Prof. Engr. Mariagrazia Dotoli, PhD

DECISION & CONTROL LABORATORY

Engr. Raffaele Carli, PhD Engr. Graziana Cavone, PhD

Engr. Nicola Epicoco, PhD Engr. Seyed Mohsen Hosseini

Engr. Gioacchino Manfredi Engr. Roberta Pellegrino, PhD

http://dei.poliba.it/DEI-it/ricerca/decision-and-control.html mariagrazia.dotoli@poliba.it

Polytechnic of Bari

Department of Electrical and Information Engineering

Decision and Control Laboratory❑ Mission: fostering the education of undergraduate/graduate students, supporting

courses lab activities, performing theoretical/applied research in a broad spectrum of

topics in systems, control, optimization, and decision-making areas.

❑ Collaborations:

✓ Universities: University of Cagliari (Italy), Ecole Centrale de Lille (France), Aix-

Marseille Université (France),Hamburg Helmut Schmidt University (Germany),

Crakow University of Science and Technology (Poland), University of Manchester

(UK), Chalmers University (Sweden), Universidad Zaragoza (Spain), New Jersey

Institute of Technology of Newark (USA), Delft University of Technology (Holland).

✓ Public companies: Banque Central du Luxembourg, Ferrovie del Sud Est,

Municipality of Bari, ReteGas Bari SpA, AMTAB SpA.

✓ Private companies: Centro Ricerche Fiat, IBM, Enel Distribuzione, Masmec SpA,

Mermec SpA, General Transport Service SpA, Planetek Italia Srl, Divella SpA, Le

Gemme, Tera Srl, Cannillo Srl, SimNT Srl, Primadonna SpA, Veronico Srl, Dream

Project SpA, Gigant Italia Srl.

❑ Recent research areas:

- transport optimization (goods, waste, passengers)

- decision-making techniques (supplier selection, re-engineering, …)

- energy management and energy efficiency (public and private sectors)

- smart city, smart governance, smart mobility

Transport Optimization

Freight transport

- Logistics (internal/external)

- Multimodal terminals

- Intermodal rail-road terminals

- detection of criticalities

- road paths optimization

- yard positioning of containers

- train load planning

- Waste collection and transportation (special and hazardous waste)

Freight Transport: External logistics

Advantages: flexible and

efficient SCN + cooperation and

competitiveness among

stakeholders + time and cost

reduction + what-if analysis

QdwS1=0 S1

Qups1=0

IS1=0

M1 QdwD1=22

QdwS2=2 Qup

D1=25

QupS2=1 S2 QD1,M1=5 SLD1=8

IS2=182 € CD1=5548 €

QdwM1=7 ID1=7842 € ĎR1=(3005;3200;3250) pcs

QM3,S2=2 QM1,S3=4 Qup M1=4 PrD1=2294 € QupR1=31 batches=3100 pcs

SLM1=2 SLR1=20 pcs

QdwS3=9 CM1=484 € QD2,M1=2 QR1,D1=20 CR1=11930 €

QupS3=5 S3 IM1=1790 € SPR1≥3.73 €/pc

IS3=1047€ PrM1=1306 € D1

QR2,D1=2

R1 Qcl,R1=3200 pcs

QM3,S3=1 QM2,S3=4 QD1,M2=8

M2

QdwS4=2 S4

QupS4=2 QD2,M2=25 QR1,D2=11

IS4=160 € QM2,S4=2

QdwM2=33 D2

QupM2=16 Qcl,R2=2102 pcs

QdwS5=0 S5 SLM2=0 R2

QupS5=0 CM2=2300 € QR2,D2=19

IS5=0 QM2,S6=3 IM2=9748 € QdwD2=30

PrM2=7448 € QupD2=27 ĎR2=(1950;2102;2200) pcs

SLD2=5 QupR2=21 batches=2100 pcs

QdwS6=3 S6 CD2=8030 € SLR2=139 pcs

QupS6=1 QD1,M3=12 ID2=12216 € CR2=8128 €

IS6=489 € M3 PrD2=4186 € SPR2≥3.87 €/pc

QM2,S8=7

QdwS7=0 S7

QupS7=0 Qdw

M3=12

IS7=0 QupM3=4

SLM3=6

QM3,S8=1 CM3=446 €

QdwS8=8 S8 IM3=2040 €

QupS8=2 PrM3=1594 €

IS8=1352 €

S5

clients

S1 S2 S3 S4

Aim: strategic design of an agile Supply Chain Network (SCN) multi-buyer multi-seller

under multi-objective and uncertain parameters, performance evaluation, resource

management.

