The purpose of this paper is to indicate how the association of deep learning with reinforcement learning, known as deep reinforcement learning (DRL), influences recent research on one of the most representative problems in the production planning and control (PPC) area, the job shop scheduling problem (JSSP), by identifying both the main approaches and tools applied in its modelling, implementation and validation, and the direction in which future research moves.

This article presents a conceptual framework that serves as a reference to propose a quantitative approach based on lean manufacturing (LM) techniques in an uncertainty context. To develop it, five input factors are defined with an influence in such a context: top management, supply chain, machines, processes, human resources. In addition, their interaction with LM tools and quantitative models for production planning under uncertainty is identified. It also determines the performance outputs obtained with the proper management of LM tools and quantitative models in each identified input factor. The objective of the application of such a conceptual framework is oriented towards improving an organisation’s performance from a LM perspective under uncertainty because it is an under researched topic that requires future research efforts, particularly in the graphic industry.

This paper proposes a methodology to support researchers, technology developers and industrial pilots, which are part of a European project consortium, on the use cases definition for the demonstration and validation of the project results. The proposed methodology analyses all the current business processes of each industrial pilot, to establish the starting point of the use cases project solutions. Thanks to this methodology, the detailed specification for the development of all composing elements of the use cases will be laid down, establishing the boundaries for the implementation and validation of the project results.

In recent years, the layout of industrial plants has been a much-discussed topic in the scientific literature. However, many of the optimisation models used to formulate the problem have been applied to hypothetical test instances by adopting simplifications of reality, which makes their application in the metal-mechanic industries difficult. In this context, the present article presents a set of five optimisation models that can constitute a reference framework for creating an optimisation model that applies to case studies in the metal-mechanic sector.

This article presents an initial overview of optimisation models applied to production scheduling and sequencing in the Industry 4.0 (I4.0) environment. It reviews algorithms based on operational research technologies and artificial intelligence within an I4.0 framework; i.e. models and algorithms that simultaneously incorporate physical and digital Internet solutions, communications and data processing, and intelligence and management. The main findings of this article show that, despite many articles introducing I4.0 concepts or applying some of their principles to production scheduling and sequencing, a real interaction between the real-time data of the physical system and its simulation using digital twins (DTs) is still lacking. Finally, it is important to highlight that one of the most popular scheduling and sequencing approaches is the job shop and most articles are about cross-sector problems.

The purpose of this paper is to present a conceptual framework to facilitate academics and practitioners’ decision making related to multi-objective facility layout planning (mFLP) by employing a bottom-up approach. Based on a literature survey framed in the mFLP context, this work identified and discussed a set of criteria that have become limitations of the traditional top-down approach. These criteria served as the basis to conceive the proposed conceptual framework. Our conceptual framework formalises FLP as a multi-objective problem by following the two traditional planning phases (block- and detailed phase) in reverse by a bottom-up approach, and by also integrating a third phase, called the refined phase, which has not previously been contemplated in the literature. Apart from identifying the inputs and outputs of each phase, the conceptual framework groups together several objectives related to mFLP that have been recently considered in the literature and formalises and contextualises them according to the planning phase in which they are involved. This is the first time that mFLP is addressed with a bottom-up approach.

Dynamic facility layout planning (DFLP) involves determining an appropriate arrangement scheme of the elements making up the production system for each time period into which the planning horizon is divided. When formulating the problem as an optimisation model, using the traditional top-down approach is usual, which firstly determines the block layout (BL) and then the detailed layout (DL) of each work cell. However by this approach, the BL generates area constraints in the detailed phase, which sometimes limit its implementation. In this context, the present paper presents a multi-objective mixed integer non-linear programming (MOMINLP) model that allows the problem to be addressed by considering an alternative approach, known in the literature as the bottom-up approach. The proposed model, called bottom-up mDFLP, considers three objective functions: (1) minimise the total material handling cost (TMHC) and the total rearrangement cost (TRAC); (2) maximise the total closeness rating (TCR) between departments; (3) maximise the area utilisation ratio (AUR). The original MOMINLP is transformed into a more computationally efficient multi-objective mixed integer linear programming (MOMILP) model. The proposed model is applied and validated in a case study of a company in the metal-mechanic sector with 12 departments for three 4-month periods.

The increasing use of wireless technologies in
Industry 4.0 is a reality and a current necessity. While the
foreseeable use of 5G networks will play an important role in this
field, the use of different communication networks in Industrial
Internet of Things (IIoT) networks is nowadays an active field of
research, since it is considered that the industry of the future will
be supported by a heterogeneous set of network technologies...

Recently more digital twins have been developed and used in the manufacturing industry. Some are based on detailed models of a particular machine/process oriented to monitoring, but others can represent a full manufacturing plant for production planning. These models involve a lot of parameters, and it is not easy to evaluate the effect of their change in the manufactured product or how they are correlated among them. This paper presents a prescriptive analysis tool that allows testing and ranking multiple scenarios using a previously generated digital twin. This is thus, a simulation, evaluation, and prescription tool. It uses a set of available models, allowing the selection of the parameters and defining the scenarios to be simulated, as well as the evaluation configuration. The tool performs exhaustive simulations making initially all possible combinations of the parameters selected and obtaining the simulation results for each of them. After this the evaluation is performed over some of the outputs of the model, leading to a ranking of all the simulations. The prescription obtained can be later used to configure a machine or to change some production parameters to optimize the system

This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the
automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and mediumsized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a
conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main
contributions of this article are to: (i) identify the functions making up traditional PPC and smart production
planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose
a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0
context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture
model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical
description, organisation and understanding of each aspect, which can provide a guide for academic research andindustrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications
and research gaps are provided.

This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the
automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and mediumsized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a
conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main
contributions of this article are to: (i) identify the functions making up traditional PPC and smart production
planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose
a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0
context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture
model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical
description, organisation and understanding of each aspect, which can provide a guide for academic research and
industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications
and research gaps are provided.

As the potential benefits of implem enting International Data Spaces (IDS) become more evident, several initiatives attempt to implement Logistics Data Spaces (LDS) and search alignment with federated IDS infrastructures. More specifically, the Topsector Logistics Action Agenda (2021-2023) proposed a research and development roadmap outlining the development of a sector-wide logistics data-sharing infrastructure, its essential building blocks, and nine illustrative use cases for practitioners. However, such an ambitious roadmap requires a significant change in the current system regime, raising new challenges throughout the four enterprise interoperability layers recognized by the European Interoperability Framework, i.e., technical, semantic, organizational, and legal. This discussion paper provides an overview of completed and current research endeavors to promote enterprise interoperability in the context of LDS and searches alignment with the IDS vision and reference architecture model. Therefore, we survey work in progress, preliminary results, lessons learned from research initiatives, policymaking, roadmap realization, and industry challenges in this area. The contribution of this discussion paper is threefold. First, it summaries the leading European developments and data-sharing initiatives. Second, it elaborates on the established roadmap, state-of-the-art LDS initiatives, and relevant achievements in the macro-context of IDS. Third, we report on some of the main lessons learned. The paper summarizes preliminary results and lists immediate research opportunities, industry challenges, recommendations for further roadmap realization, policymaking, and alignment with related international developments.