Fábio Silva Moreira

Abstract
In this paper, a mathematical model is developed to tackle a Consistent Vehicle Routing Problem, which considers customers with multiple daily deliveries and different service level agreements such as time windows, and release dates. In order to solve this problem, an instance size reduction algorithm and a mathematical programming based decomposition approach are developed. This solution approach is benchmarked against a commercial solver. Results indicate that the method solves instances of large size, enabling its application to real-life scenarios. A case study in a pharmaceutical distribution company is analyzed. Consistent routes are planned for several warehouses, comprising hundreds of orders. A simulation model evaluates the performance of the generated route plans. Significant improvements in terms of the total distance traveled and the total travel times are obtained when compared to the company's current planning process. © 2018 Elsevier B.V.

Abstract

Abstract

Abstract
This research aims at tackling a real-world long-haul freight transportation problem where tractors are allowed to exchange semi-trailers through several transshipment points until a request reaches its destiny. The unique characteristics of the considered logistics network allow for providing long-haul services by means of short-haul jobs, drastically reducing empty truck journeys. A greater flexibility is achieved with faster responses. Furthermore, the planning goals as well as the nature of the considered trips led to the definition of a new problem, the long-haul freight transportation problem with multiple transshipment locations. A novel mathematical formulation is developed to ensure resource synchronization while including realistic features, which are commonly found separately in the literature. Considering the complexity and dimension of this routing and scheduling problem, a mathematical programming heuristic (matheuristic) is developed with the objective of obtaining good quality solutions in a reasonable amount of time, considering the logistics business context. We provide a comparison between the results obtained for 79 real-world instances. The developed solution method is now the basis of a decision support system of a Portuguese logistics operator (LO).

Abstract
Production planning and scheduling in the process industry in general and in the pulp and paper (P&P) sector in particular can be very challenging. Most practitioners, however, address those activities relying only on spreadsheets, which is time-consuming and sub-optimal. The literature has reported some decision support systems (DSSs) that are far from the state-of-the-art with regard to optimization models and methods, and several research works that do not address industrial issues. We contribute to reduce that gap by developing and describing a DSS that resulted from several iterations with a P&P company and from a thorough review of the literature on process systems engineering. The DSS incorporates relevant industrial features (which motivated the development of a specific model), exhibits important technical details (such as the connection to existing systems and user-friendly interfaces) and shows how optimization can be integrated in real world applications, enhanced by key pre- and post-optimization procedures.