Robert Bucki

Modelling Decision-Making Processes in the Management Support of the Manufacturing Element in the Logistic Supply Chain

This paper highlights the problems of mathematical modelling for a specific element of the logistic supply chain, that is, the manufacturing system. The complex manufacturing system consisting of a determined number of parallel subsystems is modelled. The fact that the same manufacturing procedure can be carried out in various locations is emphasised. Control algorithms as well as manufacturing strategies are explained. The equations of state are introduced. The two-stage criterion lets us use the result data generated by the simulator of the production system as the initial data for further processing; however, the main goal remains to minimise the time of the course of production. The precisely elaborated case study implements initial data obtained by preceding simulation procedures carried out in manufacturing systems consisting of three, four, and five subsystems.

Business Process Modeling of Logistic Production Systems

The paper highlights the problem of the logistic system modeling which consists of similar structures representing a synthetic manufacturing plant. Routes for products as well as matrixes of life and state are given. Structures differ from each other as they have various transport times from one production stand to another. Additionally, tool replacement times and machine maintenance times are given. There are also dedicated buffer stores which make the manufacturing process possible even if the subsequent machine is busy. Manufacturing strategies and heuristic algorithms are responsible for the control of the system. The main criterion is to minimize the total manufacturing time by means of searching for such a sequence of operations which often leads to avoiding bottlenecks. The main goal of the paper is to build a mathematical model of the above characterized system including proposals for algorithms for optimizing production in relation to orders.

Self-learning Bayesian Networks in Diagnosis☆

Abstract The article presents the main bases of artificial intelligence, probabilistic diagnostic methods, development of the diagnostic database and diagnostic base of knowledge and Bayesian networks as a base of the diagnostic self-learning systems which are commonly used in medicine to recognize diseases on the basis of symptoms. Probabilistic models of diagnostic networks are based on the Bayesian formulas. These formulas let us determine probabilities of causes on the basis of probabilities of results. This is the reason why databases must be created and adequate probabilities determined. Results of this research are then analyzed by means of statistical methods.

Simulation Analysis of the Complex Production System with Interoperation Buffer Stores

The article highlights the problem of mathematical modelling and subsequent simulation of the highly complex synthetic environment illustrating the real production system consisting of parallel manufacturing plants equipped with interoperation buffer stores. The system can be arranged optionally by means of the simulator which was created on the basis of the presented assumptions in the C# programming language. The discussed system realizes orders set by defined customers. Production control is based on heuristic algorithms which choose an order to be realized and a manufacturing plant in which the production process is carried out. The criterion is to minimize the total time of realizing orders however, as seen in the case study, also either the remaining capacity of tools after realizing all orders or the total tool replacement time can be taken into account while dealing with the problem. The modelling and projecting stages are followed by the simulation study. This simulation study is realized for the specific list of orders. It all leads to the thorough analysis of the obtained results which are later compared with the results obtained for the system without interoperation buffer stores.

Heuristic Control of the Assembly Line

The paper highlights the mathematical model of the assembly process for the automated line as well as sequence control algorithms for the assembled version of objects. The automatic assembly line control requires numerical simulation. Minimizing the assembly time of all objects or maximizing the number of assembled objects within the given time are treated as optimization criteria. Specification of the robot assembly line is described in detail as well as the method of controlling the order of manufactured objects. The equations of state of an automatic assembly line are presented. The simulation model includes heuristic algorithms for control determining of the assembly line. The assembly process in a line can be modeled with different assumptions.

Modelling the Validation Process of Enterprise Software Systems

The paper highlights the problem of the criteria-based assessment of the software system taking into account their sustainability, maintainability, and usability. The extended iteration model of creating software as well as its subsequent implementation is emphasised. The mathematical model for validating the software by highly qualified experts is introduced. It is followed by presenting the general pseudocode on the basis of which it is possible to program the software responsible for choosing the appropriate experts who are able to validate the software types.

Modelling of the Logistic Supplier-Consumer Behavior

The paper highlights the problems of mathematical modelling in the delivery system. The system describes the suppliers who offer different types of products as well as the consumers who order different products. Products are ordered at stochastic times, however, manufacturers offer predictable demand. The problem becomes more complex when the number of orders grows. The structure of the system is shown, equations of state are introduced and control algorithms as well as criteria are proposed. Orders change their state which leads to modifying it at every decision stage. The same concerns the actual output of manufacturers which also has to be modified. Therefore, the problem consists of the design of such a delivery pattern which can minimise losses of the discussed company. The goal of the paper is to present the mathematical model of the logistic system taking into account the consumer-supplier relations. The model forms the basis for the subsequent information support tool.

Logistic Modelling Of Order Realization In The Complex Parallel Manufacturing System.

