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In this work, we study a variant of the team orienteering problem motivated by a real-world situation faced by a Mexican telecommunications company. The problem consists of the daily assignment and scheduling of service orders to crews, aiming to balance their wages, and considering the compatibility between service orders and crews. We present a mixed-integer linear formulation with two different metrics to achieve balanced scores and two valid inequalities leveraging the structure of the problem. Afterward, we propose a practical adaptive multi-start heuristic that integrates the learning mechanism of a reactive Greedy Randomized Adaptive Search Procedure. We test the performance of the models and the proposed algorithm on a benchmark of instances adapted from the literature and in a case study based on real data. The results confirm the effectiveness of the proposed algorithm to support the decision-making process. [ABSTRACT FROM AUTHOR]

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm. [ABSTRACT FROM AUTHOR]

This study addresses the set orienteering problem with mandatory visits (SOPMV), a variant of the team orienteering problem (SOP). In SOPMV, certain critical sets must be visited. The study began by formulating the mathematical model for SOPMV. To tackle the challenge of obtaining a feasible route within time constraints using the original MILP approach, a two-stage mixed-integer linear programming (MILP) model is proposed. Subsequently, a simulated annealing (SA) algorithm and a dynamic programming method were employed to identify the optimal route. The proposed SA algorithm was used to solve the SOP and was compared to other algorithms, demonstrating its effectiveness. The SA was then applied to solve the SOPMV problem. The results indicate that the solutions obtained using SA are superior and more efficient compared to those derived from the original MILP and the two-stage MILP. Additionally, the results reveal that the solution quality deteriorates as the ratio of the set of mandatory visits increases or the maximum allowable travel time decreases. This study represents the first attempt to integrate mandatory visits into SOP, thereby establishing a new research direction in this area. The potential impact of this research is significant, as it introduces new possibilities for addressing complex combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

The following paper adopts the term Future Orienteering to indicate the attitude of openness and preparedness to the future of individuals, communities and organisations. If Futures Studies wants to become an independent field of research, future skills need to be developed with appropriate training paths, and future attitudes and strategies need to be evaluated accurately. Thus, considering the necessity of a measurement system for the evaluation of strategic foresight and future-based activities, this article illustrates a new theoretical model, where special emphasis is placed on the distinction between Forecasting, Foresight and Anticipation, according to the Theory of Anticipation presented in (Poli, Introducing anticipation. In: Poli R (ed) Handbook of anticipation, 2019a); (Poli, Lavorare con Il futuro: idee e strumenti per governarel'incertezza, 2019b); (Poli, Introduction to anticipation studies, 2017). The result is a battery of three synthetic indexes, immediately readable to allow easy comparisons. Here, the following model described represents a preliminary stage in the development of an evaluation system, indeed few suggestions for use will be provided. [ABSTRACT FROM AUTHOR]

As a sport conducted in dynamically changing natural environments, orienteering places high demands on athletes' cognitive processing abilities and visual search efficiency. However, previous studies on orienteering have been primarily limited by the use of fixed stimulus materials on computer screens, which are unable to fully simulate authentic sports scenarios. To better understand the sports expertise of orienteering athletes in terms of their real scene image recognition performance and visual search characteristics, this study recruited 40 orienteering athletes, both experts and novices, as participants. By utilizing eye-tracking technology and setting observation points in real-world scenarios to conduct image recognition task tests, the ecological validity of the experiment was further enhanced. The results showed that the experts demonstrated a high level of accuracy and a short response time, with visual search characteristics including few saccade counts, low fixation frequency, concentrated fixation points, simple and clear fixation paths, and higher visual search efficiency. This study further reveals that long-term specialized training will lead to the formation of a unique cognitive structure related to the specific knowledge and long-term memory required by expert orienteering athletes, thereby promoting the development of expert advantage. [ABSTRACT FROM AUTHOR]

Ski-orienteering, which combines cross-country skiing with orienteering, dates back to the late 1800s, with the first World Championships in 1975. While researchers have explored the physiological and biomechanical determinants of success in cross-country skiing and orienteering separately in detail, scientific knowledge concerning ski-orienteering remains limited. Based on the information that is presently available together with interviews with elite ski-orienteers, we explore here for the first time the historical development, physiological, biomechanical, and psychological demands, certain training strategies, and future prospects and challenges associated with this sport, including its potential to become an Olympic event. A demanding endurance sport (with racing times of 12–120 min), ski-orienteering requires both considerable aerobic and anaerobic capacity, as well as well-trained upper and lower body muscles. In addition, ski-orienteering demands advanced skiing technique on various types of terrain, with frequent changes between sub-techniques, on both wide and narrow tracks and with numerous turns on downhill terrain. Moreover, success in this sport requires accurate and rapid orienteering—the ability to navigate a complex network of ski tracks with numerous intersections/crossings in a manner designed to pass the multiple control points in the order indicated on the map as rapidly as possible, i.e., advanced spatial cognition and highly developed navigational skills. Thus, ski-orienteering requires training designed to improve both relevant physiological characteristics and orienteering skills, which should become the focus of future interdisciplinary research on this complex sport. [ABSTRACT FROM AUTHOR]

