Determining departure times in dynamic and stochastic maritime routing and scheduling problem

In maritime transportation, decisions are made in a dynamic setting where many aspects of the future are uncertain. However, most academic literature on maritime transportation considers static and deterministic routing and scheduling problems. This work addresses a gap in the literature on dynamic and stochastic maritime routing and scheduling problems, by focusing on the scheduling of departure times. Five simple strategies for setting departure times are considered, as well as a more advanced strategy which involves solving a mixed integer mathematical programming problem. The latter strategy is significantly better than the other methods, while adding only a small computational effort.

Improved solutions to dynamic and stochastic maritime pick-up and delivery problems using local search.

Heuristics for stochastic and dynamic vehicle routing problems are often kept relatively simple, in part due to the high computational burden resulting from having to consider stochastic information in some form. In this work, three existing heuristics are extended by three different local search variations: a first improvement descent using stochastic information, a tabu search using stochastic information when updating the incumbent solution, and a tabu search using stochastic information when selecting moves based on a list of moves determined through a proxy evaluation. In particular, the three local search variations are designed to utilize stochastic information in the form of sampled scenarios. The results indicate that adding local search using stochastic information to the existing heuristics can further reduce operating costs for shipping companies by 0.5–2 %. While the existing heuristics could produce structurally different solutions even when using similar stochastic information in the search, the appended local search methods seem able to make the final solutions more similar in structure.

Dynamics and diversity of Escherichia coli in animals and system management of the manure on a commercial farrow-to-finish pig farm

The objective of this study was to determine the dynamics and diversity of Escherichia coli populations in animal and environmental lines of a commercial farrow-to-finish pig farm in Spain along a full production cycle (July 2008 to July 2009), with special attention to antimicrobial resistance and the presence of integrons. In the animal line, a total of 256 isolates were collected from pregnant sows (10 samples and 20 isolates), 1-week-old piglets (20 samples and 40 isolates), unweaned piglets (20 samples and 38 isolates), growers (20 samples and 40 isolates), and the finishers' floor pen (6 samples and 118 isolates); from the underfloor pits and farm slurry tank environmental lines, 100 and 119 isolates, respectively, were collected. Our results showed that E. coli populations in the pig fecal microbiota and in the farm environment are highly dynamic and show high levels of diversity. These issues have been proven through DNA-based typing data (repetitive extragenic palindromic PCR [REP-PCR]) and phenotypic typing data (antimicrobial resistance profile comprising 19 antimicrobials). Clustering of the sampling groups based on their REP-PCR typing results showed that the spatial features (the line) had a stronger weight than the temporal features (sampling week) for the clustering of E. coli populations; this weight was less significant when clustering was performed based on resistotypes. Among animals, finishers harbored an E. coli population different from those of the remaining animal populations studied, considering REP-PCR fingerprints and resistotypes. This population, the most important from a public health perspective, demonstrated the lowest levels of antimicrobial resistance and integron presence.