Methodology for the Assessment of Imprecise Multi-State System Availability

Most existing studies of a system’s availability in the presence of epistemic uncertainties assume that the system is binary. In this paper, a new methodology for the estimation of the availability of multi-state systems is developed, taking into consideration epistemic uncertainties. This paper formulates a combined approach, based on continuous Markov chains and interval contraction methods, to address the problem of computing the availability of multi-state systems with imprecise failure and repair rates. The interval constraint propagation method, which we refer to as the forward–backward propagation (FBP) contraction method, allows us to contract the probability intervals, keeping all the values that may be consistent with the set of constraints. This methodology is guaranteed, and several numerical examples of systems with complex architectures are studied.

Job Matching under Constraints

Studying job matching in a Kelso-Crawford framework, we consider arbitrary constraints imposed on sets of doctors that a hospital can hire. We characterize all constraints that preserve the substitutes condition (for all revenue functions that satisfy the substitutes condition), a critical condition on hospitals’ revenue functions for well-behaved competitive equilibria. A constraint preserves the substitutes condition if and only if it is a “generalized interval constraint,” which specifies the minimum and maximum numbers of hired doctors, forces some hires, and forbids others. Additionally, “generalized polyhedral constraints” are precisely those that preserve the substitutes condition for all “group separable” revenue functions. (JEL C78, D47, I11, J23, J41, J44)

Interval Constraint Satisfaction and Optimization for Biological Homeostasis and Multistationarity

Homeostasis occurs in a biological or chemical system when some output variable remains approximately constant as one or several input parameters change over some intervals. We propose in this paper a new computational method based on interval techniques to find species in biochemical systems that verify homeostasis. A somehow dual and equally important property is multistationarity, which means that the system has multiple steady states and possible outputs, at constant parameters. We also propose an interval method for testing multistationarity. We have tested homeostasis, absolute concentration robustness and multistationarity on a large collection of biochemical models from the Biomodels and DOCSS databases. The codes used in this paper are publicly available at: https://github.com/Glawal/IbexHomeo.

Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.