Name of Host Organization

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa

Department or Lab

MagIC (Information Management Research Center) - the NOVA IMS research and development center

Name of Building

Manuel Vilares Building

Physical Address

Campus de Campolide, 1070-312 Lisboa

Website Links

https://www.novaims.unl.pt/

Institutional contact name

Cristina Oliveira

Institutional contact email

magic@novaims.unl.pt

Principal Investigator Name

Professor Ian James Scott

Position / institutional role

Assistant Professor

ORCID

0000-0001-9699-4473

Email

iscott@novaims.unl.pt

TestBed Responsible Name
(if different from PI)

Funding source(s)
for TestBed’s acquisition

This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

Application case:

Short description:

Supply Chain Optimization

Formulate and solve large-scale MIP models to optimize inventory levels, distribution routes, and supplier selection across complex supply networks

Production Scheduling

Develop LP and MIP models to optimize manufacturing schedules, minimizing costs and maximizing throughput while respecting resource and time constraints

Energy Systems Planning

Apply optimization models to support renewable energy integration, grid load balancing, and cost-efficient energy distribution planning

Portfolio Optimization

Use linear and quadratic programming techniques to optimize financial asset allocation, balancing risk and return under real-world constraints

Non-academic stakeholders

Industrial partners, SMEs, Startups, Government bodies, Professional associations, Public agencies and municipalities

Academic stakeholders

MSc students, PhD students, Researchers, Visiting researchers, Seconded researchers

Other types of stakeholders

R&I support professionals, R&I infrastructure operators, Innovation intermediaries, Technology transfer actors

TRL application range

4

Internal academic research

Yes

Collaborative research with external academic partners

Yes

Contract research / Proof-of-Concept for industry

Yes

Pilot / DeepTech Deployment in operational environment

No

Training services (courses, workshops, certification)

Yes

Service provision (testing, benchmarking, validation)

Yes

Open access for walk-in users (e.g. open days / hackathons)

No

Other (Secondments / sponsored access for visiting researchers under project-based or institutionally approved arrangements)

Yes

Type of contractual relationship

Academic partner

Industrial partner

No contract (direct access)

No

No

Direct contract between parties
(e.g., research agreement)

Yes (See Note 1)

Yes (See Note 1)

Indirect contract between parties
(e.g., project framework)

Yes (See Note 1)

Yes (See Note 1)

Type of prerequisites

Description of prerequisites

Agreements

Confidentiality agreement for proprietary algorithms

In some cases (See Note 2)  

Data sharing agreement for datasets generated

In some cases (See Note 2)

IP agreements

In some cases (See Note 2)

Other 

In some cases (See Note 3 and 4)

Insurance

Users must have appropriate liability coverage through their home institution

Yes

Mandatory technical training

N/A

Recommended technical training

Recommended training on cluster operation, job submission, queuing in SLURM.

Mandatory safety requirements

N/A

Components:

Description:

Hardware

(physical equipment available in this TestBed)

1) NOVA DAH01 System Specifications:

a) CPU: 32-Core CPU - This processor provides a significant amount of processing power, enabling users to run multiple demanding tasks simultaneously, such as simulations, data processing, and other compute-intensive workloads.

b) GPU: 2 x Nvidia RTX 6000 ADA - These high-performance GPUs are designed to accelerate AI, HPC, and other GPU-accelerated workloads. With two RTX 6000 ADA GPUs, users can leverage massive parallel processing capabilities, handling large amounts of data and providing a substantial boost to performance.

c) Storage: 7TB NVMe Storage - This high-capacity storage solution provides rapid data access and transfer speeds, ideal for applications that require high-performance storage, such as data analytics, scientific simulations.

2) NOVA DAH02 System Specifications:

a) CPU: 112 CPU cores, providing a substantial amount of processing power for compute-intensive tasks. This will enable users to run multiple simulations, data processing, and other tasks concurrently.

b) GPU: 2 x Nvidia H100 NVL (Next-Generation High-Performance Computing) GPUs, which offer significant performance boosts for AI, HPC, and other GPU-accelerated workloads. The H100 NVL GPUs are designed to handle massive amounts of data and provide high-performance computing capabilities.

3) NOVA DAH WS includes:

a) 16 Lenovo Thinkstations P5 units, each equipped with an Intel(R) Xeon(R) W3-2423 processor, 32 GB DDRS-4800 MHz ECC memory, NVIDIA RTX(R) 2000 GPU with 16 GB GDDR6 (Ada Generation), and 1 TB PCIe Neg4 SSD.

b) Operating system Windows 11 Education,

c) Broad range of licensed and open-source software for data science, analytics, modelling, and visualisation, including but not limited to a broad range of licensed and open-source software for data science, analytics, modelling, and visualization, including but not limited to Python, R, Power BI, Tableau, SPSS, SAS, QGIS, ArcGIS, Docker, Anaconda, Visual Studio Code, and Zotero.

c) Storage: 500 TB of storage, providing ample space for storing large datasets, applications, and other data. This storage capacity will enable users to work with big data and store the results of their computations.

4) Others

Software

(needed to run
the TestBed)

1) SSH client

2) File transfer tools recommended

3) Apptainer runtime to test locally

Standards and regulations
(relevant for the safe and compliant operation of this TestBed)

N/A

Type

Short description:

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH01)

Tutorial

How to connect to NOVA DAH (Tutorial NOVA DAH02)

Ethical and societal
aspect:

Short description:

Improving Quality of Life Through Smarter Resource Allocation

Optimization models can help public institutions and governments allocate scarce resources more fairly and efficiently, such as distributing healthcare services, emergency response units, or social support programs. For non-technical audiences, this means faster ambulance response times, better-staffed hospitals, and more equitable access to public services.

Supporting Sustainable Development and Climate Goals

By enabling organizations to minimize waste, reduce energy consumption, and optimize logistics routes, OPTIMAX directly contributes to environmental sustainability goals aligned with the European Green Deal. For example, optimizing delivery routes reduces fuel consumption and carbon emissions, contributing to cleaner cities and a lower carbon footprint.

Enhancing Worker Well-Being and Human-Centric Scheduling

Optimization tools can be used to design fair and balanced work schedules that respect employee preferences, avoid overwork, and ensure adequate rest periods. This is particularly relevant in Industry 5.0, where human-centric production planning prioritizes worker well-being alongside operational efficiency.

Ethical Use in Public Policy and Social Planning

Optimization models used to inform public policy decisions, such as school district boundaries, social housing allocation, or public transport planning, carry significant ethical responsibilities. OPTIMAX promotes responsible model design by encouraging users to explicitly incorporate fairness constraints, ensuring that optimization outcomes do not inadvertently disadvantage vulnerable or marginalized communities.