Methodology: cross-efficiency fuzzy/stochastic DEA + fuzzy bargaining game.

- M. Dotoli, N. Epicoco (2018), “Integrated network design of agile resource-efficient Supply Chains under uncertainty”. IEEE Transactions on Systems, Man, and Cybernetics: Systems. In press.

- G. Cavone, M. Dotoli, N. Epicoco, C. Seatzu (2018), “A game-theoretical approach for multi-stage Supply Chains Network Design under uncertainty”. 14th IEEE International Conference on

Automation Science and Engineering (CASE 2018), Aug. 20-24, 2018, Munich (Germany).

- M. Dotoli, N. Epicoco, M. Falagario, F. Sciancalepore (2016), “A stochastic cross-efficiency Data Envelopment Analysis approach for supplier selection under uncertainty”. International

Transactions in Operational Research, vol. 23 (4), pp. 725-748.

Freight Transport: Multimodal terminals

Aim: strategic management of combined freight transport terminals, performance

evaluation, and resource management. Also useful for process re-engineering.

Methodology: discrete event simulation (Petri Nets).

Advantages: standardized and modular modeling + dynamic analysis + simulation and

what-if analysis + application of control policies.

INTERMODAL TERMINAL

OPENING/CLOSING

(for Days/Hours)

ACCESS ROAD

(Entrance for Semitrailers)

ACCESS ROAD

(Exit for Semitrailers - Port)

ACCESS ROAD (Exit for Semitrailers - Customer)

RAILWAY ACCESS (Incoming/Outgoing ITUs

BARI-BOLOGNA Line)

RAILWAY ACCESS (Incoming/Outgoing ITUs

BARI-PIACENZA Line)

OPENING/CLOSING

(for Days)

YARD

STORAGE

Semitrailer trucks

Tractors and ITUs

ITUs

- G. Cavone, M. Dotoli, C. Seatzu, “Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets”. IEEE Robotics and Automation Letters, vol. 1(1), pp. 2-9, 2016.

- G. Cavone, M. Dotoli, C. Seatzu, “A Survey on Petri Net Models for Freight Logistics and Transportation Systems”. IEEE Transactions on Intelligent Transportation Systems, vol. 19(6), pp. 1795-

1813., 2018

- G. Cavone, M. Dotoli, N. Epicoco, C. Seatzu, “Efficient resource planning of intermodal terminals under uncertainty”. 15th IFAC Symposium on Control in Transportation Systems (CTS 2018), June

6-8, 2018, Savona (Italy).

- M. Dotoli, N. Epicoco, M. Falagario, G. Cavone, “A Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals“, IEEE Transactions on Automation Science and

Engineering, vol. 13 (2), pp. 842-857, 2016.

- G. Cavone, M. Dotoli, N. Epicoco, C. Seatzu (2017), “Intermodal terminal planning by Petri Nets and Data Envelopment Analysis”. Control Engineering Practice, vol. 69, pp. 9-22.

Freight Transport: criticalities

Aim: removing criticalities (Pareto principle), also useful for internal logistics and

process re-engineering.

Methodology: integrated approach (UML + VSM + math formulation of Lean Philosophy).

Advantages: materials and information flows analysis, identification of responsabilities,

time, bottlenecks and how they impact on performance.

N observed problemserror

%

profit

abilityquality PV CV AIV

1 lack of immediate information about any extra costs 1 1 0 2 3 5

2 lack of summary information from historical accounts 1 0 0 1 5 6

3 lack of basic information in the process of receiving the order 2 1 1 4 6 10

4 lack of schedules load planning 0 1 1 2 4 6

5 manual management of the security seals on containers 1 0 0 1 2 3

6 manual management of the association between ITU and wagon 1 0 0 1 3 4

7 manual management of creating the load list 1 2 0 3 4 7

8 lack of standardization in the container stocking in the yard 0 2 0 2 7 9

9 lack of optimization criteria in the train composition 0 2 1 3 6 9

10 lack of customer feedback tools 0 1 2 3 2 5

11 lack of clarity on the responsibilities 1 1 1 3 2 5

12 lack of standardized criteria for the management of overbooking 1 1 1 3 5 8

13 lack of standardized criteria for the management of matching 0 2 0 2 6 8

14 lack of schedules unload planning 0 1 1 2 3 5

- G. Cavone, M. Dotoli, N. Epicoco, M. Franceschelli, C. Seatzu, «Hybrid Petri Nets to Re-design Low-Automated Production Processes: the Case Study of a Sardinian Bakery». IFAC-

PapersOnLine, vol. 51(7), pp. 265-270, 2018.

M. Dotoli, N. Epicoco, M. Falagario, N. Costantino, B. Turchiano, “An integrated approach for warehouse analysis and optimization: a case study”. Computers in Industry, vol. 70, pp. 56-69, 2015.

- M. Dotoli, N. Epicoco, M. Falagario, N. Costantino, “A lean warehousing integrated approach: a case study”. Proc. 18th IEEE Conference on Emerging Technologies & Factory Automation (ETFA

2013), September 10-13, 2013, Cagliari (Italy).

- M. Dassisti, M. Dotoli, N. Epicoco, M. Falagario, “Internal logistics integration by Automated Storage and Retrieval Systems: A reengineering case study”. On the Move to Meaningful Internet

Systems: OTM 2012 Workshops. In: Lecture Notes in Computer Science, vol. 7567, pp. 78-82, P. Herrero et al. (Eds), 2012.

Freight Transport: road paths

Aim: matching of delivery and pick-up requests (first/last miles).

Methodology: CVRP(TW) + FFM. (Constraints: max 2 clients per visit; discharge before

loading).

Advantages: combination of load/unload activities, savings (times, costs, resources).

Simple trip Double matching Single matching

Delivery nodeDepot Pickup node Simultaneous

pickup&delivery node

Empty run Loaded run

Path Match Delivery Pick Up Km tk [min]

Routes for customers' ITUs (run time 21.04 sec)

p1 double Putignano Brindisi 224 344

p2 double Gravina Massafra 179 299

p3 double Melfi M. Savoia 208 328

p4 double Foggia M. Savoia 229 349

p5 double Valenzano Monopoli 92 212

p6 simple trip Modugno / 12 72

p7 simple trip Valenzano / 18 78

p8 double Buccino Statte 359 479

Routes for owned 45' ITUs (run time 41.74 sec)

p9 double Trani Buccino 284 404

p10 double Fasano Cavallino 297 417

p11 no match / Statte 142 202

p12 single Andria Andria 92 212

Routes for owned 20' ITUs (run time 22.06 sec)

p13 double Fasano Cavallino 297 417

p14 no match / Putignano 82 142

p15 single Andria Andria 92 212

p16 double Policoro Buccino 356 476

Scheduling for with customers' ITUs (run time 0.053 sec)

path Tv [min]

Tover [min] p1 p2 p3 p4 p5 p6 p7 p8

veh

icle

s

v1 x x x 449 0

v2 x 349 0

v3 x 344 0

v4 x 328 0

v5 x 212 0

v6 x 479 0

Scheduling for owned ITUs (run time 0.0517 s)

path Tv

[min]

Tover

[min] p9 p10 p11 p12 p13 p14 p15 p16

veh

icle

s

v'1 x x 414 0

v'2 x x 354 0

v'3 x 417 0

v'4 x 417 0

v'5 x 404 0

v'6 x 476 0

- M. Dotoli, N. Epicoco, M. Falagario, “A model for the optimal management of containers’ drayage at intermodal freight terminals”. 2016 IEEE International Conference on Robotics and

Automation (ICRA 2016), May 16-21, 2016, Stockholm (Sweden).

