Product design has a major, often under-estimated impact on sustainability. This article describes the process of a new sustainable product design aiming for integrating circular engineering and social aspects as an integral part of the design process. A methodology is presented that enables a holistic, systematic approach to product development with the product design goal of a ‘Circular System’ with three subordinates but interdependent key elements: the ‘Product’, its ‘Life Cycle System’ and the related ‘Context System’. Social and ecological responsibility is integrated into product design from the beginning, ending up in a cyclic ‘Convergent Creation Model’. The associated methodology can be understood as a valuable opportunity to structure and control the complexity of sustainable product design for circularity. Two representative use cases - an established high-volume circular product (smart water metering unit) and a strategic design and R D perspective on “green” robots, contributing as sustainably engineered production systems to zero emission production – are discussed. Both demonstrators for the general applicability of the proposed ‘Circular System Design’ approach underline the potential of the methodology for advanced sustainable systems engineering in different phases of product design and circular production.
The work concerns the use of D-Wave’s quantum cloud service to solve the NP-hard flow shop scheduling problem with due dates and with the criterion of maximizing the weighted number of tasks performed on time. Constrained Quadratic Model, Binary Constrained Quadratic Model, and Binary Unconstrained Quadratic Model were proposed. Load experiments were carried out in a hybrid D-Wave LeapHybridCQMSampler environment using a combination of metaheuristics and quantum annealing, and DWaveSampler natively implementing quantum annealing. Calculations in the DWaveSampler environment are performed very quickly, but their practical application is currently limited due to the relatively small number of available qubits.
In this paper we tackle the dynamic stacking problem by introducing a framework for incremental online optimization. The dynamic stacking problem features continuous uncertain arrival and delivery of blocks via a crane controlled by the solver. The problem is implemented as a discrete event simulation and the solver runs asynchronously. We develop a framework that can use our existing offline solver for the dynamic stacking problem and turn it into an online solver capable of incrementally updating optimized plans. We test our framework by comparing to our previously published iterative approach as well as a rule based baseline solver on a diverse set of problem instances. Using the new framework, the solver improves our key performance indicators across the benchmark instances. We also investigate the reasons for the performance differences both in the aggregate as well as the level of individual simulation runs. The framework not only works well on this specific stacking problem, but is general enough to be used in many online dynamic optimization problems.
This paper aims to analyze and compare several technologies to measure their energy efficiency, with the goal of defining sustainable software design and architectural patterns. The study presents a methodology designed for assessing and optimizing energy consumption in IT systems and demonstrates how it contributes to sustainable development within the industry.
Trustworthiness in the design of AI software is crucial for acceptance and performance, particularly in complex socio-technical systems. The AI-DATA model, introduced by the funded 506 GEMINI project, addresses this need through a human-centered approach to Onsite Customer Journey Optimization in a case study of an online shop for nutritional supplements. This model combines systems thinking, process mining, and Large Language Models (LLMs) to enhance the Customer Journey in E-Commerce. By evaluating various prompt techniques, AI-generated marketing claims for onsite activation are tailored to a specific audience within specific phases of the AI-DATA model and utilized as triggers for interventions. This integrated methodology ensures optimization of the Onsite Customer Journey by providing relevant and trustworthy content at the right Moments of Truth in order to increase conversion rates, such as purchases, thereby fostering sustainably revenue growth.
