SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

Tomasz  Rymarczyk; Grzegorz  Kłosowski

doi:10.5604/01.3001.0010.7244

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Published: 2017-12-21

DOI: https://doi.org/10.5604/01.3001.0010.7244

Issue Vol. 7 No. 4 (2017)

SELECTED PROBLEMS OF EVALUATION AND CLASSIFICATION OF HISTORICAL BUILDINGS USING ROUGH SETS
Krzysztof Czajkowski

5-10
LABORATORY STAND FOR SMALL WIND TURBINE SIMULATION
Wojciech Matelski, Eugeniusz Łowiec, Stanisław Abramik

11-14
DEVELOPMENT OF AN AUTOMATED DIAGNOSTICS AND CONTROL SYSTEM FOR BIOGAS COMBUSTION PROCESSES
Oxana Zhirnova

15-19
SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD
Tomasz Rymarczyk, Grzegorz Kłosowski

20-23
THE USE OF PETRI NETS IN DECISION SUPPORT SYSTEMS BASED ON INTELLIGENT MULTIPLY SOURCE DATA ANALYSIS
Tomasz Rymarczyk, Grzegorz Kłosowski, Tomasz Cieplak

24-27
APPLICABILITY ANALYSIS OF REST AND SOAP WEB SERVICES
Tomasz Zientarski, Marek Miłosz, Marek Kamiński, Maciej Kołodziej

28-31
A REVIEW OF CONTROL METHODS OF WIND TURBINE SYSTEMS WITH PERMANENT MAGNET SYNCHRONOUS GENERATOR
Piotr Gajewski

32-37
DIRECT TORQUE CONTROL OF MULTI-PHASE INDUCTION MOTOR WITH FUZZY LOGIC SPEED CONTROLLER
Jacek Listwan

38-43
IMAGE COMPLETION WITH LOW-RANK MODEL APPROXIMATION METHODS
Tomasz Sadowski, Rafał Zdunek

44-48
APPROXIMATION OF ELECTRIC PROPERTIES OF PERIODIC LAYERED COMPOSITE MATERIALS
Adam Steckiewicz, Bogusław Butryło

49-52
BOOST QUASI-RESONANT CONVERTERS FOR PHOTOVOLTAIC SYSTEM
Michał Harasimczuk

53-56
RESEARCH OF FLOW AROUND SELECTED SENSORS PROFILES FOR METROLOGY FLOWS
Piotr Zgolak

57-61
MAXIMUM SUBARRAY PROBLEM OPTIMIZATION FOR SPECIFIC DATA
Tomasz Rojek

62-65
ANALYSIS OF POWER LOSS IN THE LOW-SPEED PNEUMATIC ENGINE
Adam Ilnicki, Mariusz Rząsa

66-69
APPLICATION OF FUZZY COGNITIVE MAP TO PREDICT OF EFFECTIVENESS OF BIKE SHARING SYSTEMS
Aleksander Jastriebow, Łukasz Kubuś, Katarzyna Poczęta

70-73
FUZZY COGNITIVE MAP AS AN INTELLIGENT RECOMMENDER SYSTEM OF WEBSITE RESOURCES
Aleksander Jastriebow, Łukasz Kubuś, Katarzyna Poczęta

74-78
MODELING OF THE ARTIFICIAL BLOOD CHAMBER AND THE MICROPUMPS PULSATILE DRIVE FOR BLOOD TRANSFUSION
Sebastian Bartel

79-81
CONTROL A SMALL WIND TURBINE WITH ASYNCHRONOUS GENERATOR
Kamil Możdżyński, Tomasz Gajowik, Krzysztof Rafał, Mariusz Malinowski

82-87
MECHANICAL PROPERTIES OF SELECTED EPOXY ADHESIVES
Izabela Miturska, Anna Rudawska

88-91
POLYNOMIAL APPROXIMATION FOR T WAVE PARAMETER RECOGNITION IN ECG PROCESSING
Marcin Maciejewski

92-95
THE IMPACT OF WINDOW FUNCTION ON IDENTIFICATION OF SPEAKER EMOTIONAL STATE
Paweł Powroźnik, Dariusz Czerwiński

96-100
USE OF MULTICRITERIAL OPTIMIZATION IN FURNITURE MANUFACTURING PROCESS
Grzegorz Kłosowski, Edward Kozłowski

101-106
MODEL OF DYNAMIC ELEVATOR CONTROL SYSTEM USING CENTRAL APPLICATION SERVER
Łukasz Furgała, Krzysztof Kolano, Włodzimierz Mosorow

107-112

Authors

Tomasz Rymarczyk

tomasz@rymarczyk.com

Research and Development Center, Netrix S.A., Lublin; University of Economics and Innovation in Lublin,

Grzegorz Kłosowski

g.klosowski@pollub.pl

Lublin University of Technology, Faculty of Management, Department of Organization of Enterprise

Abstract

In this paper, the conceptual model of risk-based cost estimation for completing tasks within supply chain is presented. This model is a hybrid. Its main unit is based on Monte Carlo Simulation (MCS). Due to the fact that the important and difficult to evaluate input information is vector of risk-occur probabilities the use of artificial intelligence method was proposed. The model assumes the use of fuzzy logic or artificial neural networks – depending on the availability of historical data. The presented model could provide support to managers in making valuation decisions regarding various tasks in supply chain management.

Keywords:

project management, decision support systems, neural networks, fuzzy logic

References

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SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD. (2017). Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(4), 20-23. https://doi.org/10.5604/01.3001.0010.7244

SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

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