Abstract
This paper analyses the process of generalisation of an innovative government-led public practice in the healthcare sector. The scaling and embedding involved in this generalisation process are assumed to be dependent on the multiple implementation processes (consecutive or simultaneous) that lead to a routine use of the innovation in different adopters. This paper, therefore, proposes the use of a configurational theory approach to conceptualise each implementation of the innovation during the generalisation process and shed light on the generalisation's scaling and embedding efforts. It suggests a set of recommendations and practices for generalisation managers, most notably: i) they should regard generalisations as organic processes where their main role is to create space for experimentation, learning and negotiation, and ii) they should adopt different modes of governance to identify adequate mechanisms and strategies and guide their actions. This configurational perspective allows them to monitor and manage the evolution of implementations, informs the valuable learning processes that take place in a generalisation and has been found to be a useful tool to support the crucial collaboration among the actors involved in a generalisation.

Abstract
This paper present an instance transformation procedure to modify known instances of the resource -constrained project scheduling problem to make them easier to solve by heuristic and/or exact solution algorithms. The procedure makes use of a set of transformation rules that aim at reducing the feasible search space without excluding at least one possible optimal solution. The procedure will be applied to a set of 11,183 instances and it will be shown by a set of experiments that these transformations lead to 110 improved lower bounds, 16 new and better schedules (found by three meta -heuristic procedures and a set of branch -and -bound procedures) and even 64 new optimal solutions which were never not found before.

Abstract

Abstract
Digital transformation has been taking place for several decades in different sectors of activity and is contributing significantly to mitigating the environmental impacts of those sectors. Various digital solutions are related to energy consumption and production, which is crucial to ensure continuous decarbonisation. Most of them are targeted to be used by general consumers. Therefore, it is essential to consider consumers' attitudes towards those solutions and their adoption behaviour to ensure a broad diffusion of them. This study uses the Technology Acceptance Model to understand the adoption of energy-related digital solutions in Europe. We conclude that the perceived usefulness of the solutions is more relevant in attitude formation than the perceived ease of use. Moreover, attitude highly influences adoption behaviour, as reported in the literature. Finally, these relations seem to be highly influenced by the belief that, by adopting digital solutions, consumers contribute to a better balance between energy supply and demand.

Abstract

Abstract
Vertical pulp and paper production is challenging from a process point of view. Managers must deal with floating bottlenecks, intermediate storage levels, and by-product production to control the whole process while reducing unexpected downtimes. Thus, this paper aims to address the integrated lot sizing and scheduling problem considering continuous digester production, multiple paper machines, and a chemical recovery line to treat by-products. The aim is to minimize the total production cost to meet customer demands, considering all productive resources and encouraging steam production (which can be used in power generation). Production planning should define the sizes of production lots, the sequence of paper types produced in each machine, and the digester working speed throughout the planning horizon. Furthermore, it should indicate the rate of byproduct treatment at each stage of the recovery line and ensure the minimum and maximum storage limits. Due to the difficulty of exactly solving the mixed integer programming model representing this problem for realworld instances, mainly with planning horizons of over two weeks, constructive and improvement heuristics are proposed in this work. Different heuristic combinations are tested on hundreds of instances generated from data collected from the industry. Comparisons are made with a commercial Mixed-Integer and Linear Programming solver and a hybrid metaheuristic. The results show that combining the greedy constructive heuristic with the new variation of a fix-and-optimize improvement method delivers the best performance in both solution quality and computational time and effectively solves realistic size problems in practice. The proposed method achieved 69.41% of the best solutions for the generated set and 55.40% and 64.00% for the literature set for 1 and 2 machines, respectively, compared with the best solution method from the literature and a commercial solver.