The team orienteering problem (TOP) is an NP‐hard optimization problem with an increasing number of potential applications in smart cities, humanitarian logistics, wildfire surveillance, etc. In the TOP, a fixed fleet of vehicles is employed to obtain rewards by visiting nodes in a network. All vehicles share common origin and destination locations. Since each vehicle has a limitation in time or traveling distance, not all nodes in the network can be visited. Hence, the goal is focused on the maximization of the collected reward, taking into account the aforementioned constraints. Most of the existing literature on the TOP focuses on its deterministic version, where rewards and travel times are assumed to be predefined values. This paper focuses on a more realistic TOP version, where travel times are modeled as random variables, which introduces reliability issues in the solutions due to the route‐length constraint. In order to deal with these complexities, we propose a simheuristic algorithm that hybridizes biased‐randomized heuristics with a variable neighborhood search and MCS. To test the quality of the solutions generated by the proposed simheuristic approach, we employ the well‐known sample average approximation (SAA) method, as well as a combination model that hybridizes the metaheuristic used in the simheuristic approach with the SAA algorithm. The results show that our proposed simheuristic outperforms the SAA and the hybrid model both on the objective function values and computational time. [ABSTRACT FROM AUTHOR]

Background and Objectives: Orienteering is a sport characterized by high physical exertion and intense mental demands, which increase susceptibility to errors. Understanding the impact of such errors on psychophysiological responses, particularly on heart rate variability (HRV), is essential. This study aimed to investigate the relationship between psychophysiological indicators and checkpoint errors made by elite orienteers during official competition. Materials and Methods: Fifty-three orienteers participated in this study, and their performance was continuously monitored and recorded by using a global positioning system (GPS) and HRV data. Errors made during the orienteering events were identified and analyzed. HRV data were examined in three temporal segments: before, during, and after the identified and standardized errors. Results: The analyses indicated that errors significantly impacted HRV indices across multiple domains: the time domain, frequency domain, and nonlinear domain. Additionally, a significant effect of sex on the normalized the root mean square of successive differences (r-MSSD) before and after the error was observed. Conclusions: The findings of this study underscore the significant impact of errors made by orienteers on cardiovascular responses, as evidenced by measurable alterations in HRV metrics. Cardiovascular activity, represented by the HRV, can provide useful information for coaches and sport psychologists to adopt effective training programs for athletes. [ABSTRACT FROM AUTHOR]

Recently, we published self-reported data concerning the perceived effects of the menstrual cycle and hormonal contraceptives on the training and performance of 1086 female athletes participating in 57 sports. However, studies comparing differences between sports with large sample sizes are lacking. The aim of this ancillary study was to compare the impact of the menstrual cycle and hormonal contraceptives on training and performance between the three largest sports in the cohort, that is, football, handball, and orienteering, as well as the knowledge and support provided to the female athletes engaged in these sports. The results are from a web-based questionnaire completed by 312 football players, 243 handball players, and 93 orienteers. A total of 54% of the orienteers planned their training based on the menstrual cycle, which was a higher proportion compared to football (45%) and handball (29%) players (p < 0.05). Fewer football players believed that the menstrual cycle had an impact on their performance during training and competition compared to the two other sports. A high proportion of the athletes used hormonal contraceptives in all three sports, but a lower proportion of the orienteers (40%) used hormonal contraceptives compared to football (76%) and handball players (66%) (p < 0.05). Football and handball players received overall more support than orienteers, and the support was mostly provided by a physiotherapist and/or a strength and conditioner coach besides the main coach. These findings demonstrate that the perceived influence of the menstrual cycle and hormonal contraceptives on training and performance differs between endurance athletes in orienteering compared to team-sport athletes in football and handball. [ABSTRACT FROM AUTHOR]

