Courses offered at Ajou University: Click each course to view covered topics.
π‘ We have four courses for the Spring 2026 semester: two undergraduate courses and two graduate courses. See below for more information about each course.
This course covers Linear Programming (LP), one of the most widely used and fundamental tools in optimization, through various approaches such as the graphical method, the Simplex method, multi-objective LP, network flow models, and integer programming. It also introduces the basics of nonlinear programming and routing problems such as TSP and PDPTW. Students will begin with manual calculations and gradually progress to building and handling models using simple tools like Excel and more advanced tools like Python, enabling them to solve even complex equations step by step.
Also taught in DNA821 Mobility Systems Optimization (S26)
Covered Topics
Introduction to Linear Programming
Graphical Solution Approach for Linear Programming
Possible Solutions in Linear Programming
Simplex Method
Sensitivity Analysis and Duality
Multi-Objective Linear Programming
Integer Linear Programming
Network Flow Programming
Nonlinear Programming
Routing Problems
λͺ¨λΉλ¦¬ν° λ° λ¬Όλ₯ λ€νΈμν¬ κ³Όλͺ©μμλ κ΅ν΅ λͺ¨λΉλ¦¬ν° μλΉμ€μ λ¬Όλ₯ μλΉμ€μμ νμ©λλ λ€νΈμν¬ μ΄λ‘ μ λ€λ£¬λ€. κ³λλΆμλ‘ (OR) κ³Όλͺ©μμ νμ΅ν μνμ λͺ¨νμ λ°νμΌλ‘, OR κΈ°λ° Transportation Problemμ ν΄κ²°νλ μκ³ λ¦¬μ¦κ³Ό Shortest Path Problem, κ·Έλ¦¬κ³ μ΄κ²μ μΌλ°νλ ννμΈ Transshipment Problemμ νμ΅νλ€. μ΄λ₯Ό ν΅ν΄ User Equilibrium λ° System Optimization κΈ°λ°μ Trip Assignmentλ‘ μ΄μ΄μ§λ κ³Όμ μ λΆμνλ€. λν, μ€λλ κ²½λ‘λ₯Ό ν¨μ¨μ μΌλ‘ μ°μΆνλ λͺ¨νλ€μ λμ μ리λ₯Ό μ€μ΅ κ³Όμ μ ν΅ν΄ μ΄ν΄νκ³ , μ΄λ¬ν λͺ¨νμ΄ μμμλ΅ν κ΅ν΅(DRT, on-demand mobility)κ³Ό λ―Έλμ μμ¨μ£Όννμ λ°λΌ λμ± νμ±νλ κ²μΌλ‘ κΈ°λλλ 곡μ λͺ¨λΉλ¦¬ν° μλΉμ€ λ° λ¬Όλ₯ μ΅μ νμ μ΄λ»κ² νμ©λ μ μλμ§ λΆμνλ€.
Covered Topics
Introduction to Transportation Networks
Transportation Problem: Hitchcock Algorithm, Northwest Corner Rule, Minimum Cost Rule, Vogel Approximation Rule
Transshipment Problem
Shortest Path Problem: Moore's Algorithm, Dijkstra's Algorithm, Floyd's Algorithm
Trip Assignment Problem: Behavioral Criteria, Path Identification, Network Loading
Equilibrium Theory: User Equilibrium, Stochastic User Equilibrium, System Optimization
In this course, students learn the fundamental theories of urban public transportation systems planning. The course discusses the evolving scope and roles of public transit and emerging shared mobility services in response to societal changes, and explores the characteristics and operational principles of various modes. In addition, it rigorously examines the increasingly blurred conceptual boundaries between shared mobility and public transit to prepare students for future roles as transportation planners. Based on the concepts learned, students complete a project to design a public transit route or a shared mobility service and articulate the purpose and effectiveness of their proposed design.
