A risk-based approach applied to system engineering projects: a new learning based multi-criteria decision support tool based on an ant colony algorithm

This article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system’s conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Scenario selection optimization in system engineering projects under uncertainty: a multi-objective ant colony method based on a learning mechanism

This paper presents a multi-objective Ant Colony Optimization (MOACO) algorithm based on a learning mechanism (named MOACO-L) for the optimization of project scenario selection under uncertainty in a system engineering (SE) process. The objectives to minimize are the total cost of the project, its total duration and the global risk. Risk is considered as an uncertainty about task costs and task durations in the project graph. The learning mechanism aims to improve the MOACO algorithm for the selection of optimal project scenarios in aSE project by considering the uncertainties on the project objectives. The MOACO-L algorithm is then developed by taking into account ants’ past experiences. The learning mechanism allows a better exploration of the search space and an improvement of the MOACO algorithm performance. To validate our approach, some experimental results are presented

Towards an integration of systems engineering and project management processes for a decision aiding purpose

This article proposes an integrated process that combine Systems engineering processes and project management ones. These processes are defined according to the industrial standard processes existing in the literature. The main idea is to define a common information model enabling the federation of all the points of view of the different actors with regards to systems engineering, project time management, project cost management and project risk management. The resulting integrated project graph encompasses all the scenarios established after defining all the coupling points between those processes. The definition of the graph is based also on the available knowledge and the capitalized experiences resulting from experience feedback on previous projects. The scenario selection optimization is then performed using a decision-aided tool that aims to build a panel of pareto-optimal solutions taking into account uncertainties on project objectives (cost and duration). This tool will also enable the decision-maker to select one scenario according to an acceptable level of risks. The integrated process, the optimization tool based on ant colony optimization (ACO) and the method for decision making are described in the paper

A MULTI-OBJECTIVE MODEL FOR TIME–COST–QUALITY–RISK TRADE-OFF PROBLEMS IN PROJECT MANAGEMENT

This study presents a weighted four-dimensional time-cost-quality-risk trade-off problem to assist decision-makers in planning the best possible use of resources. The proposed model aims to minimize time and cost while maximizing quality and safety and to ensure that the project is completed as required. The critical path method was used to calculate the completion time, the analytical hierarchy process method was used to determine the weights of the quality parameters, and the 3T risk assessment method was used to calculate the risk values. The algorithm was coded in GAMS and optimized using CPLEX. A construction project with a deadline of 310 days, a budget of 5,250,000 ₺, 88% quality and a safety index (SI) of 77% was selected to analyze the accuracy of the model. The model achieved a solution with a completion time of 310 days, costs amounting to 5,247,775 ₺, 88.036% quality, and 77.338% SI

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

COMPREHENSIVE FRAMEWORKS FOR DECISION MAKING SUPPORT IN MEDICAL EQUIPMENT MANAGEMENT

Throughout medical equipment life cycle, hospitals need to take decisions on medical equipment management based upon a set of different criteria. In fact, medical equipment acquisition, preventive maintenance, and replacement are considered the most important phases, accordingly a properly planned management for these issues is considered a key decision of medical equipment management. In this thesis, a set of frameworks were developed regarding acquisition, preventive maintenance, and replacement to improve management process of medical equipment. In practice, quality function deployment was proposed as a core method around which the frameworks were developed

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities