Module also offered within study programmes:
General information:
Name:
VRP and Supply Chain Optimization Models
Course of study:
2019/2020
Code:
ZZIP-1-614-s
Faculty of:
Management
Study level:
First-cycle studies
Specialty:
-
Field of study:
Management and Production Engineering
Semester:
6
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr inż. Sawik Bartosz (BSawik@zarz.agh.edu.pl)
Module summary

This course will consist of two parts.
The first part includes introduction to optimization models for transportation: classification and different types of Vehicle Routing Problems (VRP). Presentation of typical applications for Vehicle Routing Problems.
In the second part includes introduction to optimization models for supply chain: classification and different types of models with considering disruptions risk: risk-neutral models, risk-averse models, mean-risk models.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. ZIP1A_U02, ZIP1A_W07 Involvement in teamwork
Skills: he can
M_U001 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. ZIP1A_U02, ZIP1A_W07 Execution of a project
M_U002 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. ZIP1A_U02, ZIP1A_W07 Execution of exercises
Knowledge: he knows and understands
M_W001 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. ZIP1A_U02, ZIP1A_W07 Test
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
60 30 0 0 0 0 0 0 0 30 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. + - - - - - - - + - -
Skills
M_U001 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. + - - - - - - - + - -
M_U002 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. + - - - - - - - + - -
Knowledge
M_W001 Student is able to acquire knowledge by oneself. Student is able to identify the type of VRP and Supply Chain optimization models. Student is able to identify and explain mathematical formulation of VRP and Supply Chain optimization models. Student is familiar with VRP and Supply Chain optimization models. Student knows VRP and Supply Chain optimization models. + - - - - - - - + - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 120 h
Module ECTS credits 4 ECTS
Udział w zajęciach dydaktycznych/praktyka 60 h
Preparation for classes 20 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 28 h
Realization of independently performed tasks 8 h
Examination or Final test 2 h
Contact hours 2 h
Module content
Lectures (30h):

This course will consist of two parts. The first part includes introduction to optimization models for transportation: classification and different types of Vehicle Routing Problems (VRP): Green VRP (G-VRP), H-VRP, VRP with time window (VRPTW), the capacitated VRP (CVRP), the multi-depot VRP (MDVRP), the site-dependent VRP (SDVRP), the open routing problem (OVRP) and more. Presentation of typical applications for Vehicle Routing Problems, such as: Distribution plan for a Wholesale dealer, Garbage Disposal, Mail delivery, Mailbox collection, Security company’s rounds, Elevator maintenance, School bus routing, Airline Schedules, Snow Plows and more. In the second part includes introduction to optimization models for supply chain: classification and different types of models with considering disruptions risk: risk-neutral models, risk-averse models, mean-risk models. Optimal supplier selection problems. Selection of resilient supply portfolio under disruption risk. Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies.

Workshops (30h):

This course will consist of two parts. The first part includes introduction to optimization models for transportation: classification and different types of Vehicle Routing Problems (VRP): Green VRP (G-VRP), H-VRP, VRP with time window (VRPTW), the capacitated VRP (CVRP), the multi-depot VRP (MDVRP), the site-dependent VRP (SDVRP), the open routing problem (OVRP) and more. Presentation of typical applications for Vehicle Routing Problems, such as: Distribution plan for a Wholesale dealer, Garbage Disposal, Mail delivery, Mailbox collection, Security company’s rounds, Elevator maintenance, School bus routing, Airline Schedules, Snow Plows and more. In the second part includes introduction to optimization models for supply chain: classification and different types of models with considering disruptions risk: risk-neutral models, risk-averse models, mean-risk models. Optimal supplier selection problems. Selection of resilient supply portfolio under disruption risk. Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies.