- M. Dotoli, N. Epicoco, M. Falagario, B. Angelico, A. Vinciullo, “A two-step optimization model for the pre- and end-haulage of containers at intermodal freight terminals”. Proc. 14th IEEE

European Control Conference (ECC 2015), July 15-17, 2015, Linz (Austria), pp. 3477-3482.

Freight Transport: Yard positioning

Aim: maximize the yard fullfillment.

Methodology: Knapsack problem (Constraints: capacity, stackability, economic and

commercial value, stringency, customers importance).

Advantages: profit increase, reduction of removals, cost, and time, informatization,

better use of resources, departures/arrivals management.

M. Dotoli, N. Epicoco, M. Falagario, C. Seatzu, B. Turchiano, “A Decision Support System for optimizing operations at intermodal railroad terminals”. IEEE Transactions on Systems Man and

Cybernetics: Systems, vol. 47 (3), pp. 487-501, 2017

Railway transport optimization

(freights and passengers)

- G. Cavone, M. Dotoli, N. Epicoco, C. Seatzu (2017), “A decision making procedure for robust train rescheduling based on Mixed Integer Linear Programming and Data Envelopment Analysis”.

Applied Mathematical Modelling, vol. 52, pp. 255-273.

- M. Dotoli, N. Epicoco, C. Seatzu, “An improved technique for train load planning at intermodal rail-road terminals”. Proc. 20th IEEE International Conference on Emerging Technologies and

Factory Automation (ETFA 2015), September 8-11, 2015, Luxembourg.

- M. Dotoli, N. Epicoco, M. Falagario, D. Palma, B. Turchiano, “A train load planning optimization model for intermodal freight transport terminals: a case study”. Proc. 2013 IEEE International

Conference on Systems, Man, and Cybernetics (SMC 2013), October 13-16, 2013, Manchester (UK).

Aim: global optimization + offline scheduling of the timetable + real-time management

of disturbances.

Methodology: MILP + heuristics, PES, re-scheduling (Constraints: dimension; weight;

priorities; customers importance; empty ITUs + railway constraints + robustness +

reshuffle for the train’s head/tail positioning of prosecuting ITUs).

Advantages: max profit, handling overbooking + min delays + user-friendly GUI,

management of intermediate stops and contingencies (discarded ITU or new urgency).

.

MILP problem time

horizon

First level:

trains rescheduling

(Rescheduling

Mathematical Model)

Second level:

robustness evaluation

(Validation Model)

Third level:

heuristics

(Conflict Identification &

Resolution)

Number of instances

Time Window

Waste Management

Aim: scheduling and routing of the special and hazardous waste collection.

Methodology: CVRP(TW) + HWM (Constraints: vehicles capacity (mass and volume),

availability, drivers break, working hours limits, segregation of immiscible waste,

multiple time windows).

Advantages: route optimization and fleet/resource management, cost/time savings.

M. Dotoli, N. Epicoco (2017), “A vehicle routing technique for hazardous waste collection”. 20th World Congress of the International Federation of Automatic Control (IFAC 2017), July 9-14, 2017,

Toulouse (France). IFAC-PapersOnLine, vol. 50 (1), pp. 9694-9699.

Process Re-engineering

Aim: re-engineering, automation, and optimization of processes.

Methodology: First-Order Hybrid Petri Nets.

Advantages: simulation of the As-Is behavior, bottlenecks and waste sources

detection, what-if analysis, improvement of the production process.

- G. Cavone, M. Dotoli, N. Epicoco, M. Franceschelli, C. Seatzu, «Hybrid Petri Nets to Re-design Low-Automated Production Processes: the Case Study of a Sardinian Bakery». IFAC-

PapersOnLine, vol. 51(7), pp. 265-270, 2018.

Smart City Management

Energy efficiency

- «Res Novae» Project

- «UCCSM» Project

Smart mobility

- «Semina» Project

RES NOVAE Project(Reti Edifici Strade Nuovi Obiettivi Virtuosi per l’Ambiente e l’Energia)Networks, buildings, roads: new virtuous objectives for the environment and energy

Work

context

Research

areas

Bari

Cosenza

• Scientific Management: ENEA• Project Management: Enel Distribuzione• Project duration: 36 months (from 01/11/12 to

31/10/2015)• Program: “Smart Cities and Communities and

Social Innovation” • Budget: about 24 M€

Aim: Develop an integrated urban planning solution to increase city performance in terms of

energy efficiency and environmental sustainability while meeting citizens’ expectations for quality

of life.