The paper highlights the problem of mathematical modeling of the highly complex manufacturing system in which work stations are arranged serially within each production plant. Production plants are located in a parallel way. The specification details and the model resulting from them led to creating the simulator to be employed to solve the problem of realizing customers’ orders. Equations of state are given to illustrate the state of the system at each stage. The complex system is controlled by implementing heuristic algorithms. The criteria to be met are defined. The study case is based on randomly generated data and solved by means of the dedicated information tool. The search for the satisfactory solution is carried out either by an increased number of simulation runs or comparing the pairs or combinations of order and plant choosing algorithms. The main goal remains to meet the stated criterion. INTRODUCTION The growth of markets towards globalization results in materialization of automated industries with high performance of manufacturing systems. Traditional manufacturing systems are no longer able to satisfy these requirements. In the global market there is an increasing trend toward achieving a higher level of integration between designed and manufacturing functions in industries to make the operations more efficient and productive (Modrak and Pandian 2012). Effective organization and management of materials, processes and human resources of a company is a prerequisite in today’s highly competitive industrial landscape. Key goals are to improve planning and scheduling of processes, increase productivity, minimize inventory level, improve responsiveness to changes in demand, improve quality, and lower operation cost. These problems are solvable with the use of modelling and simulation of such production systems. One of the most useful tools in the arsenal of an operations research (industrial engineering) management science analyst consists in computer simulation. In this case it is necessary to put attention to the benefits resulting from combining both spheres, the one of formalized algorithms and the other one of the human instinct (Neumann 2011). The use of simulation, as a support tool to the operational decision making process, allows us to analyze, from a statistical point of view, the behavior of a production or logistic system that is subject generally to either controllable and or not controllable factors. Through computer simulation it is possible to select those operational decisions that maximize an objective function or a system performance parameter, and to evaluate effects of these decisions without controllable factors variability. An approach to implement efficiently and effectively simulation models in manufacturing systems is deployed in (Chramcov et al. 2011). Currently, a wide range of commercial products which use graphic interfaces (e.g. Arena, Witness, MapleSim 4, AutoMod, Quest, PlantSimulation, etc.) offer an extremely wide spectrum of possibilities for modelling and simulation of manufacturing, logistic and other queuing systems (Rizzoli 2009). Nevertheless, the general language C# has been adopted for creation of our production system simulator because the programming logic cannot be easily expressed in GUIbased systems (Babich and Bylev 1991). The initial specification and consequent modelling of the discussed manufacturing system is described in detail in (Bucki et al. 2012a). This paper is later expanded to the simulation form enabling us to carry out a simple simulation process (Bucki et al. 2012b). The simulation process shows that one of the suggested heuristics minimizes the total order realization time. However, the need to search for the solution to tasks carried out in the complex manufacturing system with buffer stores leads to extending specification details and the subsequent model which finally forms the basics for the simulation process (Bucki 2012). The simulation process is carried out by means of the simulation tool built on the basis of these specification details and the subsequent model (Marusza 2013). GENERAL SYSTEM FORMULATION Let us propose the information system imitating the continuous production process carried out in J work Proceedings 27th European Conference on Modelling and Simulation ©ECMS Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang (Editors) ISBN: 978-0-9564944-6-7 / ISBN: 978-0-9564944-7-4 (CD) stations arranged in a series. We assume that there is a machine in each work station which can perform I operations. However, we assume that only one tool can be determined to perform the operation on the order unit in each work station. We assume there are buffer stores between the work stations. The capacity of each buffer store is limited. Operations are performed in the work stations in sequence. Further, we assume there are more than one identical production systems available. Let us assume that A manufacturing plants are arranged in parallel. This system requires K stages to realize the order elements. The matrix of orders at the k-th stage is considered in the form (1), where k n m z , is the number of conventional units of the n-th order of the m-th customer at the k-th stage. The stage k, k=1,...,K is the moment of making the production decision. [ ] k n m k z Z , = ,m=1,...,M; n=1,...,N; k=1,...,K (1) The order matrix is modified after every decision about production in accordance with the specification (2). ⎪ ⎪ ⎩ ⎪⎪ ⎨ ⎧ − − = − − otherwise. stage, th at the realized is units of number the if 1 , , , 1 , , k n m k n m k n m k n m k n m z k x x z z (2) Some of the charge materials are used for manufacturing products of the specific order. The assignment matrix of ordered products to charges takes the form (3), where n m, ω is the number of charge material assigned to the order n m z , [ ] n m, ω = Ω , m=1,...,M; n=1,...,N (3) Elements of the assignment matrix take values according to (4). ⎪ ⎩ ⎪ ⎨ ⎧ − = otherwise. 0 charge, th the from realized is order the if