Copyright of Journal of ROL Sport Sciences / ROL Spor Bilimleri Dergisi is the property of Journal of ROL Sport Sciences / ROL Spor Bilimleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: This article discusses how to plan the transport of fattened pigs from farms to the abattoir efficiently when the farms are coordinated with the abattoir operation. In contrast to the papers published to date that have been concerned with the farmer's profit, this paper adopts the slaughterhouse perspective. Coordination with growers should help to better plan the collection of pigs by establishing the best routes for the trucks to ensure the optimal functioning of the abattoir. Starting from a real scenario, the study delves into a complex Team Orienteering Problem, taking into account factors such as stochastic production and maximum truck workload. The paper adopts the PJS heuristic and compares it with exact methods, revealing the computational infeasibility of the latter. Through scenario analysis, the study unveils insights into practical solutions, highlighting a positive relationship between the exploration of alternative routes, the number of trips, transport costs, and maximum reward. In particular, greater variability in the number of pigs to be collected presents opportunities for a more efficient mix of loads, especially if one truck can visit at least one more farm. These results offer valuable insights for optimizing the pig transport logistics, potentially improving the profitability and sustainability. In the context of pig farming, this paper addresses the optimization problem of collecting fattened pigs from farms to deliver them to the abattoir. Assuming that the pig sector is organized as a competitive supply chain with narrow profit margins, our aim is to apply analytics to cope with the uncertainty in production costs and revenues. Motivated by a real-life case, the paper analyzes a rich Team Orienteering Problem (TOP) with a homogeneous fleet, stochastic demands, and maximum workload. After describing the problem and reviewing the related literature, we introduce the PJS heuristic. Our approach is first compared with exact methods, which are revealed as computationally unfeasible. Later, a scenario analysis based on a real instance was performed to gain insight into the practical aspects. Our findings demonstrate a positive correlation between the number of alternative routes explored, the number of trips, the transportation cost, and the maximum reward. Regarding the variability in the number of pigs to collect, when a truck can visit more than one farm, better solutions can be found with higher variability since the load can be combined more efficiently. [ABSTRACT FROM AUTHOR]

The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25 % better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems. [ABSTRACT FROM AUTHOR]

Team score orienteering, a challenging and interesting sport, is gradually becoming known by the majority of sports enthusiasts. Integrating team score orienteering with the Internet can enhance the interactive experience for athletes. However, this integration increases the risk of the leakage of the athletes' information. In order to protect the privacy of athletes, it is necessary to employ encryption. Therefore, this paper proposes an efficient lattice-based revocable certificateless public key encryption (RCL-PKE) scheme with decryption key exposure resistance (DKER). The adoption of certificateless encryption not only avoids the complex certificate management required for traditional public key encryption, but also addresses the key escrow problem of identity-based encryption, thereby significantly ensuring data security and privacy. Furthermore, the revocable mechanism enables the organizing committee to flexibly manage the athletes' qualification for competitions, and DKER can effectively prevent the leakage of decryption keys, which further enhances data security. The constructed RCL-PKE scheme was proven to be IND-CPA secure under the learning with errors (LWE) assumption. Experiments indicated that the proposed RCL-PKE scheme had lower computation and communication costs, making it particularly suitable for team score orienteering. [ABSTRACT FROM AUTHOR]

Background: The Clustered Orienteering Problem is an optimization problem faced in last-mile logistics. The aim is, given an available time window, to visit vertices and to collect as much profit as possible in the given time. The vertices to visit have to be selected among a set of service requests. In particular, the vertices belong to clusters, the profits are associated with clusters, and the price relative to a cluster is collected only if all the vertices of a cluster are visited. Any solving methods providing better solutions also imply a new step towards sustainable logistics since companies can rely on more efficient delivery patterns, which, in turn, are associated with an improved urban environment with benefits both to the population and the administration thanks to an optimized and controlled last-mile delivery flow. Methods: In this paper, we propose a constraint programming model for the problem, and we empirically evaluate the potential of the new model by solving it with out-of-the-box software. Results: The results indicate that, when compared to the exact methods currently available in the literature, the new approach proposed stands out. Moreover, when comparing the quality of the heuristic solutions retrieved by the new model with those found by tailored methods, a good performance can be observed. In more detail, many new best-known upper bounds for the cost of the optimal solutions are reported, and several instances are solved to optimality for the first time. Conclusions: The paper provides a new practical and easy-to-implement tool to effectively deal with an optimization problem commonly faced in last-mile logistics. [ABSTRACT FROM AUTHOR]

