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Resource-constrained assembly lines are widely found in industries that manufacture complex products. In such lines, tasks may require specific resources to be processed. Therefore, decisions on which tasks and resources will be assigned to each station must be made. When the number of available stations is fixed, the problem’s main goal becomes the minimisation of cycle time (type-II version). This paper aims to explore this variant of the problem that lacks investigation in the literature.

In this paper, the authors propose mixed-integer linear programming (MILP) models to minimise cycle time in resource-constrained assembly lines, given a limited number of stations and resources. Dedicated and alternative resource types for tasks are considered in different scenarios.

Besides, past modelling decisions and assumptions are questioned. The authors discuss how they were leading to suboptimal solutions and offer a rectification.

The proposed models and data set fulfil more practical concerns by taking into account characteristics found in real-world assembly lines.

The proposed MILP models are applied to an existing data set, results are compared against a constraint programming model, and new optimal solutions are obtained. Moreover, a data set extension is proposed due to the simplicity of the current one and instances up to 70 tasks are optimally solved.

The innovation expected by clients is identified as a business success factor of industrial companies in the current decade and the accountability of it is primarily attributable to Research and Development (R&D), which makes it a strategic topic for studies on the decision-making process. In light of this, the purpose of this paper is to present a decision aiding methodology used to support R&D management in the technology-based company, specialised in refrigeration solutions and world leader in the hermetic compressor market.

It is an exploratory study and has deductive-inductive logic and uses a quail-quantitative approach. It uses the Knowledge Development Process Constructivist (ProKnow-C) to systemically revise the literature surrounding the theme in order to identify research opportunities in the subject and adopts the Multi-Criteria Decision Aiding Constructivist (MCDA-C) methodology as an instrument of organisational performance evaluation to fulfil the research opportunities identified.

In the theoretical aspect, this research fulfilled the opportunities observed in recent and qualified literature about R&D performance evaluation. The paper also offers practical implications for the performance evaluation in R&D, since the methodology allowed the R&D manager to build knowledge to understand the consequences of his decisions in the criteria deemed important by himself.

The importance of this work covers academic and practical interests, as it documents the application of MCDA-C and increases knowledge concerning R&D management, whilst developing a recurrent tool of decision aiding in the context of the company studied.