Abstract
Build to Order or Make to Order is a common approach for highly configured products such as special vehicles (vehicles that are adapted and altered to suit a specific purpose). Examples of such vehicles are special ambulances as well as vehicles adapted for the support and transport of passengers with less mobility. In this type of business, operations are scheduled in response to a confirmed order received from a final customer. Thus, the variability and the uncertainty characterizing what is project based, generate a complexity that requires specifically tailored managerial approaches to handle all the involved processes - from design and engineering to production and delivery. Hence, in this accentuated complexity, it is extremely important to guarantee that both the material and information flows are efficient and effective. The present study, framed in a program of operational improvement in a manufacturer of special vehicles, aims to address some concrete improvement opportunities related to the significant number of raw materials stockouts and to the high number of changes made by the client after production has started. In fact, during the manufacturing and assembly process, there are constant changes that delay and difficult planning and consequently decreases the overall efficiency and effectiveness. Strategies to address all these matters are to be identified and applied. © IEOM Society International.

Abstract
Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

Abstract
This paper describes a solution method that was created with the objective of obtaining a more efficient finished goods distribution process for a food industry company. The finished goods distribution process involves the use of the companys own fleet to serve a specific group of customers, and the use of outsourcing transportation services that can make direct and transshipment customer deliveries. The complexity of the problem is due to the need to decide which customers should be served by each of the outsourcing transportation services, direct or transshipment, and to find cost efficient solutions for the multiple vehicle routing problems created. First, an original clustering method consisting of a logical division of the customer orders using a delivery ratio based on the transportation unit cost, distance and order weight, is used to define customer clusters by service type. Then, an exact method based on a mixed integer programming model, is used to obtain optimal vehicle routing solutions, for each cluster created. The solution method for the company real instances, proved able to reach the initial proposed objectives and obtain promising results that suggest an average reduction of 34% for the operational costs, when compared to the current distribution model of the company. © 2019, Springer Nature Switzerland AG.

Abstract
In an increasingly and globally competitive industry like the automotive sector, the continuous improvement of processes assumes a key role in the enhancement of effectiveness and efficiency. In line with this philosophy, the study undertaken of the work method and its inherent activities has pointed to time measurement as a gemba support tool of great potential in the optimization of the production process and the elimination of mudas. The present case study relates to the improvement of productivity of an assembly line dedicated to the manufacture of brake cables for the automotive industry. The application of the A3 methodology to this improvement project aims to monitor the evolution of key indicators as productivity per hour and the Overall Equipment Effectiveness (OEE) of the assembly line, to define the different improvement actions to be executed, and to achieve the defined target, and to validate the results of its implementation. The measurement of the different activities of the assembly line allowed to identify the major wastes (of material movements, operator movements, among others.) and to identify the most critical workstations that contribute for the unbalancing of the assembly line. The solutions implemented allowed to increase the productivity by 49% and, as a consequence, reduce the cycle time in 33%. The rearrangement and improvement of operations allowed also to increase the efficiency of the assembly line balancing in 11%. As the A3 methodology assumed a key role for this project, allowing the monitoring of the effectiveness of the different improvement actions implemented, it was standardized so that it could be applied to other improvement projects.

Abstract
In many companies in the automotive industry there are challenges in some key processes in their logistic departments, mainly in internal logistics. These challenges happen due to poorly defined rules for the transportation of goods, resulting in a great cost associated with the time lost in the process. Also, the optimization of these processes, incrementing the efficiency of internal logistics can bring competitive advantages to the companies. For that matter, this study was developed at a major tire manufacturing company and proposes a model for the optimization of in-bound logistics, viewed as an online vehicle routing problem with soft deadlines (OVRPSD), using multiple depots. The main goal of this study is the increase of efficiency in logistic, optimizing the number of vehicles to supply the machines in order to reduce the stopping time of machines due to the lack of tires to consume. © 2019, Springer International Publishing AG, part of Springer Nature.

Abstract
Production scheduling is a function that can contribute strongly to the competitive capacity of companies producing goods and services. Failure to stagger tasks properly causes enormous waste of time and resources, with a clear decrease in productivity and high monetary losses. The efficient use of internal resources in organizations becomes a competitive advantage and can thus dictate their survival and sustainability. In that sense, it becomes crucial to analyze and develop production scheduling models, which can be simplified as the function of affecting tasks to means of production over time. This report is part of a project to develop a dynamic scheduling tool for decision support in a single machine environment. The system created has the ability, after a first solution has been generated, to trigger a new solution as some tasks leave the system and new ones arrive, allowing the user, at each instant of time, to determine new scheduling solutions, in order to minimize a certain measure of performance. The proposed tool was validated in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.