In this paper, we review the main concepts and methods to perform capacity analyses, and we present an automated tool that is able to perform several capacity analyses. Capacity is extremely dependent on infrastructure, traffic, and operating parameters. Therefore, an in-depth study of the main factors that influence railway capacity is performed on several Spanish railway infrastructures. The results show how the capacity varies according to factors such as train speed, commercial stops, train heterogeneity, distance between railway signals, and timetable robustness.

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Manufacturing industries are faced with environmental challenges so their industrial processes must be optimized in terms of both profitability and sustainability. Most of these processes are dynamic, so the previously obtained solutions cannot be valid after incidences or disruptions. This paper is focused on recovery in dynamic job-shop scheduling problems where machines can work at different rates. Machine speed scaling is an alternative framework to the on/off con-trol framework for production scheduling. Thus, given a disruption, the main goal is to recover the original solution by rescheduling the minimum number of tasks. To this end, a new match-up technique is developed to determine the rescheduling zone and a feasible reschedule. Then, a memetic algorithm is pro-posed for finding a schedule that minimize the energy consumption within the rescheduling zone but maintaining the makespan constraint. An extensive study is carried out to analyze the behavior of our algorithms to recover the original solution and minimize the energy reduction in different benchmarks, taken from the OR-Library. The energy consumption and processing time of the involved tasks in the rescheduling zone will play an important role to determine the best match-up point and the optimized rescheduling. Upon a disruption, different rescheduling solutions can be obtained, all of them holding with the require-ments, but with different values of energy consumption. The results proposed in this paper may be useful to be applied in real industries for energy-efficient production rescheduling.

This work deals with the multi-mode resource-constrained project scheduling problem. In this problem, activities of the project may be executed in more than one operating mode and renewable resource constraints exist. Each activity operation mode has a different duration and requires different amount of renewable resources. The objective function considered is the minimisation of the project completion time. Heuristics based on priority rule are considered as solution procedures for this problem. Both, parallel and serial scheduling generation schemes are described in the multi-mode context. On the basis of a well-known set of project instances, the new heuristics based on priority rules are evaluated against the best ones published. Finally, multi-pass heuristics based on priority rules that outperforms the deterministic multi-pass heuristic previously published are designed.

Rodríguez Molins, M.; Ingolotti Hetter, LP.; Barber Sanchís, F.; Salido Gregorio, MA.; R. Sierra, M.; Puente, J. (2014). A genetic algorithm for robust berth allocation and quay crane assignment. Progress in Artificial Intelligence. 2(4): 177-192. doi:10.1007/s13748-014-0056-3. A Genetic Algorithm for Robust Berth Allocation and Quay Crane AssignmentAbstract Scheduling problems usually obtain the optimal solutions assuming that the environment is deterministic. However, actually the environment is dynamic and uncertain. Thus, the initial data could change and the initial schedule obtained might be unfeasible. To overcome this issue, a proactive approach is presented for scheduling problems without any previous knowledge about the incidences that can occur. In this paper, we consider the Berth Allocation Problem and the Quay Crane Assignment Problem as a representative example of scheduling problems where a typical objective is to minimize the service time. The robustness is introduced within this problem by means of buffer times that should be maximized in order to absorb possible incidences or breakdowns. Therefore, this problem becomes a multi-objective optimization problem with two opposite objectives: minimizing the total service time and maximizing the robustness or buffer times.