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Enterprise Systems Engineering

At CESE, we use the knowledge generated in research to provide high value-added niche services to industrial enterprises in areas such as Manufacturing Systems Design, Manufacturing Systems Planning and Management, Collaborative Platforms, Supply Chain Strategy, Manufacturing Intelligence or Construction Information Management.

Our mission is to advance the scientific knowledge in enterprise systems engineering, fostering high impact management and ICT systems, and generating innovative services for industrial organisations.

We want to be recognised as a leading research centre in enterprise systems engineering and as a first choice in helping industrial organisations to achieve sustainable, high-performance levels.

Latest News

How to create more “sustainable” logistics chains? This discussion will take place in Porto – with INESC TEC’s involvement

The EurOMA Sustainability forum will bring together researchers from all over the world to discuss and rethink the current linear model of supply and demand, and show how companies can adopt regenerative and restorative operations that have a positive environmental and social impact. Porto will host the event over the next two years.

17th October 2024

Disruptions in supply chains are a major issue for SMEs – INESC TEC has a model to help them addressing this problem

A resilient supply chain must be able to innovate and adapt to new realities. The RISE-SME project relies on INESC TEC to provide supply chain stakeholders with more solutions to detect and anticipate disruptions. In Portugal, 99.9% of the business fabric features SMEs.  

17th October 2024

New course to help companies face digitalisation challenges scheduled for October

"Shop floor digitalisation – making digitalisation happen in the Industry"; this is the name of the new training programme organised by INESC TEC and INEGI, scheduled for October. The programme is designed to help companies face the challenges of digitalisation and applications are already open.

16th July 2024

Systems Engineering and Management

Sustainable deliveries: researchers aim to support retailers in addressing environmental impact of online shopping

Online shopping and just-in-time delivery – or almost. We became familiarised with this scenario over the past few years, mainly because of the COVID-19 pandemic. The environmental impact of new retail strategies is not yet fully known, particularly regarding new business models and consumption habits, associated with e-commerce; this was the starting point of the e-LOG project, led by INESC TEC, which has now come to an end.

17th April 2024

Systems Engineering and Management

Researchers support the manufacturing industry by anticipating disruptions in supply chains

Over the next three years, a group of European researchers, including a team from INESC TEC, will work on the development of management models to support the manufacturing industry – namely small and medium-sized enterprises (SMEs); the goal is to identify potential disruptions in current and future supply chains. This work will take place within the European RISE-SME project, focusing on four industrial ecosystems: Agro-food, Digital, Mobility and Transportation, and Textile.

11th March 2024

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Featured Projects

SustainableTestBed

Plataforma Rastreabilidade Produto para infraestrutura TestBed de sustentabilidade

2024-2025

C2T_GLASS

Maturity assessment and definition of an implementation roadmap for Digital Twin

2023-2023

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001

Laboratories

Laboratory of Industrial Robotics and Automation

Publications

CESE Publications

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2024

D3S: Decision support system for sectorization

Authors
Öztürk, EG; Rocha, P; Rodrigues, AM; Ferreira, JS; Lopes, C; Oliveira, C; Nunes, AC;

Publication
DECISION SUPPORT SYSTEMS

Abstract
Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

2024

How to know it is "the one"? Selecting the most suitable solution from the Pareto optimal set. Application to sectorization

Authors
Öztürk, EG; Rodrigues, AM; Ferreira, JS; Oliveira, CT;

Publication
OPERATIONS RESEARCH AND DECISIONS

Abstract
Multi -objective optimization (MOO) considers several objectives to find a feasible set of solutions. Selecting a solution from Pareto frontier (PF) solutions requires further effort. This work proposes a new classification procedure that fits into the analytic hierarchy Process (AHP) to pick the best solution. The method classifies PF solutions using pairwise comparison matrices for each objective. Sectorization is the problem of splitting a region into smaller sectors based on multiple objectives. The efficacy of the proposed method is tested in such problems using our instances and real data from a Portuguese delivery company. A non -dominated sorting genetic algorithm (NSGA-II) is used to obtain PF solutions based on three objectives. The proposed method rapidly selects an appropriate solution. The method was assessed by comparing it with a method based on a weighted composite single -objective function.

2024

Many-objective sectorization for last-mile delivery optimization: A decision support system

Authors
Torres, G; Fontes, T; Rodrigues, AM; Rocha, P; Ribeiro, J; Ferreira, JS;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
The efficient last-mile delivery of goods involves complex challenges in optimizing driver sectors and routes. This problem tends to be large-scale and involves several criteria to meet simultaneously, such as creating compact sectors, balancing the workload among drivers, minimizing the number of undelivered packages and reducing the dissimilarity of sectors on different days. This work proposes a Decision Support System (DSS) that allows decision-makers to select improved allocation strategies to define sectors. The main contribution is an interactive DSS tool that addresses a many-objective (more than 3 objectives) sectorization problem with integrated routing. It establishes a global allocation strategy and uses it as a benchmark for the created daily allocations and routes. A Preference-Inspired Co-Evolutionary Algorithm with Goal vectors using Mating Restriction (PICEA-g-mr) is employed to solve the many-objective optimization problem. The DSS also includes a visualization tool to aid decision-makers in selecting the most suitable allocation strategy. The approach was tested in a medium-sized Metropolitan Area and evaluated using resource evaluation metrics and visualization methods. The proposed DSS deals effectively and efficiently with the sectorization problem in the context of last-mile delivery by producing a set of viable and good-quality allocations, empowering decision-makers in selecting better allocation strategies. Focused on enhancing service efficiency and driver satisfaction, the DSS serves as a valuable tool to improve overall service quality.

2024

Deep Reinforcement Learning-Based Approach to Dynamically Balance Multi-manned Assembly Lines

Authors
Santos, R; Marques, C; Toscano, C; Ferreira, M; Ribeiro, J;

Publication
Lecture Notes in Mechanical Engineering

Abstract
Assembly lines are at the core of many manufacturing systems, and planning for a well-balanced flow is key to ensure long-term efficiency. However, in flexible configurations such as Multi-Manned Assembly Lines (MMAL), the balancing problem also becomes more challenging. Due to the increased relevance of these assembly lines, this work aims to investigate the MMAL balancing problem, to contribute for a more effective decision-making process. Therefore, a new approach is proposed based on Deep Reinforcement Learning (DRL) embedded in a Digital Twin architecture. The proposed approach provides a close-to-reality training environment for the agent, using Discrete Event Simulation to simulate the production system dynamics. This methodology was tested on a real-world instance with preliminary results showing that similar solutions to the ones obtained using optimization-based strategies are achieved. This research provides evidence of success in terms of dynamic resource assignment to tasks and workers as a basis for future developments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2024

Towards a more inclusive mobility: participatory mobility planning at a metropolitan scale

Authors
Carvalho J.; de Sousa J.P.; Macário R.;

Publication
Transportation Research Procedia

Abstract
Participatory processes are an essential aspect of collaborative planning and decision-making processes, but designing such processes effectively can be quite challenging. This work departs from the assumptions that in sustainable urban mobility planning, the functional urban area needs to be considered, and that citizen engagement is often enacted at the neighborhood level. Under these assumptions, we have examined the experiences of 6 metropolitan cases (Bologna, Nantes, Manchester, Montreal, Christchurch, and Santiago de Chile) and draw insights from their experiences. We conclude this work with some general reflections on the importance of systemic approaches to effectively plan for sustainable transitions in urban mobility.

Facts & Figures

0Book Chapters

2020

23Senior Researchers

2016

8Proceedings in indexed conferences

2020