John E. Kobza

Optimizing the performance of a surface mount placement machine

Process planning is an important and integral part of effectively operating a printed circuit board (PCB) assembly system. A PCB assembly system generally consists of different types of placement machines, testing equipment, and material handling equipment. This research develops a new solution approach to determine the component placement sequence and feeder arrangement for a turret style surface mount-placement machine often used in PCB assembly systems. This solution approach can be integrated into a process planning system to reduce assembly time and improve productivity. The algorithm consists of a construction procedure that uses a set of rules to generate an initial component placement sequence and feeder arrangement along with an improvement procedure to improve the initial solution. An industrial case study conducted at Ericsson, Inc., using a Fuji CP4-3 machine and actual PCB data, is presented to demonstrate the performance of the proposed solution approach. The solutions obtained using the proposed solution approach are compared to those obtained using state of the art PCB assembly process optimization software. For all PCBs in the case study, the proposed solution approach yielded lower placement times than the commercial software, thus generating additional valuable production capacity. This research is applicable for both researchers and practitioners in printed circuit board assembly systems.

Analyzing the Cost of Screening Selectee and Non-Selectee Baggage

Determining how to effectively operate security devices is as important to overall system performance as developing more sensitive security devices. In light of recent federal mandates for 100% screening of all checked baggage, this research studies the trade-offs between screening only selectee checked baggage and screening both selectee and non-selectee checked baggage for a single baggage screening security device deployed at an airport. This trade-off is represented using a cost model that incorporates the cost of the baggage screening security device, the volume of checked baggage processed through the device, and the outcomes that occur when the device is used. The cost model captures the cost of deploying, maintaining, and operating a single baggage screening security device over a one-year period. The study concludes that as excess baggage screening capacity is used to screen non-selectee checked bags, the expected annual cost increases, the expected annual cost per checked bag screened decreases, and the expected annual cost per expected number of threats detected in the checked bags screened increases. These results indicate that the marginal increase in security per dollar spent is significantly lower when non-selectee checked bags are screened than when only selectee checked bags are screened.

Modeling aviation baggage screening security systems: a case study

Aviation security protects vital national interests, as well as passengers and aircraft. Key components of an aviation security system include baggage and passenger screening devices and operations. Determining how and where to assign (deploy) such devices can be quite challenging. Moreover, even after such systems are in place, it can be difficult to measure their effectiveness. This paper describes how discrete optimization models can be used to address these questions, based on three performance measures that quantify the effectiveness of airport baggage screening security device systems. These models are used to solve for optimal airport baggage screening security device deployments considering the number of passengers on a set of flights who have not been cleared using a security risk assessment system in use by the Federal Aviation Administration (i.e., passengers whose baggage is subjected to screening), the number of flights in this set, and the size of the aircraft for such flights. Several examples are provided to illustrate these results, including an example that uses data available from the Official Airline Guide.

A cost-benefit analysis of alternative device configurations for aviation-checked baggage security screening.

The terrorist attacks of September 11, 2001 have resulted in dramatic changes in aviation security. As of early 2003, an estimated 1,100 explosive detection systems (EDS) and 6,000 explosive trace detection machines (ETD) have been deployed to ensure 100% checked baggage screening at all commercial airports throughout the United States. The prohibitive costs associated with deploying and operating such devices is a serious issue for the Transportation Security Administration. This article evaluates the cost effectiveness of the explosive detection technologies currently deployed to screen checked baggage as well as new technologies that could be used in the future. Both single-device and two-device systems are considered. In particular, the expected annual direct cost of using these devices for 100% checked baggage screening under various scenarios is obtained and the tradeoffs between using single- and two-device strategies are studied. The expected number of successful threats under the different checked baggage screening scenarios with 100% checked baggage screening is also obtained. Lastly, a risk-based screening strategy proposed in the literature is analyzed. The results reported suggest that for the existing security setup, with current device costs and probability parameters, single-device systems are less costly and have fewer expected number of successful threats than two-device systems due to the way the second device affects the alarm or clear decision. The risk-based approach is found to have the potential to significantly improve security. The cost model introduced provides an effective tool for the execution of cost-benefit analyses of alternative device configurations for aviation-checked baggage security screening.

