Course Details for A.Y. 2018/2019
Name:
Gestione della Produzione e della Logistica Aziendale / Industrial Applications of Operations Research
Basic information
Credits:
: Master Degree in Computer Science 6 CFU (c)
Degree(s):
Master Degree in Computer Science curriculum SDRC Elective
Master Degree in Computer Science curriculum ASSC Elective
Master Degree in Computer Science curriculum GSEEM Elective
Master Degree in Computer Science curriculum General Elective
Language:
English
Course Objectives
The course aims at illustrating a role Operations Research can play in industry, to help making optimal decisions based on important cost and performance indicators. Optimization models in manufacturing, logistics and project management are surveyed. Applications to real production contexts are described. Models and algorithms are exemplified with numerical applications solved via Excel optimization tools.
Course Content
- Inotrduction
Industry: the realm of organization. Basis of Industrial Revolution, and the rise of economic theories: Quesnay, Smith, Ricardo. Taylor and the taylorism. Measures, objectives, constraints, decisions. Outsourcing vs. in-house, and multiple deciders. Externalities: Jevons, environmental sustainability and rebound effect.
- Lot Sizing
Production and inventory planning. MRP. The hold-reorder trade-off. A periodic model: EOQ. The EOQ extended. Limits of EOQ. Non-periodic models: convex vs. concave cost function. Minimizing convex costs: a linear program. Minimizing concave costs: Wagner-Whitin algorithm. Backlogs. Management of heterogeneous inventories. Zoom in: perishable inventory control.
- Elements of Project Management
The success of PERT. Gantt charts, activity networks, critical and non-critical paths. The critical path method as an optimal way to attribute resources in order to get a time target. Formulation as linear programming (Refresh: the Network Simplex Method). Zoom in: solving the dual by network simplex.
- Location models
The most efficient way to locate a facility. Simple facility location, p-median, p-centre. Formulation as integer programming. k-echelon problems. Capacitated problems. Zoom in: how to model customer preferences.
- Cutting stock
The most efficient way to cut material. Kantorovich's integer program. Drawbacks. Dantzig-Wolfe decomposition and Gilmore-Gomory's model. Advantages and drawbacks. Computing the LP relaxation. The pricing problem. Making it real: reuse of material, assortment problems, set-ups; cutting and lot sizing, cutting and scheduling.
Zoom in: cutting stock with custom due-dates.
- Case studies
Reuse of material at Dayco Europe. Optimal material cut and inventory management at Pilkington.
Learning Outcomes (Dublin Descriptors)
On successful completion of this course, the student should
- Learn how to model decision variables, constraints and performance/cost indicators of a logistic/manufacturing system, learn new algorithms and optimization techniques
- Apply known and acquired optimization techniques to models, understand specific model properties
- Evaluate the complexity of an optimization model, choose the optimization method which is most adequate in terms of cost/performance
- Acquire an adequate technical vocabulary, being able to correctly transfer the concepts of modelling and optimization to industrial decision makers
- Being able to read and discuss a technical article on industrial optimization
Prerequisites and Learning Activities
Basic notions of Operations Research: linear programming (LP), simplex method, duality theory in LP
Assessment Methods and Criteria
Written test (50%), oral test (50%)
Textbooks
- M.L. Pinedo, Scheduling: Theory, Algorithms, and Systems , Springer-Verlag. 2008.
- R.J. Tersine, Principles of Inventory and Materials Management , North-Holland. 1988.
Course page updates
This course page is available (with possible updates) also for the following academic years:
To read the current information on this course, if it is still available, go to the university course catalogue .
Course information last updated on: 08 marzo 2017, 17:11