OVERVIEW

The rapid evolution of technology in industrial automation systems requires closer integration between the devices in the shopfloor and the rest of the company. This integration requires intelligent devices for data collection and the ability to transform data into usable information. This course deals with providing tools and methodologies to achieve this integration, with particular reference to the automation of manufacturing industries.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at providing the modeling and methodological tools for the formalization and resolution of some important decision-making and management problems in the context of industrial systems. During the course, planning, scheduling and control problems will be formalized and solved according to the framework proposed by the ANSI/ISA-95 international standard. Special focus will be devoted to the primary and support functions given by the Manufacturing Execution System (MES). At the end of the course, the student will be able to position an industrial automation problem in the context of ANSI/ISA-95 and to formalize and to solve decision-making problems, using proper methods and tools.

AIMS AND LEARNING OUTCOMES

The course aims at providing the modeling and methodological tools for the formalization and resolution of some important decision-making and management problems in the context of production systems. During the course, planning, scheduling and control problems will be formalized and solved in the framework of ANSI/ISA-95 international standard for developing an automated interface between enterprise and control systems. Special focus will be devoted to the primary and support functions given by the Manufacturing Execution System (MES). In addition, an important company working in this field will describe use cases. At the end of the course, the student will be able to position a problem in the context of ANSI/ISA-95 and to formalize and solve decision-making problems, using tools as Matlab.

PREREQUISITES

No prerequisite

TEACHING METHODS

Lessons and exercises in different software environments (among which Matlab)

SYLLABUS/CONTENT

1. Introduction (4h + 2h):

1. 1. Introduction to the Course
   2. Architectural Models in Industrial Automation
   3. Manufacturing Methods: Batch production, Job Production, Flow Production
   4. Improvement Methods (Lean Manufacturing, Reliability-centered Maintenance, Zero Defects…)
   5. Information and Communication Standards (ISA-88, ISA-95, ERP, IEC 62264, B2MML …)
   6. Matlab Basic Exercise 1.1 

1. Field Level and Direct Control (8h + 4h):

   1. Shopfloor Description and Examples
   2. SCADA, PLC, DCS
   3. Linear quadratic optimal control, Linear Quadratic Tracking, PID
   4. Matlab Exercise 2.1: Generate Ladder Logic Diagrams (https://www.plcfiddle.com/)
   5. Matlab Exercise 2.2: LQ control in discrete time system, tracking, and PID

1. Manufacturing Execution Systems (24h + 12h):

1. 1. Definition and Models
   2. Who’s Who in MES
   3. MES Primary Functions: Planning System Interface; Work Orders; Work Stations; Inventory / Materials; Material Movement; Data Collection; Exception Management
   4. MES Support Functions: Maintenance; Time and Attendance; Statistical Process Control; Quality Assurance; Process Data; Documentation Management; Genealogy; Supplier Management.
   5. Scheduling methods
   6. Process control and quality control
   7. Matlab Exercise 3.1: Single Machine Scheduling: SPT, EDD
   8. Matlab Exercise 3.2: Single Machine Scheduling: Moore; Flow Shop Scheduling: Johnson
   9. Matlab Exercise 3.3: Job Shop Scheduling
   10. Matlab Exercise 3.4: Dynamic Programming for Scheduling
   11. Matlab Exercise 3.5: Stochastic Scheduling
   12. Matlab Exercise 3.6: Run and Trend Charts, Time Plots in Statistical Process Control. Scatter Diagrams and Control Charts in Statistical Process Control

1. MRP, MRPII and ERP Systems (8h + 4h):

1. 1. Definition and Models (Make to Order, Make to Stock)
   2. Introduction to Basic Problems at Planning Level
   3. Exercise 4.1: Inventory Control Basic Example
   4. Exercise 4.2: Demand Prediction Basic Example

1. Use Cases (6h):

1. 1. Definition of Use Cases by a MES/ERP developer

Total: 72h

• Lectures: 44 hours; Hands-on: 28 hours

TEACHERS AND EXAM BOARD

Exam Board

ROBERTO SACILE (President)

MICHELE AICARDI

RICCARDO MINCIARDI (President Substitute)

LESSONS

LESSONS START

https://courses.unige.it/11160/p/students-timetable

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Project and oral interview

ASSESSMENT METHODS

Interview

Exam schedule