The objective of the course is to endow the students with advanced knowledge for operations planning and scheduling in manufacturing and logistics systems. Topics include deterministic operations research methodology for the design of decision support systems, modeling, algorithms and applications.
Curriculum
teacher profile teaching materials
Gradient, Hessian
Local minimum, Necessary conditions (first and second order)
Local minimum, Sufficient conditions (secondo order and convex case)
Gradient method, Line search
Newton method,
2. Constrained non-linear programming
KKT conditions
Barrier method and Penalty functions
3. Lot Sizing
EOQ model
Wagner-Whitin Algorithm
Zangwill Algorithm
4. Job Shop Scheduling
Exact methods, Carlier-Pinson Algorithm
Euristhic methods, Nowicki-Smutnicki Algorithm
5. Vehicle Routing Problem
6. Crew Scheduling
7. Plant Location
Mutuazione: 20801784 OTTIMIZZAZIONE DELLA LOGISTICA in Ingegneria gestionale e dell'automazione LM-32 PACCIARELLI DARIO
Programme
1. Non-linear programmingGradient, Hessian
Local minimum, Necessary conditions (first and second order)
Local minimum, Sufficient conditions (secondo order and convex case)
Gradient method, Line search
Newton method,
2. Constrained non-linear programming
KKT conditions
Barrier method and Penalty functions
3. Lot Sizing
EOQ model
Wagner-Whitin Algorithm
Zangwill Algorithm
4. Job Shop Scheduling
Exact methods, Carlier-Pinson Algorithm
Euristhic methods, Nowicki-Smutnicki Algorithm
5. Vehicle Routing Problem
6. Crew Scheduling
7. Plant Location
Core Documentation
Lecture notesType of delivery of the course
Classroom lectures and exercises.Type of evaluation
Verification of learning takes place through a selective written test, lasting 90-120 minutes, aimed at verifying the level of actual understanding of the concepts learned in the course and the students' ability to apply them in real contexts. The course also includes an optional written test, which is held after the first part of the course, consisting of two exercises and a theory question, aimed at verifying the level of learning after the first part of the course. The expected time is 90-120 minutes. The exam consists of a written test, consisting of two compulsory exercises and a theory question divided into open-ended points. The expected time is 90-120 minutes. The exam texts of the last years are available on the course website (http://pacciarelli.dia.uniroma3.it/CORSI/MSP/Welcome.html ). teacher profile teaching materials
Gradient, Hessian
Local minimum, Necessary conditions (first and second order)
Local minimum, Sufficient conditions (secondo order and convex case)
Gradient method, Line search
Newton method,
2. Constrained non-linear programming
KKT conditions
Barrier method and Penalty functions
3. Lot Sizing
EOQ model
Wagner-Whitin Algorithm
Zangwill Algorithm
4. Job Shop Scheduling
Exact methods, Carlier-Pinson Algorithm
Euristhic methods, Nowicki-Smutnicki Algorithm
5. Vehicle Routing Problem
6. Crew Scheduling
7. Plant Location
Programme
1. Non-linear programmingGradient, Hessian
Local minimum, Necessary conditions (first and second order)
Local minimum, Sufficient conditions (secondo order and convex case)
Gradient method, Line search
Newton method,
2. Constrained non-linear programming
KKT conditions
Barrier method and Penalty functions
3. Lot Sizing
EOQ model
Wagner-Whitin Algorithm
Zangwill Algorithm
4. Job Shop Scheduling
Exact methods, Carlier-Pinson Algorithm
Euristhic methods, Nowicki-Smutnicki Algorithm
5. Vehicle Routing Problem
6. Crew Scheduling
7. Plant Location
Core Documentation
Lecture notesReference Bibliography
Caramia, Giordani, Guerriero, Musmanno, Pacciarelli, "Ricerca Operativa", Isedi, Italia, 2014. Sassano A., "Modelli e Algoritmi della Ricerca Operativa", Franco Angeli. Carlier J., Pinson E., “An algorithm for solving the job shop problem”, Management Science, 35 (2), 164-175 (1989). Carlier J., Pinson E., “Adjustment of heads and tails for the job-shop problem”, European Journal of Operational Research, 78 (2), 146-161 (1994). Brucker P., Jurisch B., Sievers B., “A branch and bound algorithm for the job scheduling shop problem”, Discrete Applied Mathematics, 49, 107-127 (1994). Nowicki E., Smutnicki C., “A fast taboo search algorithm for the job shop problem”, Management Science, 42 (6), 797-813 (1996). Nowicki E., Smutnicki C., “An advanced tabu search algorithm for the job shop problem”, Journal of Scheduling, 8, 145-159 (2005). Il PDF è scaricabile qui da un PC di Roma Tre. Heinz Gröflin, Andreas Klinkert, "A new neighborhood and tabu search for the Blocking Job Shop", Discrete Applied Mathematics, 157 (2009), 3643-3655.Il PDF è scaricabile qui da un PC di Roma Tre. Yazid Mati and Xiaolan Xie, "Multiresource Shop Scheduling With Resource Flexibility and Blocking", IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, in press Christoph J. Schuster, Jose M. Framinan, "Approximative procedures for no-wait job shop scheduling", Operations Research Letters, 31 (2003) 308 – 318.Il PDF è scaricabile qui da un PC di Roma Tre.Type of delivery of the course
Classroom lectures and exercises.Type of evaluation
Verification of learning takes place through a selective written test, lasting 90-120 minutes, aimed at verifying the level of actual understanding of the concepts learned in the course and the students' ability to apply them in real contexts. The course also includes an optional written test, which is held after the first part of the course, consisting of two exercises and a theory question, aimed at verifying the level of learning after the first part of the course. The expected time is 90-120 minutes. The exam consists of a written test, consisting of two compulsory exercises and a theory question divided into open-ended points. The expected time is 90-120 minutes. The exam texts of the last years are available on the course website (http://pacciarelli.dia.uniroma3.it/CORSI/MSP/Welcome.html ).