Models and methods of management in corporate information systems
Description: The course studies problems using applied regression analysis and reduced to a transport model, in transport networks, the problem of the shortest path and the problem of maximum flow, queuing systems, dynamic programming. As a result of studying the course, the student will be able to correctly set the management task, choose a method and software for its solution.
Amount of credits: 8
Пререквизиты:
- Statistical methods of data analysis
- Algorithmization and programming fundamentals
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 30 |
| Practical works | |
| Laboratory works | 45 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 45 |
| SAW (Student autonomous work) | 120 |
| Form of final control | Exam |
| Final assessment method |
Component: Component by selection
Cycle: Profiling disciplines
Goal
- Training of specialists capable of solving problems of informatization, setting management tasks, selecting methods and software for its solution
Objective
- acquisition of deep theoretical and practical knowledge of linear and dynamic programming
- formation of knowledge and skills in setting management tasks, selection of methods for their solution
- mastering modern application software packages implementing data analysis algorithms
Learning outcome: knowledge and understanding
- demonstrate knowledge of data analysis methods to study the activities of organizations
Learning outcome: applying knowledge and understanding
- independently solve problems on the choice of management methods in practical situations
Learning outcome: formation of judgments
- correctly formulate questions and build professional dialogues on topics related to management methods
Learning outcome: communicative abilities
- work in a team, defend your point of view, offer new solutions to management problems in information systems
Learning outcome: learning skills or learning abilities
- to collect the necessary information, systematize and generalize it, use the acquired knowledge in the management of corporate information systems
Teaching methods
интерактивная лекция (проблемная лекция, дискуссионная лекция, лекция-конференция, лекция-консультация, лекция «Пресс-конференция», лекция «Вопросы-ответы-обсуждение»);
метод проектов (наработка и преобразование собственного опыта и компетентности)
Assessment of the student's knowledge
Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.
| Period | Type of task | Total |
|---|---|---|
| 1 rating | Лабораторная работа 1 | 0-100 |
| Лабораторная работа 2 | ||
| Лабораторная работа 3 | ||
| Рубежный контроль | ||
| 2 rating | Лабораторная работа 4 | 0-100 |
| Лабораторная работа 5 | ||
| Лабораторная работа 6 | ||
| Тестирование | ||
| Total control | Exam | 0-100 |
The evaluating policy of learning outcomes by work type
| Type of task | 90-100 | 70-89 | 50-69 | 0-49 |
|---|---|---|---|---|
| Excellent | Good | Satisfactory | Unsatisfactory |
Evaluation form
The student's final grade in the course is calculated on a 100 point grading scale, it includes:
- 40% of the examination result;
- 60% of current control result.
The final grade is calculated by the formula:
| FG = 0,6 | MT1+MT2 | +0,4E |
| 2 |
Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;
E is a digital equivalent of the exam grade.
Final alphabetical grade and its equivalent in points:
The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:
| Alphabetical grade | Numerical value | Points (%) | Traditional grade |
|---|---|---|---|
| A | 4.0 | 95-100 | Excellent |
| A- | 3.67 | 90-94 | |
| B+ | 3.33 | 85-89 | Good |
| B | 3.0 | 80-84 | |
| B- | 2.67 | 75-79 | |
| C+ | 2.33 | 70-74 | |
| C | 2.0 | 65-69 | Satisfactory |
| C- | 1.67 | 60-64 | |
| D+ | 1.33 | 55-59 | |
| D | 1.0 | 50-54 | |
| FX | 0.5 | 25-49 | Unsatisfactory |
| F | 0 | 0-24 |
Topics of lectures
- Основные понятия теории моделирования
- Линейное программирование
- Технология решения задач нелинейного и целочисленного программирования
- Транспортная задача и ее применение
- Специальные задачи линейного программирования, сводящиеся к транспортной модели
- Сети
- Сетевое планирование многоэтапных операций
- Прикладной регрессионный анализ
- Системы массового обслуживания
- Элементы теории случайных процессов
Key reading
- Бахвалов Л.А. Компьютерное моделирование: долгий путь к сияющим вершинам [Электронный ресурс]. – Режим доступа: http://www.gpss–forum.narod.ru/GPSSmodeling.html, свободный.
- Большаков А. С. Моделирование в менеджменте: учеб. пособие. – М.: Филинъ, 2010.
- Бешенков С. А. Моделирование и формализация: методическое пособие. – М.: Лаборатория базовых знаний, 2012.
Further reading
- Докукин В. П. Основы математического моделирования: Конспект лекций. Санкт-Петербургский ГГИ. – М.: Дело, 2017.
- Волчков С., Балахонова И. Бизнес-моделирование для совершенствования деятельности промышленного предприятия // ЦИТ «Платон» "КомпьютерПресс". 2001. №11.
