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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 work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1 s. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances. [ABSTRACT FROM AUTHOR]

First-mile logistics tackles the movement of products from retailers to a warehouse or distribution centre. This first step towards the end customer has been pushed by large e-commerce platforms forming extensive networks of partners and is critical for fast deliveries. First-mile pickup requires efficient methods different from those developed for last-mile delivery, among other reasons due to the complexity of cargo features and volume — increasing the relevance of advanced packing methods. More importantly, the problem is essentially dynamic and the pickup process, in which the vehicle is initially empty, is much more flexible to react to disruptions arising when the vehicles are en route. We model the static first-mile pickup problem as a vehicle routing problem for a heterogeneous fleet, with time windows and three-dimensional packing constraints. Moreover, we propose an approach to tackle the dynamic problem, in which the routes can be modified to accommodate disruptions — new customers' demands and modified requests of known customers that are arriving while the initially established routes are being covered. We propose three reactive strategies for addressing the disruptions depending on the number of vehicles available, and study their results on a newly generated benchmark for dynamic problems. The results allow quantifying the impact of disruptions depending on the strategy used and can help the logistics companies to define their own strategy, considering the characteristics of their customers and products and the available fleet. • A tool to plan the first-mile collection of parcels by a courier is provided. • Packing and shipping the products are jointly optimized. • Uncertainty caused by disruptions and changes is tackled. • Algorithms for static and dynamic VRPTW with packing constraints are proposed. [ABSTRACT FROM AUTHOR]

The multiport container ship stowage problem consists in determining the position of the containers on board a ship along its route with the objective of minimizing the number of unproductive moves required in the loading and unloading operations at each port. This paper presents an integer programming model for the problem and proposes several sets of valid constraints that bring its LP-relaxation closer to an integer solution. Moreover, it presents a GRASP algorithm that generates stowage plans with a minimal number of unproductive moves in a high percentage of medium and large-size instances. An extended computational analysis has been performed in which, to the best of the authors' knowledge, the efficiency of integer programming models for the problem is tested for the first time. With respect to GRASP, the computational results show that it performs well on different sized datasets. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of a logistics company that has to serve its customers by first putting the products on pallets and then loading the pallets into trucks. Besides the standard geometric constraints of products not overlapping each other and not exceeding the dimensions of pallets and trucks, in this real problem, there are many other constraints, related to the total weight of the load, the maximum weight supported by each axle, and the distribution of the load inside the truck. Although the problem can be decomposed into two phases, pallet loading and truck loading, we have taken a combined approach, building and placing pallets at the same time. For each position in the truck, a pallet is built and tailored for that position according to the constraints of height and weight. We have developed a GRASP algorithm, in which the constructive algorithm is randomized and an improvement phase is added to obtain high-quality solutions. The algorithm has been tested on two sets of real instances with different characteristics, involving up to 44 trucks. The results show that solutions with an optimal or near optimal number of trucks are obtained in very short computing times. [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]

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]

In this study, we estimated the risk of acute coronary syndrome and stroke associated with several emerging cardiovascular risk factors. This was a case-control study, where an age - and sex-matched acute coronary syndrome group and stroke group were compared with controls. Demographic and clinical data were collected through patient interviews, and blood samples were taken for analysis. In the bivariate analysis, all cardiovascular risk factors analyzed showed as predictors of acute coronary syndrome and stroke, except total cholesterol and smoking. In the multivariate logistic regression model for acute coronary syndrome, hypertension and body mass index, N-terminal section brain natriuretic peptide and pregnancy-associated plasma protein-A were independent predictors. For stroke, the predictors were hypertension, diabetes mellitus, body mass index, and N-terminal section brain natriuretic peptide. Controlling for age, sex, and classical cardiovascular risk factors, N-terminal section brain natriuretic peptide and pregnancy-associated plasma protein-A were independent emerging cardiovascular risk factors for acute coronary syndrome, but pregnancy-associated plasma protein-A was not for stroke. High levels of cardiovascular risk factors in individuals with no episodes of cardiovascular disease requires the implementation of prevention programs, given that at least half of them are modifiable. [ABSTRACT FROM AUTHOR]