Additional information
Teaching methods and techniques:
  • Lectures: Nie określono
  • Workshops: Nie określono
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

W przypadku nieuzyskania zaliczenia w wymaganym terminie, każdemu studentowi przysługuje jeden termin zaliczenia poprawkowego na zasadach ustalonych z prowadzącym.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
  • Workshops:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
Method of calculating the final grade:

Exercises 35%, project (case study) 35%, written tests 30%.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

W przypadku nieobecności na zajęciach decyzja o możliwości i formie uzupełnienia zaległości należy do prowadzącego zajęcia, z zastrzeżeniem zapisów wynikających z Regulaminu Studiów.

Prerequisites and additional requirements:

This course is addressed to students of management, engineering and computer science.
Knowledge of English on communicative level.
Basics of mathematics including logic and algebra are required to participate.

Udział w wykładach nieobowiązkowy.
Udział w zajęciach obowiązkowy.

Recommended literature and teaching resources:

ANBUUDAYASANKAR S. P., GANESH ‎K., MOHAPATRA S. (2014). Models for Practical Routing Problems in Logistics: Design and Practices, Springer, London, UK.
SAWIK B. (2018). Weighted-Sum Approach for Bi-Objective Optimization of Fleet Size with Environmental Aspects. chapter in: Lawrence K.D., Kleinman G. (Eds.) Applications of Management Science (Vol. 19) Applications of Management Science. Bingley, UK: Emerald Group Publishing Limited, Bingley, UK, pp. 101-116
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). Multi-Criteria Optimization for Fleet Size with Environmental Aspects, Transportation Research Procedia, Vol. 27: 61-68
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). A Multicriteria Analysis for the Green VRP: A Case Discussion for the Distribution Problem of a Spanish Retailer, Transportation Research Procedia, Vol. 22: 305-313
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). Multi-Objective Traveling Salesman and Transportation Problem with Environmental Aspects. chapter in: Lawrence K.D., Kleinman G. (Eds.) Applications of Management Science (Vol. 18) Applications of Management Science. Bingley, UK: Emerald Group Publishing Limited, Bingley, UK, pp. 21-56
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). Selected Multi-Criteria Green Vehicle Routing Problems, chapter in: Lawrence K.D., Kleinman G. (Eds.) Applications of Management Science (Vol. 18) Applications of Management Science. Bingley, UK: Emerald Group Publishing Limited, Bingley, UK, pp. 57-84
SAWIK T. (2018). Supply Chain Disruption Management Using Stochastic Mixed Integer Programming, Springer, New York, USA.

Scientific publications of module course instructors related to the topic of the module:

ANBUUDAYASANKAR S. P., GANESH ‎K., MOHAPATRA S. (2014). Models for Practical Routing Problems in Logistics: Design and Practices, Springer, London, UK.
SAWIK B. (2018). Weighted-Sum Approach for Bi-Objective Optimization of Fleet Size with Environmental Aspects. chapter in: Lawrence K.D., Kleinman G. (Eds.) Applications of Management Science (Vol. 19) Applications of Management Science. Bingley, UK: Emerald Group Publishing Limited, Bingley, UK, pp. 101-116
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). Multi-Criteria Optimization for Fleet Size with Environmental Aspects, Transportation Research Procedia, Vol. 27: 61-68
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). A Multicriteria Analysis for the Green VRP: A Case Discussion for the Distribution Problem of a Spanish Retailer, Transportation Research Procedia, Vol. 22: 305-313
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). Multi-Objective Traveling Salesman and Transportation Problem with Environmental Aspects. chapter in: Lawrence K.D., Kleinman G. (Eds.) Applications of Management Science (Vol. 18) Applications of Management Science. Bingley, UK: Emerald Group Publishing Limited, Bingley, UK, pp. 21-56
SAWIK B., FAULIN J., PÉREZ-BERNABEU E. (2017). Selected Multi-Criteria Green Vehicle Routing Problems, chapter in: Lawrence K.D., Kleinman G. (Eds.) Applications of Management Science (Vol. 18) Applications of Management Science. Bingley, UK: Emerald Group Publishing Limited, Bingley, UK, pp. 57-84
SAWIK T. (2018). Supply Chain Disruption Management Using Stochastic Mixed Integer Programming, Springer, New York, USA.

Additional information:

None