An updated annotated bibliography on arc routing problems

The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Hazard alerting and situational awareness in advanced air transport cockpits

Advances in avionics and display technology have significantly changed the cockpit environment in current 'glass cockpit' aircraft. Recent developments in display technology, on-board processing, data storage, and datalinked communications are likely to further alter the environment in second and third generation 'glass cockpit' aircraft. The interaction of advanced cockpit technology with human cognitive performance has been a major area of activity within the MIT Aeronautical Systems Laboratory. This paper presents an overview of the MIT Advanced Cockpit Simulation Facility. Several recent research projects are briefly reviewed and the most important results are summarized

Image-Based Roadway Assessment Using Convolutional Neural Networks

Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling large-scale roadway study at a fraction of the cost of manual approaches

A matheuristic for the Distance-Constrained Close-Enough Arc Routing Problem

[EN] The Close-Enough Arc Routing Problem, also called Generalized Directed Rural Postman Problem, is an arc routing problem with interesting real-life applications, such as routing for meter reading. In this application, a vehicle with a receiver travels through a series of neighborhoods. If the vehicle gets within a certain distance of a meter, the receiver is able to record the gas, water, or electricity consumption. Therefore, the vehicle does not need to traverse every street, but only a few, in order to be close enough to each meter. In this paper we deal with an extension of this problem, the Distance-Constrained Generalized Directed Rural Postman Problem or Distance-Constrained Close Enough Arc Routing Problem, in which a fleet of vehicles is available. The vehicles have to leave from and return to a common vertex, the depot, and the length of their routes must not exceed a maximum distance (or time). For solving this problem we propose a matheuristic that incorporates an effective exact procedure to optimize the routes obtained. Extensive computational experiments have been performed on a set of benchmark instances and the results are compared with those obtained with an exact procedure proposed in the literature.This work was supported by the Spanish Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) through Project MTM2015-68097-P (MINECO/FEDER). Authors want to thank two anonymous referees for their suggestions and comments that have contributed to improve the paper.Corberán, A.; Plana, I.; Reula, M.; Sanchís Llopis, JM. (2019). A matheuristic for the Distance-Constrained Close-Enough Arc Routing Problem. Top. 27(2):312-326. https://doi.org/10.1007/s11750-019-00507-3S312326272Aráoz J, Fernández E, Franquesa C (2017) The generalized arc routing problem. TOP 25:497–525Ávila T, Corberán Á, Plana I, Sanchis JM (2016) A new branch-and-cut algorithm for the generalized directed rural postman problem. Transportation Science 50:750–761Ávila T, Corberán Á, Plana I, Sanchis JM (2017) Formulations and exact algorithms for the distance-constrained generalized directed rural postman problem. EURO Journal on Computational Optimization 5:339–365Cerrone C, Cerulli R, Golden B, Pentangelo R (2017) A flow formulation for the close-enough arc routing problem. In Sforza A. and Sterle C., editors, Optimization and Decision Science: Methodologies and Applications. ODS 2017., volume 217 of Springer Proceedings in Mathematics & Statistics, pages 539–546Corberán Á, Laporte G (editors) (2014) Arc Routing: Problems,Methods, and Applications. MOS-SIAM Series on Optimization,PhiladelphiaCorberán Á, Plana I, Sanchis J.M (2007) Arc routing problems: data instances. http://www.uv.es/~corberan/instancias.htmDrexl M (2007) On some generalized routing problems. PhD thesis, Rheinisch-Westfälische Technische Hochschule, Aachen UniversityDrexl M (2014) On the generalized directed rural postman problem. Journal of the Operational Research Society 65:1143–1154Gendreau M, Laporte G, Semet F (1997) The covering tour problem. Operations Research 45:568–576Hà M-H, Bostel N, Langevin A, Rousseau L-M (2014) Solving the close enough arc routing problem. Networks 63:107–118Mourão MC, Pinto LS (2017) An updated annotated bibliography on arc routing problems. Networks 70:144–194Renaud A, Absi N, Feillet D (2017) The stochastic close-enough arc routing problem. Networks 69:205–221Shuttleworth R, Golden BL, Smith S, Wasil EA (2008) Advances in meter reading: Heuristic solution of the close enough traveling salesman problem over a street network. In: Golden BL, Raghavan S, Wasil EA (eds) The Vehicle Routing Problem: Lastest Advances and New Challenges. Springer, pp 487–50