Edyta Kucharska

Learning method for co-operation

The aim of the paper is to present a new heuristic method for determining intelligent co-operation at project realization. The method uses local optimization task of a special form and is based on learning idea. The paper presents a formal approach to creation of constructive algorithms that use a local optimization and are based on a formal definition of multistage decision process. It also proposes a general conception of creation local optimization tasks for different problems as well as a conception of local optimization task modification on basis of gathering information. To illustrate the conceptions, the learning algorithm for NP-hard scheduling problem is presented as well as results of computer experiments.

ALMM Solver - A Tool for Optimization Problems

The aim of our paper is to present the concept and structure of a software tool named the ALMM Solver. The goal of the solver is to generate solutions for discrete optimization problems, in particular for NP-hard problems. The solver is based on Algebraic Logical Meta-Model of Multistage Decision Process (ALMM of MDP) methodology, which is briefly described in the paper. Functionality and modular structure of the ALMM Solver is presented. SimOpt, the core module of the solver, is described in detail. Some possible future advances regarding the solver are also given.

Idea of switching algebraic-logical models in flow-shop scheduling problem with defects

The paper presents a new approach, based on an algebraic-logical meta-models (ALMM), to solving NP - hard manufacturing problems. There is considered the flow-shop manufacturing problem with time limits, the quality control, removing of manufacturing defects on an additional repair machine and retreatment of task in technological route. Proposed solution consist in switching of original algebraic-logical model to model with modified parameters when quality defects appear. In particular algebraic-logical model of flow-shop manufacturing problem with time limits and modified model with additional repair machine are proposed. Furthermore, the switching function from original model into modified model is given.

Extended Learning Method for Designation of Co-operation

The aim of the paper is to present a new machine learning method for determining intelligent co-operation at project realization. The method uses local optimization task of a special form and is based on learning idea. Additionally, the information gathered during a searching process is used to prune non-perspective solutions. The paper presents a formal approach to creation of constructive algorithms that use a sophisticated local optimization and are based on a formal definition of multistage decision process. It also proposes a general conception of creation local optimization tasks for different problems as well as a conception of local optimization task modification on basis of acquired information. To illustrate the conceptions, the learning algorithm for NP-hard scheduling problem is presented as well as results of computer experiments.

ALMM Solver: The Idea and the Architecture

The ALMM Solver is a software tool which aim is generating solutions for discrete optimization problems, in particular for NP-hard problems. The idea of the solver is based on Algebraic Logical Meta-Model of Multistage Decision Process (ALMM of MDP). The aim of the paper is to present the architecture of the ALMM Solver and to describe requirements regarding the solver, in particular non-functional ones. SimOpt, the core module of the solver, is described in detail. The practices, design patterns and principles, that was used to ensure the best quality of the solver software, are mentioned in the paper.

ST Method-Based Algorithm for the Supply Routes for Multilocation Companies Problem

This paper presents an optimization algorithm, based on the substitution tasks method (ST method). It is designed for the supply routes for multilocation companies problem. This problem is NP-hard and belongs to the class of problems for which it is impossible to establish all values and parameters a priori. The substitution tasks method uses a mathematical model of multistage decision process named algebraic-logical meta-model (ALMM). This method allows one to create many algorithms, also automatically. A formal algebraic-logical model of the problem and an algorithm based on ST method are introduced in this paper. Results of computer experiments are presented as well.

Cellular Automata approach for parallel machine scheduling problem

The aim of the paper is to present a new approach based on the Cellular Automata technique for a specific class of scheduling problems with parallel machines (in which some important parameter values cannot be determined a priori). The problem domain is represented by an asynchronous non-homogeneous cellular automaton. In addition, the division of the method into three levels is introduced. Inseparable use of simulation, optimization and result levels, is proposed. To illustrate our proposition, the optimization problem of drilling tunnels in a given area is considered. A number of simulation experiments were performed involving different instances of the problem and the results are presented and discussed in the paper.

Unifying business concepts for SMEs with Prosecco ontology

Knowledge management in business information systems often requires a unified dictionary of business concepts, that allows for a transparent integration of such systems. Thanks to it sharing the conceptualization between users becomes possible, and better decision support facilities can be provided. The Prosecco project is a research and development project aims to address the needs and constraints of small and medium enterprises by designing methods that will significantly improve BPM systems. In this paper we focus on the development of ontology-based mechanisms allowing for creating taxonomies of business logic concepts unifying system objects. Building a taxonomy of business concepts shared by number of SMEs targeted in the project and then turning it into a formalized ontology integrating the software components is a major challenge. The paper demonstrates how this ontology is used to unify vocabulary of business processes and rules. The original contribution of this research discussed in the paper is the design and implementation of the ontology, and the demonstration of its practical use in the system.

ALMM-based switching method for FSS problem with defects.

The paper presents a method based on algebraic-logical meta-model for solving flow-shop scheduling problem with time limits, the quality control, removing of manufacturing defects on an additional repairing machine and re-treatment of job in technological route. The appearance of the defect is unexpected event and would influence the total job processing time when the schedule is executed. Since a priori quality control results are unknown we deal with stochastic uncertainties. To be exact, the job processing time is stochastic. Flow shop scheduling problems are NP-hard and tend to become more complex when unexpected events are taken into consideration. The presented method is called a switching of algebraic-logical models. The switching method allows to present a problem by using models of subproblems and switching function. The switching function specifies the rules of using models of subproblems and these models are simplest then a model of initial problem. This method allows to solve dynamical nondeterministic problems.

ALMM-Based Methods for Optimization Makespan Flow-Shop Problem with Defects

The paper presents a new algorithm for solving flow-shop manufacturing problem with time limits, the quality control, removing of manufacturing defects (quality lack) on an additional repair machine and re-treatment of task in technological route. Because an appearance of the defect is an unexpected event the quality control results as well as a job processing time are not known a priori. Thus, we deal with stochastic uncertainties. Our algorithm is based on algebraic-logistic meta-model (ALMM) methodology and is a combination of the searching algorithm with the special local criterion and the method of algebraic-logical models switching. The searching algorithm has been determining the deterministic problems solution on the basis of discrete process simulation until now. Switching method presents the problem by using two simple models and switching function, which specifies the rules of using these models and is used to model the removal of the manufacturing defects on an additional repair machine. The proposed approach was tested and the results of computer experiments are presented in the paper.