Abstract
This work considers sustainable scheduling of manufacturing operations and preventive maintenance activities in a single-machine environment where the machine works continuously in three eight-hour shifts per day. The jobs can be produced at different processing speeds, which reduces energy consumption and/or processing times. In a tri-objective mixed integer linear programming model, sustainability is attained through minimizing total weighted earliness/ tardiness - economic pillar, total energy consumption - environmental pillar, and number of undesired activities - social pillar. Moreover, a multi-objective genetic algorithm finds near optimal solutions in a timely manner. Numerical results will be presented at the conference.

Abstract

Abstract
Fleet tracking technology collects real-time information about geolocation of vehicles as well as driving-related data. This information is typically used for location monitoring as well as for analysis of routes, vehicles and drivers. From an operational point of view, the geolocation simply identifies the state of a vehicle in terms of positioning and navigation. From a management point of view, the geolocation may be used to infer the state of a vehicle in terms of process (e.g., driving, fueling, maintenance, or lunch break). Meaningful information may be extracted from these inferred states using process mining. An innovative methodology for inferring process states from geolocation data is proposed in this paper. Also, it is presented the potential of applying process mining techniques on geolocation data for process discovery. © 2020 The Authors. Published by Elsevier B.V.

Abstract
This work apply a deep learning artificial neural network model-the Multilayer Perceptron- A s a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: Individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays). © 2020 Tânia Fontes et al., published by Sciendo.

Abstract
Accessibility is one of the key measures of urban transportation planning, which quantify how easy is the access to a facility. Public transport accessibility concerns of the access level of geographical locations to public transport. In this paper, accessibility is used as an indicator to estimate social exclusion based on the maximum distance that someone has to walk to reach the public transport. The concept of the 6-minute walking distance (6MWD) is applied to measure accurately the walking ability for different groups of the population. A real life case study is conducted to get insight into the transportation network of the Porto Metropolitan Area, Portugal. For this purpose, geographic, demographic and infrastructure data were collected and integrated. Also, webservices are used to measure walking distances between locations. The results of this study allowed to characterize regions by different levels of accessibility, providing insight into the social exclusion in public transport. This assessment is used not only to identify inequities but also to get an overview of the service quality of public transport. © 2020 The Authors. Published by ELSEVIER B.V.

Abstract
Firms recognize that an assertive SCM could lead to an important competitive advantage in the business world. The purpose of this study is to capture the effect of supply chain management practices in the performance of firms, through a meta-analysis. It aims at highlighting which SCM practices that have a superior effect and their positive or negative impact on performance. Partnership with suppliers, process driven events, employee involvement, and customer satisfaction are the SCM practices that proved to have a positive impact in firm performance, according to the meta-analysis results. The findings from the research can help managers deciding on in which SCM practices concentrate their effort. It also allows making comparisons among different regions in terms of practices with a positive effect and how SCM practices evolves since first insights to now and if it changes throughout time. Concerning supply chain theories, this research sustains the hypotheses that SCM practices impact on some firm performance measures.