potential retraining, and many more. Constraints are applied at both the individual and team levels. This paper introduces the technology and then describes its use by IBM Global Services, where large numbers of service and consulting employees are considered when forming teams assigned to customer projects.

Applying supply chain optimization techniques to workforce planning problems

The IBM Research Division has developed the Resource Capacity Planning (RCP) Optimizer to support the Workforce Management Initiative (WMI) of IBM. RCP applies supply chain management techniques to the problem of planning the needs of IBM for skilled labor in order to satisfy service engagements, such as consulting, application development, or customer support. This paper describes two RCP models and presents two approaches to solving each of them. We also describe the motivation for using one approach over another. The models are built using the Watson Implosion Technology toolkit, which consists of a supply chain model, solvers for analysis and optimization, and an Application Programming Interface (API) for developing a solution. The models that we built solve two core resource planning problems, gap/glut analysis and resource action planning. The gap/glut analysis is similar to material requirements planning (MRP), in which shortages (gaps) and excesses (gluts) of resources are determined on the basis of expected demand. The goal of the resource action planning problem is to determine what resource actions to take in order to fill the gaps and reduce the gluts. The gap/glut analysis engine is currently deployed within the IBM service organization to report gaps and gluts in personnel.

Optimatch: Applying Constraint Programming to Workforce Management of Highly-skilled Employees

Today many companies face the challenge of matching highly-skilled professionals to high-end positions in large organizations and human deployment agencies. Unlike traditional Workforce Management problems such as shift scheduling, highly-skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work specifically focuses on the services industry, where much of the revenue comes from the assignment of highly professional workers. Here, non-accurate matches may result in significant monetary losses and other negative effects. We deal with very large pools of both positions and employees, where optimal decisions should be made rapidly in a dynamic environment. Since traditional Operations Research (OR) methods fail to answer this problem, we employ Constraint Programming (CP), a subfield of Artificial Intelligence with strong algorithmic foundations. Our CP model builds on new constraint propagators designed for this problem (but applicable elsewhere), as well as on information retrieval methods used for analyzing the complex text describing high-end professionals and positions. Optimatch, which is based on this technology and is being used by IBM services organizations, provides strong experimental results.

Scheduling Semiconductor Lines Using A Fluid Network Model

We present a novel meth.od f o r “what’s next” scheduhg of semiconductor manufacturing lines based on the determanistic fluid network model of Chen and Yao [l]. By “what’s next” scheduling we mean specifying the order i n which jobs are processed at each tool group. The approach we take here is first to determine how to allocate tool capacity among competing job types b y solving a series of linear and quadratic programming problems related to the fluid model and then, to specify a “what’s next” scheduling algorithm design,ed to track these capacity allocations. The primary advantage of our approach. is that it gives rise to a schedule which is based on global rath,er than local state informadion and which, is responsive to stochastic changes an the line including tool incapacitation events and operator unaraila,bility. In addition to describing the scheduling algorithm, we present some results about the fluid model that have important implications in the context of semiconductor manufacturing.

Workforce Analytics for the Services Economy

Central to the notion of services operation are concepts of labor and people – the deployment of knowledge, skills, and competences that one person or organization has for the benefit of another. In the new economics of services, the ability to manage skills and resources more effectively and efficiently is becoming the critical driver of success for any organization.

Discrete event simulation modeling of resource planning and service order execution for service businesses

In this paper, we present a framework for developing discrete-event simulation models for resource-intensive service businesses. The models simulate interactions of activities of demand planning of service engagements, supply planning of human resources, attrition of resources, termination of resources and execution of service orders to estimate business performance of resource-intensive service businesses. The models estimate serviceability, costs, revenue, profit and quality of service businesses. The models are also used in evaluating effectiveness of various resource management analytics and policies. The framework is aided by an information meta-model, which componentizes modeling objects of service businesses and allows effective integration of the components.

Optimatch: applying constraint programming to workforce management of highly skilled employees

Today many companies face the challenge of matching highly skilled professionals to high-end positions in large organisations and human deployment agencies. Unlike traditional workforce management problems, such as shift scheduling, highly skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work specifically focuses on the services industry, where much of the revenue comes from the assignment of highly professional workers. Here, non-accurate matches may result in significant monetary losses and other negative effects. We deal with very large pools of both positions and employees, where optimal decisions should be made rapidly in a dynamic environment. Since traditional operations research methods fail to answer this problem, we employ Constraint Programming (CP), a subfield of Artificial Intelligence with strong algorithmic foundations. Our CP model builds on new constraint propagators designed for this problem (but applicable elsewhere), as well as on ...

Managing workforce resource actions with multiple feedback control schemes

Demand disturbances in service businesses are typically managed by resource actions such as hiring, releasing and cross training of the workforce. The magnitudes of resource actions are often decided by estimating the discrepancy between the demand for services and the supply of workforce. However, naive feedback control of the resource actions by policies that equate the discrepancy to the control action can produce undesirable effects such as oscillation between hiring and releasing of workforce, and amplified oscillation through the stages of the service processes. Effective combination of multiple feedback control schemes can produce desirable policies of workforce resource actions. In this work, we study application of control theoretic principles in managing resource actions to see how various feedback control schemes can improve costs, utilization and stability of workforce.

Collateral damage-free debridement using 193nm ArF laser

Burn eschar and other necrotic areas of the skin and soft tissue are anhydrous compared to the underlying viable tissue. A 193 nm ArF excimer laser, emitting electromagnetic radiation at 6.4 eV at fluence exceeding the ablation threshold, will debride such necrotic areas. Because such radiation is strongly absorbed by aqueous chloride ions through the nonthermal process of electron photodetachment, debridement will cease when hydrated (with chloride ions) viable tissue is exposed, avoiding collateral damage to this tissue. Such tissue will be sterile and ready for further treatment, such as a wound dressing and/or a skin graft.