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Brazil has been increasing its participation in the international trade market, mainly due to agricultural and forestry products, as in the case of soybeans and cellulose. This growth led to the expansion of the logistics infrastructure and its use. An important example of this trend is the port of São Luís, MA, in northern Brazil, which saw an increase in exports via rail (more than 200% growth in 6 years) and, consequently, an increase in the circulation of trains within its port complex.

This work proposes a mixed-integer linear programming model for the daily train scheduling problem at this port. All trains operated by VLI, a logistics company, are scheduled to minimize the departure times in order to improve the dwell time of freight train cars.

The railroad system in this Brazilian port consists of two classification yards, five terminals and a double-track railway for circulation. Different products such as grains, minerals, cellulose, and fuels are transported. The model also incorporates different operations at terminals and occupation restrictions due to maintenance and the physical flow of other third-party logistics companies. These features are modeled through a preprocessing step. In this phase, a series of auxiliary sets are defined to simplify constraints, circulation options are mapped, and the double-track is divided into segments based on the transit time with the objective to control track occupation.

This preprocess step also reduces the model complexity and, consequently, the computational time to solve it, as shown in the numerical tests using real-world operational data.

The main gains of the project were a reference train timetable for peak days, standardization of train crossing options, and a support tool for traffic adjustments with other rail operators.

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and consequences that are difficult to deal with. This chapter presents a review of selected multiple criteria problems used in supply chain optimization. Research analyzed the multiple criteria decision-making methods to tackle the problem of supplier evaluation and selection. It also focuses on the problem of supply chain when a disruption happens and presents strategies to deal with the issue of disruptions in supply chain and how to mitigate the impact of disruptions. Prevention, response, protection, and recovery strategies are explained. Practical part is focused in the risk-averse models to minimize expected worst-case scenario by single sourcing. Computational experiments for practical examples have been solved using CPLEX solver.