A design process is traditionally viewed as a sequential process model from the formulation of the problem to the synthesis of solutions. Simon (1981) regards design as a state-space search where a problem leads to the solution. To be more practical, there are many versions of solution generated during design, where each current one is an improvement over the previous one. This kind of synthesi...

The topics of this paper are state space transformations and the (partial) state space problem in the max algebra, which is one of the modeling frameworks that can be used to model discrete event systems. We use the fact that a system of multivariate max-algebraic polynomial equations can be transformed into an Extended Linear Complementarity Problem to perform state space transformations and t...

Business process models are required to be in line with frequently changing regulations, policies, and environments. In the field of intelligent modeling, organisations concern automated business process compliance checking as the manual verification is a timeconsuming and inefficient work. There exist two key issues for business process compliance checking. One is the definition of a business ...

For a finite state Markov process and a finite collection {Γk, k ∈ K} of subsets of its state space, let τk be the first time the process visits the set Γk. We derive explicit/recursive formulas for the joint density and tail probabilities of the stopping times {τk, k ∈ K}. The formulas are natural generalizations of those associated with the jump times of a simple Poisson process. We give a nu...

The objective of this exercise is to demonstrate how a steady state gain matrix, K can be calculated from a linearized state space dynamic process model. The approach used has been published by Arkun and Downs [1], and it is aimed at handling cases where integrating elements, e.g. liquid levels, are present. The dynamic approach is applied to the Tennessee Eastman process, and the relative gain...

The doctoral thesis addresses the existing gap between business process models and states of business objects. Existing modelling methods such as BPMN and ArchiMate lack an explicitly declarative approach for capturing states of business objects and laws of state transitions. This gap hinders the compliance of business process models with regulations imposed internally or externally, and can re...

In this paper, we advocate the use of reactive robots in industrial process control and production management. It is explained why reactive robots are well-suited to modern industrial applications that necessitate a high degree of autonomy and reactivity to unforeseen events and an illustrative example is studied in depth. A reactive robot whose role is to regulate the demands of a set of servi...

Little research has been conducted so far on causes for errors in business process models. In this paper we investigate on how mainly domain independent factors such as the size or complexity of models influence errors observed in a wide range of existing business process models. In particular, we provide a set of six metrics presumably related to the comprehensibility of both the process model...