Covered Topics
Public Transit Modes and Operational Characteristics
Service Planning Fundamentals
Transit Performance Metrics & Service Level Evaluation
Transit Scheduling, Headways, and Reliability
Data-Driven Analysis for Transit Planning (GTFS & Open Data)
Case Studies of Transit Innovations
Introduction to Shared Mobility & Demand-Responsive Services
Microtransit, MaaS, and Platform-Based Mobility Ecosystems
Interaction Between Shared Mobility and Traditional Transit
Policies, Pricing, and Governance of New Mobility Services
λμ€κ΅ν΅νΉλ‘ μ λνμ μμ€μ μ¬ν κ°μλ‘, λμ€κ΅ν΅ λ° κ³΅μ λͺ¨λΉλ¦¬ν°μ μ μ± κ³Ό κ³νμ μ΄λ‘ κ³Ό μ€λ¬΄λ₯Ό μμ°λ₯΄λ κ΄μ μμ μ¬μΈ΅μ μΌλ‘ λ€λ£¬λ€. μ μ± μ¬λ‘μ μ΅μ μ°κ΅¬λ₯Ό λΆμνμ¬ λ³ννλ λͺ¨λΉλ¦¬ν° νκ²½ μμμ λμ€κ΅ν΅μ λ²μ£Όμ μλ¨λ³ μν λ³νλ₯Ό νꡬνλ€. κ°μ μ λ°λΆλ κ΅μ κ°μμ νμ λ°μ λ‘ κ΅¬μ±λλ©° ν λ‘ μ€μ¬μΌλ‘ μ§νλκ³ , νλ°λΆμλ μ°κ΅¬ κ³νμ λ° μ μ± λ¦¬λ·° λ³΄κ³ μλ₯Ό μμ±νμ¬ νμ μ κΈμ°κΈ° λ₯λ ₯κ³Ό μ°κ΅¬ μ£Όμ ꡬμ μλμ κ°ννλ€. λ€μν μ°κ΅¬ μλ£μ μ μ± μ μ£Όμ λ‘ μ견μ κ΅ννλ©°, μ΄λ₯Ό λ°νμΌλ‘ κ°μμ μ°κ΅¬ μ£Όμ λ₯Ό ꡬ체ννκ³ λ°μ μν¬ μ μλλ‘ νλ€.
Covered Topics
μμ¨μ£Όνμ°¨μ λμ€κ΅ν΅μ λ―Έλ
λμ€κ΅ν΅ λ Έμ μ‘°μ κ³Ό νμΉ μ ν
μμ¨μ£Όν νμμ μ£Όμ°¨ μ μ±
κ°μΈ-곡μ μμ¨μ£Όνμ°¨ 볡ν©μλ¨ ν΅ν
κ³ μλλ‘ κ΅¬κ° κ΄μλ²μ€ κ³΅κΈ λμ±
λ²μ€ μ΄ν κ°κ²© λΆκ·μΉν νμ
λͺ¨λΉλ¦¬ν° μλΉμ€ μ΅μ νμ μνμ μ κ·Ό
κ΅ν΅μ μ± νΉλ‘ μ λνμ μμ€μ μ¬ν κ°μλ‘, κ΅ν΅ μ μ± λΆμΌμ ν΄μΈ μ°μ νμ μ§μ κ²μ¬λ μ΅μ λ Όλ¬Έμ λΉνμ μκ°μμ λΆμνκ³ λ¦¬λ·°νλ€. λ Όλ¬Έ μμ±μ κΈ°λ³Έμ΄ λλ ꡬμ±κ³Ό μ κ° λ°©μμ μ΄ν΄νλ©°, μ μλ μ μ± μ μ λΉμ±κ³Ό νμ€μ±μ μ€μ¬μΌλ‘ μ¬μΈ΅ λΆμμ μννλ€. λν λ Όλ¬Έμμ νμ©λ λ°©λ²λ‘ κ³Ό λͺ¨νμ μ μ© κ°λ₯μ±μ κ²ν νκ³ , μ μλ μ μ± μ΄ μΈκ³μ μΌλ‘ 보νΈμ±μ μ§λλμ§, κ΅λ΄μ λμ νκΈ°μ μ μ νμ§, κ·Έλ¦¬κ³ λμ μ νμν μ λμ , κΈ°μ μ μ€λΉ μ건μ 무μμΈμ§ ν λ‘ νλ€. λ³Έ κ°μλ λ€μμ λ Όλ¬Έμ ν¨μ¨μ μΌλ‘ μ½κ³ ν΅μ¬μ νμ νλ λ₯λ ₯μ μꡬνλ©°, νμλ€μ΄ μ¬μ μ λ Όλ¬Έ λ΄μ©μ μμ§ν ν λΆμ κ²°κ³Όλ₯Ό λ°ννκ³ μ΄λ₯Ό κΈ°λ°μΌλ‘ ν λ‘ μ μ§ννλ ννλ‘ μ΄μλλ€.
