| Module content |
- Review of the general framework for the planning and control of manufacturing and service systems
- Deterministic Lot Sizing Models of Inventory Management
- Basic single item EOQ/EPQ, shortage, all unit and marginal discount models
- Discrete time and quantity models and their solution approaches
- Multi item models including shared resource with constraints, common cycle, basic cycle, power of two and Economic Lot Scheduling models
- Multi echelon and foundational supply chain inventory models
- Models with building blocks for contemporary research areas in deterministic inventory models: deterioration, non-linear demand rate, non-linear production rate, growing items, demand-, time-, stock and price- dependent models and other emerging model block areas
- Finite Job Scheduling Models and their Solution Techniques
- Scheduling notation, dispatch rules and their solution characteristics
- Flow shop models, job shop models, selected variants and their solution algorithms
- Formulation of basic mathematical programming models for scheduling problems
- Solution techniques for scheduling LP models and analysis of solution heuristics: review of general mathematical proof techniques; growth functions and asymptotic bounds of solution algorithms; NPcompleteness, orst- and average-case behaviour of algorithms and illustration with some basic problems; analysis of selected exact scheduling solution algorithms; discussion of selected heuristic and meta heuristic alternatives and their time complexity; design and analysis of hybrid-solutions for NP-hard scheduling problems; scheduling solution/result analysis
- Structural Models of Supply Chain Factors and their Relationships
- Review of descriptive statistics, statistical inference, estimation and hypothesis testing principles
- Multivariate statistical problems and foundational regression analysis
- Foundations of Structural Equation Modelling (SEM) and its representations
- Foundational Principal Component Analysis (PCA) and Factor Analysis (FA)
- Introduction to Covariance Based (CB) and Partial Least Square (PLS) SEM approaches
- Procedure for implementing PLS SEM and interpretation of solution output
- Cases of Supply Chain SEM models and their analysis with PLS SEM using Smart PLS
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