Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover

Stochastic Boolean Function Evaluation is the problem of determining the value of a given Boolean function f on an unknown input x, when each bit of x_i of x can only be determined by paying an associated cost c_i. The assumption is that x is drawn from a given product distribution, and the goal is to minimize the expected cost. This problem has been studied in Operations Research, where it is known as "sequential testing" of Boolean functions. It has also been studied in learning theory in the context of learning with attribute costs. We consider the general problem of developing approximation algorithms for Stochastic Boolean Function Evaluation. We give a 3-approximation algorithm for evaluating Boolean linear threshold formulas. We also present an approximation algorithm for evaluating CDNF formulas (and decision trees) achieving a factor of O(log kd), where k is the number of terms in the DNF formula, and d is the number of clauses in the CNF formula. In addition, we present approximation algorithms for simultaneous evaluation of linear threshold functions, and for ranking of linear functions. Our function evaluation algorithms are based on reductions to the Stochastic Submodular Set Cover (SSSC) problem. This problem was introduced by Golovin and Krause. They presented an approximation algorithm for the problem, called Adaptive Greedy. Our main technical contribution is a new approximation algorithm for the SSSC problem, which we call Adaptive Dual Greedy. It is an extension of the Dual Greedy algorithm for Submodular Set Cover due to Fujito, which is a generalization of Hochbaum's algorithm for the classical Set Cover Problem. We also give a new bound on the approximation achieved by the Adaptive Greedy algorithm of Golovin and Krause

Safety Instrumented Bypass Management

PresentationProper management of Safety Instrumented Function (SIF) bypasses during process plant operation can be challenging and could compromise process safety if the SIF is bypassed longer than its allowable maximum time interval. Safety bypass procedures are usually written on site to comply with OSHA 1910.119 and IEC61511. However, in practice, safety bypass management can be difficult due to a lack of readily available process safety information, lack of operator awareness and the existence of a production throughput oriented culture. For many operating sites, process safety information (PSI) is only available in Process Hazard Analysis (PHA) reports. Commercial databases are available which display process safety information and make it readily available to operations and maintenance to properly implement and handle safety bypasses. An alternative approach is the creation of an in-house process safety database to provide easily-accessed process safety information. This paper will present a case-study on how TOTAL-Port Arthur Refinery developed and implemented such a system. The paper will include our flow chart for bypass approval, how we perform a bypass risk assessment and how we developed our SIS database. This SIS database has also proven useful for ‘operator training’ on the risks associated with the process unit and the available safeguards to manage those risks