Ewa Dudek-Dyduch

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.

Substitution Tasks Method for Discrete Optimization

The aim of the paper is to present a novel heuristic optimization method for discrete dynamic optimization problems. The method has been named substitution tasks method (ST method). According to the method, a solution is generated by means of sequence of dynamically created local optimization tasks so-called substitution tasks. The method is based on formal algebraic-logical meta model of multistage decision process (ALMM of MDP), that is given in the paper. The paper presents a formal approach for designing constructive algorithms that are based on the method. A general idea of creating substitution tasks for different optimization problems is given. Then creation of substitution tasks, based on automatic analisys of set of non-admissible states is proposed. To illustrate the presented ideas, a scheduling algorithm for a particular NP-hard problem is given and results of computer experiments are presented.

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.

Algebraic Logical Meta-Model of Decision Processes - New Metaheuristics

The paper presents a formal approach to developing new heuristic methods for finding solutions of discrete optimization problems. The presented approach is based on algebraic-logical meta-model of multistage decision process (ALMM of DMP) that has been developed by the author. Definitions are provided for two deterministic classes of multistage decision processes: common multistage decision processes (cMDP) and multistage dynamic decision processes (MDDP). The paper presents some part of research results pertaining to heuristic methods utilising ALMM of MDP. It lays out a three stage concept of heuristic method synthesis involving local optimization together with two heuristic methods based on the said concept: Machine Learning Based on ALMM of DMP and the Substitute Task Method.

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.

Modeling Manufacturing Processes with Disturbances - A New Method Based on Algebraic-Logical Meta-Models

The paper discusses modeling and control of discrete manufacturing processes (DMP) with disturbances of various types: machine failures, quality defects, unexpected additional orders etc. A novel formal modeling method is presented for DMP with disturbances. The method is based on formal description of DMP given by the algebraic logical meta model (ALMM). It is called two stage AL model transformation method (2SALMT method). Method application is shown herein for IT systems managing manufacturing on both operational and tactical levels. The paper also shows how 2SALMT method can be applied for modeling scheduling problems with disturbances such as machine failure.

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.

Knowledge based model for scheduling in failure modes

The paper presents an artificial intelligence approach to simulation and scheduling discrete manufacturing processes (DMP) in a failure modes. It presents a knowledge based model of DMP for the failure mode and a new conception of solving of failure control problem. The solving method is based on formal description of DMP given by the algebraic-logical meta model (ALMM). Authors propose the application of FMEA method to determine RPN coefficients that allows to choose simulation experiments for failure modes.

From Homogeneous Network to Neural Nets with Fractional Derivative Mechanism

The paper refers to ANNs of the feed-forward type, homogeneous within individual layers. It extends the idea of network modelling and design with the use of calculus of finite differences proposed by Dudek-Dyduch E. and then developed jointly with Tadeusiewicz R. and others. This kind of neural nets was applied mainly to different features extraction i.e. edges, ridges, maxima, extrema and many others that can be defined with the use of classic derivative of any order and their linear combinations. Authors extend this type ANNs modelling by using fractional derivative theory. The formulae for weight distribution functions expressed by means of fractional derivative and its discrete approximation are given. It is also shown that the application of discrete approximation of fractional derivative of some base functions allows for modelling the transfer function of a single neuron for various characteristics. In such an approach smooth control of a derivative order allows to model the neuron dynamics without direct modification of the source code in IT model. The new approach presented in the paper, universalizes the model of the considered type of ANNs.

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.