Designing optimal aviation baggage screening strategies using simulated annealing

Terrorist activities are a critical threat to domestic aviation security. Current aviation security models created to address this threat are descriptive rather than prescriptive; they evaluate a given baggage screening strategy rather than identify optimal baggage screening strategies. Moreover, these models only incorporate costs associated with purchasing and operating baggage screening security devices. This research introduces a comprehensive cost function that not only includes direct costs associated with the purchase and operation of baggage screening security devices, but also includes indirect costs associated with device errors. A methodology is presented to determine the best selection of baggage screening security devices that minimizes the expected annual total cost of a baggage screening strategy. Computational experiments with this methodology are presented.

Integer programming models and analysis for a multilevel passenger screening problem

Designing effective aviation security systems has become a problem of national interest and concern. Passenger prescreening is an important component of aviation security. Effectively using passenger prescreening information to develop screening strategies can be quite challenging. Moreover, it can be difficult to measure the effectiveness of such systems after they are in place. To address these issues, this paper introduces the Multilevel Passenger Screening Problem (MPSP). In MPSP, a set of classes are available for screening passengers, each of which corresponds to several device types for passenger screening, where each device type has an associated capacity and passengers are differentiated by their perceived risk levels. The objective of MPSP is to use prescreening information to determine the passenger assignments that maximize the total security subject to capacity and assignment constraints. MPSP is illustrated with examples that incorporate flight schedule and passenger volume data extracted from the Official Airline Guide.

Assessing the impact of deterrence on aviation checked baggage screening strategies

This paper analyses checked baggage screening strategies that incorporate the effects of deterrence on explosive detection systems (EDSs) deployed at airports. Cost models for these strategies are presented that incorporate the cost of purchasing, operating, and maintaining an EDS, the number of checked bags available to be screened, and the numbers of selectees and non-selectees checked bags actually screened over a one-year period. The model also includes the effect of deterrence on the level of threat at an airport. The cost models provide a quantitative tool to assess the strategy of 100% screening of all checked bags, as set forth by the USA Aviation and Transportation Security Act. Comparing the expected direct cost per expected prevented attack to the expected cost of an aviation terrorist incident provides an indication of the cost effectiveness of 100% checked bag screening.

A Detection Theoretic approach to Modeling Aviation Security Problems using the Knapsack Problem

Designers, operators and users of multiple-device, access-control security systems are challenged by the false alarm, false clear tradeoff. Given a particular access control security system, and a prescribed false clear standard, there is an optimal (minimal) false alarm rate that can be achieved. The objective of this research is to develop a new methodology for determining this false alarm rate. A static grid estimation procedure is used to estimate the joint conditional probability density functions for the security device responses. The concept of a system response function is introduced and the problem of determining a system response function that minimizes the false alarm rate, while meeting the false clear standard, is formulated as a decision problem and proven to be NP-complete. A Greedy Algorithm and a Dynamic Programming algorithm are presented to address this problem. Computational results using simulated security data are reported. These results are compared to analytical results obtained for a pre-specified system response function form. Directions for future research are also discussed.

Queueing network analysis: concepts, terminology, and methods

Queueing network analysis can be a valuable tool to analyze network models. However, the vast number and diverse nature of the tools available to analyze a problem can often leave the uninitiated frustrated or bewildered--awash in concepts, terminology, and methods not encountered elsewhere. As a primer for queueing network analysis, this paper emphasizes essential concepts and terminology. Selection of analytical methods based on the type of queueing network is discussed. Analytical methods are demonstrated using numerous examples and references for further study into advanced analysis are included throughout.

A stochastic model of empty-vehicle travel time and load request service time in light-traffic material handling systems

Empty-vehicle travel time plays an important role in the design and control of automated guided vehicle systems (AGVSs). However, many analytical models of these systems assume the amount of empty-vehicle travel time is the same as the loaded-vehicle travel time. This paper examines empty-vehicle travel time in AGVSs with low traffic intensity. The model uses a discrete-time Markov chain based on vehicle location and represents dispatching rules in the one-step transition matrix. The model can be used to compute moments and cumulative probabilities for the empty-vehicle travel time. Coupled with the loaded-vehicle travel time and the loading/unloading time, similar results can be obtained for the time to service a load request.