RES NOVAE Project(Reti Edifici Strade Nuovi Obiettivi Virtuosi per l’Ambiente e l’Energia)Networks, buildings, roads: new virtuous objectives for the environment and energy

Smart District

Urban Data Center

Smart Grids

Energy

Environment

MobilitySmart Street ControlPublic Light controlSmart urban objects

Building Diagnostics& ControlActive DemandManagement

Urban renewables and ecobuildings

Smart Grid

Distributed generation

Distributed storage

Aim: create a platform for monitoring andmanaging urban dynamics and city performance,to support the energy governance for PublicAdministrations and to enable communicationto citizens and participation of communities.

Advantages:

⚫ Indicators dashboard: report and analysis of urban performance (status, history, predicted state).

⚫ Business intelligence tools for strategic decision making and planning of urban sectors.

⚫ Collaboration tool for citizens’ awareness and active involvement.

UCCSM Project(Urban Control Center per le Smart city Metropolitane)Urban Control Center for the metropolitan smart cities

➢ Tools for developing smart cities

Two main functionalities are presented for smart cities monitoring and management:

⚫ the measure and monitoring of the smart city performance

⚫ the definition of strategic action programs as result of decision making and planning

.

2. decision making

support tools

1. Dashboard of KPIs

smart city

KPIs target values

Current KPI values

Decision

making

KPIs

Evaluation

City Urban dynamics

Action plans

Disturbance factors List of potential action plans

City

Policies

Objectives

Urban Control Center

Monitoring, management, and control of smart cities

Aim: Development of a multi-criteria technique to

support the Public Administration in the analysis

and benchmarking of the sustainability of

energy/water/environment systems in metropolitan

areas.

Methodology: AHP (Analytic Hierarchy Process)

– Case study:

35 SDEWES (Sustainable Development of

Energy, Water, and Environment Systems)

indicators

Metropolitan City of Bari (Bari, Bitonto, Mola,

Molfetta)

➢ Benchmarking of metropolitan areas

- R. Carli, M. Dotoli, R. Pellegrino (2018), “Multi-criteria Decision-Making for Sustainable Metropolitan Cities Assessment,” Journal of Environmental Management , vol. 226, pp. 46-61.

Monitoring, management, and control of smart cities

Aim: Define strategic action plans in metropolitan areas (multi-criteria, with multiple

decision makers, under uncertain data).

Methodology: Fuzzy Analytic Hierarchy Process (FAHP) Group Decision Analysis

Case study:

35 SDEWES (Sustainable Development of Energy, Water, and Environment Systems)

indicators

Metropolitan City of Bari (11 strategic areas)

22 EU projects aggregated by budget in 4 action plans

➢ Planning

- R. Carli, M. Dotoli, N. Epicoco, R. Pellegrino, “Defining sustainable action plans in metropolitan cities using the fuzzy Analytic Hierarchy Process group decision analysis”. 13th Conference on

Sustainable Development of Energy, Water and Environment Systems (SDEWES 2018), Sept. 30-Oct. 4, 2018, Palermo (Italy).

Monitoring, management, and control of smart cities

➢ Local decision-making panels

LDP #1 – Street Lighting Panel

Aim: Determine the optimal set of actions to beimplemented in the given existing street lightingsystem in order to reduce energy consumption,maintaining the required comfort and quality of life,protecting the environment, while not exceeding thegiven budget constraint.

Problema di Ottimizzazione LDP #1

Methodology: bi-level decentralized hierarchical constrained optimization.

⚫ Decision variables:

⚫ Luminaire replacement: ust = quantity of lamps of the t-th type to be replaced in the s-th zone lighting subsystem

⚫ Energy harvesting module installation: vs = quantity of energy harvesting devices to be installed in the s-th zone lighting subsystem

⚫ Zonal dimming device installation: ws = binary variable of unit value if dimming has to be integrated in the control station of s-th zone lighting subsystem (zero otherwise)

- R. Carli, M. Dotoli, R. Pellegrino (2018), “A decision-making tool for energy efficiency optimization of street lighting,” Computers and Operations Research , vol. 96, pp. 223-235.