Covered Topics
μ κ·Όμ±κ³Ό 15λΆ λμ
μμ κ±° λ° νΌμ€λ λͺ¨λΉλ¦¬ν°
TDM: νΌμ‘ ν΅νλ£, λμ¬ μ£Όμ°¨ κ΄λ¦¬ μ μ±
λμ€κ΅ν΅κ³Ό 곡μ λͺ¨λΉλ¦¬ν°, MaaS
μμ¨μ£Όν λͺ¨λΉλ¦¬ν° μ μ±
κ³ μμ² λμ λ―Έλ μ§μ κ° κ΅ν΅
λ¬Όλ₯ κ΅ν΅, ν곡 λλ‘ λ°°μ‘
κ΅ν΅ ννμ±, μ¬νμ μ½μ μ΄λκΆ
κ΅ν΅κ³Ό νκ²½: μ¨μ€κ°μ€μ VMT
ν΅κ³νμ λ€λ₯Έ λΆμΌμ λ§μ°¬κ°μ§λ‘ κ΅ν΅ λΆμΌμμλ νλκ² μ°μ΄λ λꡬμ νλ¬ΈμΌλ‘μ, μ 곡 λΆμΌμ λν 본격μ μΈ νꡬλ₯Ό νκΈ° μ μ κ°μΆμ΄μΌ ν κΈ°λ³Έμ μλμ΄λ€. μ€λ§νΈμν° ν΅κ³νμμλ κ΅ν΅κ³΅ν, νΉμ λ³΄λ€ λκ² μ€λ§νΈμν° λΆμΌμμ μ£Όλ‘ νμ©λλ μ λ¬Έ μμ€μ ν΅κ³ν κ°λ μ νμ΅νλ€. κΈ°μ΄ν΅κ³λλΆν° μμνμ¬ νκ· λΆμ λ± λ€μν λΆμ κΈ°λ²μ ν΅ν΄ μ μ°¨ μ κ·Όμ±μ΄ λμμ§κ³ μλ λμ©λμ λΉ λ°μ΄ν° λΆμμ 체κ³μ μΌλ‘ μνν μ μλλ‘ νλ€. λν, 곡κ°ν μ μλ ν₯λ―Έλ‘μ΄ μμ νμ΄λ₯Ό ν΅ν΄ νμ΅ ν¨μ¨μ λμ΄λ©΄μ, μ΅λλ νλ₯ λ° ν΅κ³ κΈ°λ²λ€μ΄ λ―Έλμ μ λ¬Έκ°λ‘μ νλΉν μμ¬ κ²°μ μ λ΄λ¦΄ μ μλλ‘ λ³΄μ‘°νλ μ μ©ν λꡬλ‘μ κΈ°λ₯ν μ μλλ‘ νλ€.
Covered Topics
ν΅κ³μ μΆλ‘
κΈ°μ ν΅κ³
νλ₯ λ³μ
νλ³ΈμΆμΆκ³Ό νμ§λΆν¬
μΆμ κ³Ό κ°μ€κ²μ
λ λͺ¨μ§λ¨ λ° λΆμ° μΆλ‘
λ²μ£Όν λ°μ΄ν° λΆμ
λΆμ°λΆμ
λ¨μμ ννκ·λΆμ
λ€μ€νκ·λΆμκ³Ό λͺ¨νꡬμΆ
λΉλͺ¨μν΅κ³
λͺ¨λΉλ¦¬ν° λ°μ΄ν° μ€μ΅