arXiv subjects

A. R. M. Wolfert

Publications and source records attributed to A. R. M. Wolfert.

4 recordsLinked to original sources

arXiv2026

Preference-Based Optimisation for Integrated Design and Group Decision-Making

Conventional multi-objective optimisation approaches (e.g., MOO-CP or MIP) fail in group decision-making by aggregating heterogeneous objectives without a valid preference foundation, producing Pareto sets instead of a unique actionable decision. As only humans define objectives, preferences constitute the legitimate basis for decision-making. Accordingly, four conditions for complex design-decision systems are established: (1) Preference-Key - all objectives, constraints, and trade-offs are evaluated within a unified preference domain using valid preference function modelling (PFM); (2) Integration - feasible system performance (object capability) and acceptable actor preferences (subject desirability) coexist within a single design-decision space; (3) Association - actors freely specify individual preferences and weights, enabling consistent aggregation towards group-optimal decision-making; and (4) Uniqueness - the solver identifies a single best-fit solution with maximum aggregated preference. The ODESYS methodology, employing the IMAP solver, enables integrated multi-objective design optimisation and multi-criteria decision-making. Its extension within the ODESYS/FIVES formulation broadens applicability while achieving elegant simplicity, explicitly operationalising affine preference aggregation and preserving equivalence with validated ODESYS 1.0 results. By mapping system behaviour into a unified preference-performance domain, ODESYS/FIVES delivers a single best-fit solution, even for highly constrained problems, guaranteeing feasible and acceptable outcomes. Two applications demonstrate transformation of multi-objective optimisation into pure group decision-making, achieving a best-fit-for-common-purpose within socio-physical reach.

math.OC↗

arXiv2025

Advancing Strategic Planning and Dynamic Control of Complex Projects

Strategic project planning and dynamic control are essential to ensure that complex projects are both prepared and executed best-fit-for-common-purpose, guided by three interrelated strategies: (1) Agreeing First, (2) Acting Feasibly, and (3) Adapting Flexibly. When these strategies become too complex for humans to fully conceive and manage, effective computer-aided decision support becomes crucial. However, standard simulation-driven evaluation and a-posteriori decision-making are typically single-sided and technically focused focus, rather than applying a combined simulation-and-optimisation approach that a-priori integrates stakeholder interests and their mitigation behavior. Moreover, current planning and control methodologies often lack robust stochastic representations and associative multi objective optimisation methods that capture the full socio-technical complexity while maximizing the potential within reach. This paper introduces Odycon (Open Design and Dynamic Control), a new purpose-driven project management methodology that provides an actionable solution to these challenges. It presents a generic mathematical framework for project planning and control that integrates stakeholder preferences (human domain) with system performances (physical domain), enabling more effective planning and dynamic control. Odycon integrates standard Monte Carlo simulation (MCS) with the novel Integrative Maximisation of Aggregated Preferences (IMAP) optimisation method to develop a best-fit strategic plan and the most effective mitigation control strategies. Its value is demonstrated through applications in offshore wind installation and highway infrastructure projects, showcasing advances in associative design and decision-making, and aiming for a best-fit-for-common-purpose synthesis across different complex project phases.

math.OC↗

arXiv2024

Confronting Conflicts to Yes: Untangling Wicked Problems with Open Design Systems

Current project development practices often fail to engage stakeholders early and effectively. Decision support is often non-inclusive, single-sided, and lacking in transparency, while complexity goes beyond human's comprehension. Additionally, many approaches focus primarily on technical system aspects, neglecting the integration of stakeholders' individual preferences. This often results in project impasses, leaving stakeholders unable to collaboratively achieve a "yes." There is a need for a purely associative, a-priori design approach that integrates system realities and stakeholder ideals within a joint socio-technical solution space. The state-of-the-art Preferendus, embedded in the proven Open Design Systems (Odesys) methodology, is a neutral tool for transforming complexity into success. Aiming for synthesis, Odesys' robust IMAP optimization method generates a single best-fit design solution. Here, Odesys is applied for a Dutch wind farm stalemate development, balancing multiple stakeholder preferences, wind farm performances, and project constraints. The success of this approach hinges on stakeholder trust and input. This article introduces a structured stakeholder assessment method using choice-based conjunctive analysis (CBCA), facilitating transparent determination of global and local stakeholder weights and preference functions. Modelling 'disputable' exogenous factors as endogenous design parameters, the application demonstrates how one can shift toward a collaborative "yes." For this, it is concluded that a zoomed-out solution space would enable the energy transition to be tackled with multiple options rather than a prescribed one. The Odesys approach fosters decision-making that aligns with the social threefold principles of freedom, equality, and fraternity, guiding projects toward genuine democratic outcomes rather than selecting from curated options.

cs.CY↗

arXiv2022

Multi-criteria optimization and automated network restructuring to mitigate construction projects delays on-the-run

Construction project management requires dynamic mitigation control ensuring the project's timely completion by a best fit for common purpose strategy for all stakeholders. Current mitigation approaches are usually performed by an iterative Monte Carlo (MC) analysis focussing on lowest-cost strategies which do not include (1) the project manager's goal-oriented behaviour, (2) automated network restructuring potential, and (3) multi-dimensional optimization criteria for best fitting mitigation strategies-criteria. Therefore, the development statement within this paper is to design a method and implementation tool that properly dissolves all the aforementioned shortcomings ensuring the project's completion date by finding the most effective and efficient mitigation strategy. To fulfill the purpose of this paper, the Mitigation Controller (MitC) has been developed using an integrative approach of non-linear optimization techniques, probabilistic Monte Carlo simulation, and preference function modeling. Compared to the conventional way of mitigating project delays. The developed MitC allows mitigating potential delays with the least negative consequences on several project criteria, such as cost, environmental impact, etc. The application of the model to the demonstrative case study shows the ability of the model to significantly increase the probability of completing the project in the given target duration. Embedding the multi-criteria evaluation in the optimization model ensures that other interests are also represented in finding the optimal strategy for project delays.

nlin.AO↗