Monitoring, management, and control of smart cities

- Carli, R.; Dotoli, M.; Pellegrino, R., «A decision making technique to optimize a building stock energy efficiency», IEEE Trans. Sys., Man Cyb.: Systems, 2015.

LDP #2 – Public Buildings Panel

Aim: Determine the optimal set of actions toimplement on buildings portfolio in order to makebuildings more energy efficient, moreenvironmentally sustainable, more comfortablefor occupants, while not exceeding the givenbudget constraint.

Methodology: two-level binary multi-objective optimization

Problema di Ottimizzazione LDP #2

➢ Local decision-making panels

s.t.

Decision variables

=else 0

applied isaction if 1jkx

Action cost

Criterion-dependent payoffBudget

Criteria

Criterion-dependent objective function

Monitoring, management, and control of smart cities

➢ Strategy for the multisectoral efficiency improvement

BLDP

PAYOFFS AND COSTS FOR PUBLIC BUILDINGS PORTFOLIO

PAYOFFS AND COSTS FOR STREET LIGHTING SYSTEM

TOTAL BUDGET OPTIMAL BUDGET ALLOCATION

OPTIMAL RETROFIT PLAN FOR PUBLIC BUILDINGS

Parameter

Subsystem 1 2 3 4 5 6 7 8 9 10

Type 1 0 0 0 0 0 1 5 52 5 27

2 27 0 0 0 0 2 18 2 24 18

Parameter

Subsystem 1 2 3 4 5 6 7 8 9 10

0 0 0 0 0 0 0 0 0 0

Parameter

Subsystem 1 2 3 4 5 6 7 8 9 10

1 1 1 1 1 1 1 1 1 1

OPTIMAL RETROFIT PLAN FOR STREET LIGHT. SYS.

Action

Bu

ild

ing

0 0 0 1 1 1 1

0 0 1 1 1 1 1

0 1 0 1 1 1 1

0 0 1 1 1 1 1

0 0 1 1 1 1 1

- Replacing half the lamps, dimming of all lamps.

- Roof thermal insul. B2, new windows B2, B4, B5.

- All buildings: new boilers, thermostatic radiator valves, water tap aerators, high efficiency lamps.

Monitoring, management, and control of smart cities

- Carli, R., Dotoli, M., Pellegrino, R., “A Hierarchical Decision Making Strategy for the Energy Management of Smart Cities”, IEEE TASE (Transactions on Automation Science and Engineering),

vol. 14, no. 2, pp. 505-523, April 2017.

Aim: provide a smart energy scheduling for

public (or private) buildings under uncertain data.

Methodology: constrained fuzzy optimization

The system comprises:home appliances (uninterruptible passive loads

distinguished into non-shiftable and shiftable loads)

renewable energy sources (photovoltaic or wind

generators)

dispatchable energy sources (microturbines or fuel cells)

energy storage systems (batteries)

Advantages: full exploitation of the potential of

local energy generation and storage to reduce the

individual user energy consumption cost and PAR

(Peak to Average Ratio)

➢ Energy scheduling for smart buildings / smart homes

Energy Scheduling

- R. Carli, M. Dotoli, N. Epicoco, “Cost-optimal energy scheduling of a smart home under uncertainty”. 2nd IEEE Conference on Control Technology and Applications (CCTA 2018), Aug. 21-24,

2018, Copenhagen (Denmark)

- R. Carli and M. Dotoli, "A decentralized resource allocation approach for sharing renewable energy among interconnected smart homes," in Decision and Control (CDC), 2015 54th IEEE

Conference on, Osaka, 2015.

- R. Carli and M. Dotoli, “Energy scheduling of a smart home under nonlinear pricing,” in Decision and Control (CDC), 2014 IEEE 53rd Conference on, Los Angeles, CA, 15-17 Dec. 2014.

Aim: provide a day-ahead scheduling technique

for the optimal charging of a large-scale fleet

of Electric Vehicles (EVs)

.

Methodology: iterative distributed / decentralized

algorithm based on duality, proximity, and

consensus theory

Advantages: ensuring a cost-optimal profile of the

aggregated energy demand, complying with the EV

individual needs; satisfying the resource constraints

depending both on power grid components capacity and

EV locations in the distribution network; using a minimal

information structure, where EVs locally communicate

only with their neighbours, without relying on a central

decision maker.

➢ Energy scheduling for smart buildings / smart homes

Energy Scheduling

- R. Carli ; M. Dotoli, "A Distributed Control Algorithm for Waterfilling of Networked Control Systems via Consensus," in IEEE Control Systems Letters, vol. 1, no. 2, pp. 334-339, Oct. 2017.

- Carli, R.; Dotoli, M.; “Distributed Control for Waterfilling of Networked Control Systems with Coupling Constraints”, Decision and Control (CDC), 2018 57th IEEE Conference on, Miami, 2018.

- Carli, R.; Dotoli, M., “A Distributed Control Algorithm for Optimal Charging of Electric Vehicle Fleets with Congestion Management,” 15th IFAC Symposium on Control in Transportation Systems

(CTS 2018), June 6-8, 2018, Savona, Italy.

- Carli, R.; Dotoli, M., “Decentralized Optimal Charging Control of Electric Vehicle Fleets with Congestion Management,” 2017 IEEE International Conference on Service Operations and

Logistics, and Informatics, September 18-20, 2017, Bari, Italy, p. 63-67.

Bari

• Funding: Apulia Region Research and Innovation Bureau • Program: Apulian ICT Living Labs SMARTPUGLIA 2020• Contact person: prof. Mariagrazia Dotoli (Project Supervisor)• Budget: € 680.972,00; Poliba Budget: € 130.000,00• Number of partners: 4

Smart Mobility: SEMINA Project(Sistemi Evoluti per la Mobilità Intelligente in Network urbani Agili)Evolved systems for smart mobility in agile urban networks

Aim: Innovative design of an ICT system to provide the citizens and the Public

Administration of a smart city with useful information about urban mobility.

• The urban mobility Information System has been developed and prototyped by the

Polytechnic of Bari in collaboration with local SMEs for the Municipality of Bari

(Italy)

Smart Mobility: SEMINA Project(Sistemi Evoluti per la Mobilità Intelligente in Network urbani Agili)Evolved systems for smart mobility in agile urban networks

Aim: use of GPS data to evaluate bus transport

system conditions and to monitor the performance of

the urban roads network.

Data source: local bus fleet monitored by GPS signals

(100 tracked buses/die; 120.000 GPS traces/die)

Advantages: low cost of setup and implementation,

automatic data storage, scalability to other probe

vehicles.

Methodology: «bus-as-probe».

➢ Urban mobility Information System

Smart Mobility: SEMINA Project(Sistemi Evoluti per la Mobilità Intelligente in Network urbani Agili)Evolved systems for smart mobility in agile urban networks

Three levels of analysis:

1. Statistical analysis of the performances of

local public transport bus services

2. Statistical analysis of urban traffic

characteristics

3. Real Time monitoring of urban traffic

congestion

➢ Urban mobility Information System

Smart Mobility: SEMINA Project(Sistemi Evoluti per la Mobilità Intelligente in Network urbani Agili)Evolved systems for smart mobility in agile urban networks

Results:

⚫ Bus line 53, inbounding route, trip scheduled at 08:05 a.m.

⚫ Weekdays statistical analysis in June 2014

Punctuality Index

Schedule Adherence

Stop ID

Time

[min]

+/- 3σ

Mean Level

advance

(Suburb Area)

delay

(City Centre)

dela

yad

av

nc

e

Planned

➢ Analysis of local public transport bus services

Smart Mobility: SEMINA Project(Sistemi Evoluti per la Mobilità Intelligente in Network urbani Agili)Evolved systems for smart mobility in agile urban networks

- R. Carli, M. Dotoli, N. Epicoco (2018), “Monitoring traffic congestion in urban areas through probe vehicles: A case study analysis”. Internet Technology Letters, vol. 1 (4), 1:e5, 7 pp.

