Hybrid make-to-stock and make-to-order systems: a taxonomic review

Production planning and control (PPC) systems that employ aspects from both make-to-order (MTO) and make-to-stock (MTS) production control are known as hybrid MTS/MTO systems. While both MTO and MTS separately have been studied extensively, their combined use has received less attention. However, the literature on this topic is growing and this paper shows that the review performed in this paper is an important addition to the field. We categorise relevant literature according to a novel taxonomy and show that hybrid MTS/MTO production control can be used in different contexts. In addition, an overview of the modelling techniques and methods used in these papers is provided. Based on the reviewed literature, relevant research questions and directions for future research are identified. Finally, it is shown that hybrid MTS/MTO production control is prevalent in practice by discussing research with industrial applications. The paper contains an overview of research on hybrid MTS/MTO production control to be used as reference for researchers active in the field, and provides managerial insights and directions for future research on this topic.     

Deductive verification of alternating systems

Alternating systems are models of computer programs whose behavior is governed by the actions of multiple agents with, potentially, different goals. Examples include control systems, resource schedulers, security protocols, auctions and election mechanisms. Proving properties about such systems has emerged as an important new area of study in formal verification, with the development of logical frameworks such as the alternating temporal logic ATL*. Techniques for model checking ATL* over finite-state systems have been well studied, but many important systems are infinite-state and thus their verification requires, either explicitly or implicitly, some form of deductive reasoning. This paper presents a theoretical framework for the analysis of alternating infinite-state systems. It describes models of computation, of various degrees of generality, and alternating-time logics such as ATL* and its variations. It then develops a proof system that allows to prove arbitrary ATL* properties over these infinite-state models. The proof system is shown to be complete relative to validities in the weakest possible assertion language. The paper then derives auxiliary proof rules and verification diagrams techniques and applies them to security protocols, deriving a new formal proof of fairness of a multi-party contract signing protocol where the model of the protocol and of the properties contains both game-theoretic and infinite-state (parameterized) aspects.     

New interval Bayesian models for software reliability based on non-homogeneous Poisson processes

We propose a new class of models for software reliability based on known models employing non-homogeneous Poisson processes, e.g., Musa-Okomoto and Goel-Okomoto models. We show that the general idea of model design is in a combined application of imprecise Bayesian inference and the maximum likelihood approach. We show examples where proposed models show better reliability prediction quality compared to the known ones.     

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief–desire–intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods—such as Hebbian strengthening of links between co-active elements, and belief–intention learning of task-specific relationships—modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.     

Verifying Temporal Properties of Reactive Systems: A STeP Tutorial

We review a number of formal verification techniques supported by STeP, the Stanford Temporal Prover, describing how the tool can be used to verify properties of several versions of the Bakery Mutual exclusion algorithm for mutual exclusion. We verify the classic two-process algorithm and simple variants, as well as an atomic parameterized version. The methods used include deductive verification rules, verification diagrams, automatic invariant generation, and finite-state model checking and abstraction.     

Low-frequency quadrature mode birdcage resonator

The birdcage resonator is frequently used in conventional MRI because of its excellent attributes. Its use in low-field MRI is restricted to field strengths higher than, for example, 0.1 T, dependent on the size of the coil. This is because of the intrinsically low inductance value of the birdcage coils. Furthermore, the sensitivity of the birdcage at low field strengths is significantly lower when compared to, for example, the solenoid. Both problems can be overcome with the multiturn technique and a novel wound birdcage coil. The quadrature mode wound birdcage coil presented in this paper can be used at frequencies as low as 100 kHz. Its sensitivity is also increased when compared to the conventional strip-ring birdcage. Homogeneity effective volume, and methods to increase the resonator bandwidth to match the signal bandwidth are left intact. The latter is a typical low-field problem.     

Bayesian graphical models for software testing

This paper describes a new approach to the problem of software testing. The approach is based on Bayesian graphical models and presents formal mechanisms for the logical structuring of the software testing problem, the probabilistic and statistical treatment of the uncertainties to be addressed, the test design and analysis process, and the incorporation and implication of test results. Once constructed, the models produced are dynamic representations of the software testing problem. They may be used to drive test design, answer what-if questions, and provide decision support to managers and testers. The models capture the knowledge of the software tester for further use. Experiences of the approach in case studies are briefly discussed     

Collecting Statistics over Runtime Executions

By collecting statistics over runtime executions of a program we can answer complex queries, such as “what is the average number of packet retransmissions” in a communication protocol, or “how often does process P₁ enter the critical section while process P₂ waits” in a mutual exclusion algorithm. We present an extension to linear-time temporal logic that combines the temporal specification with the collection of statistical data. By translating formulas of this language to alternating automata we obtain a simple and efficient query evaluation algorithm. We illustrate our approach with examples and experimental results.