Abstract
Purpose This paper aims to provide a service-based integrated prototype framework for the design of reusable modular assembly systems (RMAS) incorporating reusability of equipment into the process. It extends AutomationML (AML) developments for an engineering data exchange to integrate and standardize the data formats that support the design of RMAS. Design/methodology/approach The approach provides a set of systematic procedures and support tools for the design of RMAS. This includes enhanced domain knowledge models that facilitate the interpretation and integration of information across the design phases. Findings The inclusion of reusability aspects in the design phase improves the sustainability of future assembly systems, by ensuring equipment use until its end-of-life. Moreover, the integrated support tools reduce the design time, while improving the quality/performance of the system design solution, as it enables the exploration of a larger solution space. This will result in a better response to dynamic and rapidly changing system requirements. Social implications - This work provides a sustainable approach for the design of modular assembly systems (MAS), which will ensure better resource utilization. Additionally, the standardization of the data and the support of low cost tools is expected to benefit industrial companies, particularly the small- and medium-sized enterprises. Originality/value This approach offers a service-based platform which uses production data to incorporate reusability aspects into the design process of modular assembly system. Moreover, it provides a framework for modular assembly system design by extending the current design processes and interactions between stakeholders. To support this, a standardized method for information representation and exchange across the several phases of the RMAS design activity is briefly illustrated with an industrial case study.

Abstract
By carefully recommending selected items to users, recommender systems ought to increase profit from product sales. To achieve this, recommendations need to be relevant, novel and diverse. Many approaches to this problem exist, each with its own advantages and shortcomings. This paper proposes a novel way to combine model, memory and content-based approaches in a cascade-hybrid system, where each approach refines the previous one, sequentially. It is also proposed a straight-forward way to easily incorporate time-awareness into rating matrices. This approach focuses on being intuitive, flexible, robust, auditable and avoid heavy performance costs, as opposed to black-box fashion approaches. Evaluation metrics such as Novelty Score are also for-malized and computed, in conjunction with Catalog Coverage and mean recommendation price to better capture the recommender's performance.

Abstract
Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.

Abstract
Current technological and market challenges increase the need for development of intelligent systems to support decision making, allowing managers to concentrate on high-level tasks while improving decision response and effectiveness. A Racing based learning module is proposed to increase the effectiveness and efficiency of a Multi-Agent System used to model the decision-making process on scheduling problems. A computational study is put forward showing that the proposed Racing learning module is an important enhancement to the developed Multi-Agent Scheduling System since it can provide more effective and efficient recommendations in most cases.

Abstract
Real world optimization problems like Scheduling are generally complex, large scaled, and constrained in nature. Thereby, classical operational research methods are often inadequate to efficiently solve them. Metaheuristics (MH) are used to obtain near-optimal solutions in an efficient way, but have different numerical and/or categorical parameters which make the tuning process a very time-consuming and tedious task. Learning methods can be used to aid with the parameter tuning process. Racing techniques have been used to evaluate, in a refined and efficient way, a set of candidates and discard those that appear to be less promising during the evaluation process. Case-based Reasoning (CBR) aims to solve new problems by using information about solutions to previous similar problems. A novel Racing+CBR approach is proposed and brings together the better of the two techniques. A computational study for the resolution of the scheduling problem is presented, concluding about the effectiveness of the proposed approach.

Abstract
This paper presents the specification of an architecture for self-organizing scheduling systems. The proposed architecture uses learning by observing the experts and interpretation of scheduling experience. The design of intelligent systems that learn with experts is a very hard and challenging domain because current systems are becoming more and more complex and subject to rapid changes. In this work, different areas as Intelligent and Adaptive Human-Machine Interfaces, Metacognition and Learning from Observation, Self-managed Systems, amongst others, are joint together resulting in a global fully integrated architecture for self-organizing scheduling systems.