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Статья
Применение технологии Process Mining в управлении цепями поставок

Воронова А. П., Заходякин Г. В.

Логистика и управление цепями поставок. 2020. № 6(101). С. 26-36.

Глава в книге
Conceptual Framework of Agent-based Model of Relational Conflicts in Russian Retail

Morozova Y. A.

In bk.: Proceedings of Analytics for Management and Economics Conference AMEC 2019. St. Petersburg: 2019. P. 367-373.

Instrumental environment for solving optimization problems in logistics

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 1 модуль

Course Syllabus

Abstract

This course aims at building skills for development and implementation of optimization-based decision support systems for logistics and supply chain management. This course introduces students to new classes of optimization models and techniques, such as multi-objective programming, as well as discrete optimization approaches based on con-straint programming, local search and metaheuristics. However, the focus is not on the mathematics, but on the application of these techniques. To implement models, students can use AMPL modeling language, or learn advanced modeling tools like AIMMS and IBM Decision Optimization supporting creation of GUI-based decision support tools. The course puts a heavy emphasis on example applications of mathematical programming and opera-tions research for logistics management. The students can familiarize themselves with this topic by studying research literature and a library of optimization models for logistics planning.
Learning Objectives

Learning Objectives

  • To provide use-cases for optimizaiton-based decision making in logistics and supply chain management
  • To apply state of the art optimization modeling software tools for implementation of supply chain planning and scheduling models and prototype solutions
  • To explore conceptual models and mahtematical formulations for planning models at different levels and scope
Expected Learning Outcomes

Expected Learning Outcomes

  • Identifies problems using information about the company
  • Determines the task of data analysis for finding the solution of the problem
  • Selects the optimization problem components, depending on the specifics of a task being solved
  • Can implement a mathematical optimziation model using a modeling language and solver
  • Can create reports and visualizations for the solution using visual analytics software tools
  • Can explain in own words the difference between linear and mixed integer solvers
  • Can formulate a multi-objective model using a goal programming approach
Course Contents

Course Contents

  • Application of mathematical programming in logistics and supply chain management
  • Data management and visualization for decision support
  • Computer tools for modeling and solving optimization problems
  • Multi-objective optimization
  • An overview of algorithms for solving linear and mixed-integer programming problems
Assessment Elements

Assessment Elements

  • non-blocking Presentation
  • non-blocking Participation
    The grade is computed by dividing the sum of points for participation obtained by the student divided by the total points for all assignments posted and multiplied by 10. The result is posted into the gradebook without rounding.
  • blocking Project Defense
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.25 * Participation + 0.25 * Presentation + 0.5 * Project Defense
Bibliography

Bibliography

Recommended Core Bibliography

  • Chris Kuip. (2013). The modeling system AIMMS for developing Mathematical Programming and Constraint Programming applications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4E60BC5B
  • Emmanuel Kwasi Mensah, & Matteo Rocca. (2019). Light Robust Goal Programming. Mathematical and Computational Applications, (4), 85. https://doi.org/10.3390/mca24040085
  • Hartmut Stadtler, Bernhard Fleischmann, Martin Grunow, Herbert Meyr, & Christopher Sürie. (2012). Advanced Planning in Supply Chains. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.mgmtpr.978.3.642.24215.1
  • Sleeper, R. (2018). Practical Tableau : 100 Tips, Tutorials, and Strategies From a Tableau Zen Master (Vol. First edition). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1752754
  • Stadtler, H., Fleischmann, B., Grunow, M., Meyr, H., & Sürie, C. (2012). Advanced Planning in Supply Chains : Illustrating the Concepts Using an SAP® APO Case Study. Berlin: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1176895
  • Taha, H. A., & Schmidt, J. W. (2014). Integer Programming : Theory, Applications, and Computations. Burlington: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=919898

Recommended Additional Bibliography

  • Ali Allahverdi, Erwin Pesch, Michael Pinedo, & Frank Werner. (2018). Scheduling in manufacturing systems: new trends and perspectives. International Journal of Production Research, (19), 6333. https://doi.org/10.1080/00207543.2018.1504252
  • Integer programming models for mid-term production planning for high-tech low-volume supply chains. (2018). European Journal of Operational Research, 269(3), 984–997. https://doi.org/10.1016/j.ejor.2018.02.049
  • Кравцова, Н., & Заходякин, Г. (2012). Применение Языка Программирования Ampl Для Решения Задачи О Выборе Оптимального Местоположения Ветряных Установок. УСПЕХИ В ХИМИИ И ХИМИЧЕСКОЙ ТЕХНОЛОГИИ, (11 (140)). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsclk&AN=edsclk.15088184