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Maritime transportation plays a crucial role in the international economy. Port container terminals around the world compete to attract more traffic and are forced to offer better quality of service. This entails reducing operating costs and vessel service times. In doing so, one of the most important problems they face is the Berth Allocation and quay Crane Assignment Problem (BACAP). This problem consists of assigning a number of cranes and a berthing time and position to each calling vessel, aiming to minimize the total cost. An extension of this problem, known as the BACAP Specific (BACASP), also involves determining which specific cranes are to serve each vessel. In this paper, we address the variant of both BACAP and BACASP consisting of a continuous quay, with dynamic arrivals and time-invariant crane-to-vessel assignments. We propose a metaheuristic approach based on a Biased Random-key Genetic Algorithm with memetic characteristics and several Local Search procedures. The performance of this method, in terms of both time and quality of the solutions obtained, was tested in several computational experiments. The results show that our approach is able to find optimal solutions for some instances of up to 40 vessels and good solutions for instances of up to 100 vessels. [ABSTRACT FROM AUTHOR]

Efficient routing and scheduling are critical for providing patients with quality health care in their homes, and palliative care involves specific requirements. This paper addresses the Palliative Home Health Care Routing and Scheduling Problem (PHHCRSP) through an integrated mathematical model. Key considerations include caregiver skills, care continuity, and a multi-period planning horizon. Special attention is also given to the case of clustered patients. Five formulations with different objective functions are analyzed and solutions reveal that optimizing only for minimum or maximum workload leads to non-efficient routing. The formulation that balances the total workload and the minimum workload emerges as the most effective, as it manages to obtain good routes while distributing the workload evenly among the caregivers. The results of the computational study on a set of well-known benchmark instances show that it is possible to obtain good patient care while keeping good working conditions for the caregivers. • We address the Palliative Home Health Care Routing and Scheduling Problem (PHHCRSP). • We develop and analyze five formulations with alternative objective functions. • We present valid constraints that improve the problem formulations. • The most effective formulation balances both the total and the minimum workload. • An extensive computational study is conducted on well-known benchmark instances. [ABSTRACT FROM AUTHOR]

When planning maintenance tasks involving the switching off of parts of the network, electricity transmission operators must evaluate and take into account the associated risks. The French operator, RTE, has developed a three-step approach in which they first calculate the risks in every possible scenario, then plan the maintenance tasks by optimizing some risk measures, and finally validate the plan. In this paper, we have addressed the second step, developing mathematical models that approximate the original problem as well as a matheuristic algorithm. It is composed of three phases, combining integer linear models in the first phase with metaheuristics such as Variable Neighbourhood Descent and Path Relinking in the second and third phases. The computational results obtained on the benchmarks provided by the company show that our algorithm improves on the best reported solutions for most of the test instances. • We have developed a matheuristic algorithm for a real maintenance planning problem. • The algorithm combines integer linear models with GRASP, VND, and Path Relinking. • The solutions are refined in a final intensification phase. • Our algorithm improves on the best reported solutions for most of the instances. [ABSTRACT FROM AUTHOR]

This paper extends the study of the continuous Berth Allocation Problem to the case of multiple quays, which is found in many container terminals around the world. Considering multiple quays adds a problem of assigning vessels to quays to the problem of determining berthing times and positions for each incoming vessel. This problem has not been considered in the literature at the operational level addressed in this study. In order to solve it, we have developed first an integer linear model and then a genetic algorithm which works on sequences of vessels that are decoded by a constructive algorithm. A local search procedure helps to improve further the solutions produced by the genetic algorithm. We have conducted an extensive computational experiment, adapting existing instances to the case of multiple quays. We have also adapted our genetic algorithm to the case of a single quay and tested it on previously reported instances, showing its superior performance compared with existing approaches. Finally, we have developed a random instance generator for the problem with multiple quays, enabling us to conduct a systematic study of the factors affecting the complexity of the problem. [ABSTRACT FROM AUTHOR]

Today's automotive factories are essentially assembly plants that receive parts from a vast network of suppliers around the world. Transporting thousands of part types over very long distances is a major logistic problem whose solution is a critical factor in the factory management. In this study we have developed a statistical and optimization methodology implemented in a software tool to help the decision makers select the most appropriate container for each part. A key element is to determine the number of parts that fit in a given container. Two optimization procedures have been developed, depending on the type of part, and used to calculate costs of each container. These costs include not only transporting parts from supplier to factory, but also the costs of handling parts within the factory and returning the empty containers to the supplier. • Selection of optimal container for parts transport from suppliers to assembly plant. • A new method estimates the number of parts in a container when loaded in bulk. • An algorithm optimizes parts in a container via strategic placement, not bulk loading. • Software integrates databases, geometry, optimization, learning and economic analysis. • The software, validated in the real world, is used by a major vehicle manufacturer. [ABSTRACT FROM AUTHOR]