Abstract
Central areas of large cities offer in general many advantages to their inhabitants. Typically, a large number of products, services, and opportunities are available in those urban zones, thus increasing life quality. Unfortunately, these benefits are associated with increasing transportation activities that can cause serious problems, such as traffic congestion, excessive energy consumption, and pollution. This paper aims at presenting a new transport system that consists of transporting freight in long-haul passenger vehicles. Two mixed integer mathematical programming models are presented: one for total cost minimization and the other for the travel time minimization. The problem under study was considered as a multi-commodity network flow problem with time windows, multi transport-lines, and multiple vehicles. Three heuristics based on mixed integer programming (MIP) were designed to solve it: size reduction, LP-and-fix, and a combination of these two procedures. The proposed approaches were validated in a case study designed around the intercity passenger transport system, in Ceará, Northeast of Brazil. Several operational scenarios were evaluated, taking into account the available freight capacities. The developed MIP heuristics produced high-quality solutions, in reasonable computational times, with the LP-and-Fix algorithm outperforming the other approaches. © 2020 The Authors. Published by Elsevier B.V.

Abstract
The negative impacts of urban logistics have fostered the search for new distribution systems in inner city deliveries. In this context, interesting solutions can be developed around two-echelon distribution systems based on mobile depots (2E-MD), where loads arriving from the periphery of the city are directly transferred, at intermediate locations, from larger to smaller vehicles more suited to operate in the city centre. Four types of 2E-MD can be identified, according to the degree of mobility of larger vehicles and their accessibility to customers. In this paper, we propose a generic three-index arc-based mixed integer programming model, for a two-echelon vehicle routing problem, with synchronisation at the satellites and multi-trips at the second echelon. This generic base model is formulated for the most restrictive type of problems, where larger vehicles visit a a single transfer location and do not perform direct deliveries to customers, but it can be easily extended to address the other types of 2E-MD. The paper presents how these extensions account for the characteristics of the different types of 2E-MD. The generic model, its extensions and the impact of a set of valid inequalities are tested using problem instances adapted from the VRP literature. Results show that the proposed extensions do adequately address the specific features of the different types of 2E-MD, including multiple visits to satellites, and direct deliveries to customers. Nevertheless, the resulting models can only tackle rather small instances, even if the formulations can be strengthened by adding the valid inequalities proposed in the paper. © 2020 The Authors. Published by ELSEVIER B.V.

Abstract

Abstract
The potential of the Internet of Things (IoT) and other technologies in the realm of Industry 4.0 to generate valuable data for monitoring the performance of the production processes and the whole supply chain is well established. However, these large volumes of data can be used within planning and control systems (PCSs) to enhance real-time planning and decision-making. This paper conducts a literature review to envisage an overall system architecture that combines IoT and PCS for planning, monitoring and control of operations at the level of an industrial production process or at the level of its supply chain. Despite the extensive literature on IoT implementations, few studies explain the interactions between IoT and the components of PCS. It is expected that, with the increasing digitization of business processes, approaches with PCS and IoT become ubiquitous in the near future. © 2020, Springer Nature Switzerland AG.

Abstract
Sustainable development is a widely spread concept nowadays, especially due to external pressure related to environmental and social issues, affecting all players of the supply chain. Sustainable policies must be adopted, such as improving process performance and reducing waste. With sustainability as a driver of operational excellence, this study is focused on the improvement of the production process of a company by reducing variability. A variability analysis was done to understand its root causes and act upon them, as well as a quantification of waste in the process. Finally, an improvement plan was delineated to mitigate the problems identified. Copyright © 2020 Inderscience Enterprises Ltd.

Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.