- R. Carli, M. Dotoli, N. Epicoco, B. Angelico, A. Vinciullo, "Automated evaluation of urban traffic congestion using bus as a probe" IEEE CASE 2015.

Results:⚫ Weekdays statistical analysis in June 2014

Piazza Garibaldi

Urban Coverage by GPS data

Travel Time index:-Time window: from 09:00 to 10:00 am

City centre

Worst Conditions

Ideal Conditions

Travel Time index

- weekdays

➢ Analysis of the urban traffic characteristics

Smart Mobility: SEMINA Project(Sistemi Evoluti per la Mobilità Intelligente in Network urbani Agili)Evolved systems for smart mobility in agile urban networks

Spin-off

Members: - Dr. Er. Mariagrazia Dotoli

- Dr. Er. Marco Falagario

- Dr. Er. Giuseppe Ardito

- Politecnico di Bari

Board: - Dr. Er. Nicola Epicoco (Chair)

- Prof. Er. Marco Bronzini

- Dr. Generosa Tagliente

Office: Via De Romita n. 22 - Bari

VAT number: 07392070723

Main activities:

Technological transfer of technical and scientific know-how to public and

private companies;

Consultancy, training, and assistance in:

▪ management and control engineering;

▪ energy savings and renewable sources;

▪ continuous improvement and quality control;

▪ management of private companies and optimization of resources in public bodies;

▪ re-engineering of business processes;

▪ organization, management, and control models (Italian Leg. Dec. 231/01).

Spin-off

Public Procurement

Application of MCDA multi-criteria decision analysis techniques under uncertainty

for the Public Procurement sector (with transparency, proportionality, equity, and non-

discrimination requirements).

Select the Best Bidder

Overall quality

Remun.

Price

Qualif. Organ. Quality

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20

S1 S2 S3 S4 S5 S6 S7

…

M. Dotoli, N. Epicoco, M. Falagario (2018), “Multi-Criteria Decision Making techniques for the management of public procurement tenders: A case study”. Applied Soft Computing. Under review.

Process analysis and re-engineering

- M. Dotoli, N. Epicoco, M. Falagario, N. Costantino, B. Turchiano (2015), “An integrated approach for warehouse analysis and optimization: A case study”. Computers in Industry, vol. 70 (1), pp. 56-

69.

- M. Dotoli, N. Epicoco, M. Falagario, F. Sciancalepore (2015), “A cross-efficiency fuzzy Data Envelopment Analysis technique for performance evaluation of decision making units under uncertainty”.

Computers and Industrial Engineering, vol. 79, pp. 103-114.

- M. Dotoli, N. Epicoco, M. Falagario, F. Sciancalepore, N. Costantino (2014), “A Nash equilibrium simulation model for the competitiveness evaluation of the auction based day ahead electricity

market”. Computers in Industry, vol. 65 (4), pp. 774-785.

Analysis of the current state and criticalities, and of functional requirements and

obtainable improvements. Support in implementing corrective measures, monitoring

and control for a continuous improvement.

DSS

Decision Support Systems

Development of multi-objective optimization tools characterized by:

- ease of use and interpretation of results;

- interactive environment to perform simulations and what-if analysis;

- direct support to decision-making;

- effectiveness in the use of models and in data analysis.

Procedure protected by copyright at the OLAF section of the SIAE

Organization, management, and control models

Company reorganization and definition of appropriate procedures to prevent the

liability of entities pursuant to the Italian Legislative Decree 231/01, reducing the risk

of committing crimes and mitigating their possible impact on the company.

Partecipation in Supervisory Boards for public and private companies.

Prof. Engr. Mariagrazia Dotoli, PhD

DECISION & CONTROL LABORATORY

Engr. Raffaele Carli, PhD. Engr. Graziana Cavone, PhD

Engr. Nicola Epicoco, PhD Engr. Seyed Mohsen Hosseini

Engr. Gioacchino Manfredi Engr. Roberta Pellegrino, PhD

http://dei.poliba.it/DEI-it/ricerca/decision-and-control.html mariagrazia.dotoli@poliba.it

Polytechnic of Bari

Department of Electrical and Information Engineering