Exercise enhances aspects of human cognition, but its intensity may matter. Recent animal research suggests that vigorous exercise, which releases greater amounts of lactate, activates more brain-derived neurotrophic factor (BDNF) in the hippocampus and, thus, may be optimal for supporting cognitive function. The cognitive benefits of exercise may be further augmented when combined with cognitive training. The sport of orienteering simultaneously combines exercise with spatial navigation and, therefore, may result in greater cognitive benefits than exercising only, especially at vigorous intensities. The present study aimed to examine the effects of an acute bout of orienteering at different intensities on cognition and BDNF compared to exercising only. We hypothesized that vigorous-intensity orienteering would increase lactate and BDNF and improve cognition more than moderate-intensity orienteering or vigorous exercise alone. Sixty-three recreationally active, healthy young adults (Mage = 21.10±2.75 years) with no orienteering experience completed a 1.3 km intervention course by navigating and exercising at a vigorous (80–85% of heart rate reserve) or moderate (40–50% of heart rate reserve) intensity or exercising vigorously without navigation. Exercise intensity was monitored using peak lactate, heart rate and rating of perceived exertion. Serum BDNF was extracted immediately before and after the intervention. Memory was assessed using the Mnemonic Similarity Task (high-interference memory) and the Groton Maze Learning Test (spatial memory). Both exercising and orienteering at a vigorous intensity elicited greater peak lactate and increases in BDNF than moderate-intensity orienteering, and individuals with higher peak lactate also had greater increases in BDNF. High-interference memory improved after both vigorous-intensity interventions but did not improve after the moderate-intensity intervention. Spatial memory only increased after vigorous-intensity orienteering, suggesting that orienteering at a vigorous intensity may particularly benefit spatial cognition. Overall, the results demonstrate the benefits of vigorous exercise on human cognition and BDNF. [ABSTRACT FROM AUTHOR]

The use of drones for collecting information and detecting bugs in orchards covered by nets is a challenging problem. The nets help in reducing pest damage, but they also constrain the drone's flight path, making it longer and more complex. To address this issue, we model the orchard as an aisle-graph, a regular data structure that represents consecutive aisles where trees are arranged in straight lines. The drone flies close to the trees and takes pictures at specific positions for monitoring the presence of bugs, but its energy is limited, so it can only visit a subset of positions. To tackle this challenge, we introduce the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the orienteering problem, where likely infested locations are prioritized by assigning them a larger profit. Additionally, the drone's movements have a cost in terms of energy, and the objective is to plan a drone's route in the most profitable locations under a given drone's battery. We show that SOAP can be optimally solved in polynomial time, but for larger orchards/instances, we propose faster approximation and heuristic algorithms. Finally, we evaluate the algorithms on synthetic and real datasets to demonstrate their effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

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This paper addresses a multiobjective version of the Team Orienteering Problem (TOP). The TOP focuses on selecting a subset of customers for maximum rewards while considering time and fleet size constraints. This study extends the TOP by considering two objectives: maximizing total rewards from customer visits and maximizing visits to prioritized nodes. The MultiObjective TOP (MO-TOP) is formulated mathematically to concurrently tackle these objectives. A multistart biased-randomized algorithm is proposed to solve MO-TOP, integrating exploration and exploitation techniques. The algorithm employs a constructive heuristic defining biefficiency to select edges for routing plans. Through iterative exploration from various starting points, the algorithm converges to high-quality solutions. The Pareto frontier for the MO-TOP is generated using the weighted method, epsilon-constraint method, and Epsilon-Modified Method. Computational experiments validate the proposed approach's effectiveness, illustrating its ability to generate diverse and high-quality solutions on the Pareto frontier. The algorithms demonstrate the ability to optimize rewards and prioritize node visits, offering valuable insights for real-world decision making in team orienteering applications. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we study the Multi-Start Team Orienteering Problem (MSTOP), a mission re-planning problem where vehicles are initially located away from the depot and have different amounts of fuel. We consider/assume the goal of multiple vehicles is to travel to maximize the sum of collected profits under resource (e.g., time, fuel) consumption constraints. Such re-planning problems occur in a wide range of intelligent UAS applications where changes in the mission environment force the operation of multiple vehicles to change from the original plan. To solve this problem with deep reinforcement learning (RL), we develop a policy network with self-attention on each partial tour and encoder-decoder attention between the partial tour and the remaining nodes. We propose a modified REINFORCE algorithm where the greedy rollout baseline is replaced by a local mini-batch baseline based on multiple, possibly non-duplicate sample rollouts. By drawing multiple samples per training instance, we can learn faster and obtain a stable policy gradient estimator with significantly fewer instances. The proposed training algorithm outperforms the conventional greedy rollout baseline, even when combined with the maximum entropy objective. The efficiency of our method is further demonstrated in two classical problems – the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP). The experimental results show that our method enables models to develop more effective heuristics and performs competitively with the state-of-the-art deep reinforcement learning methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the Multi-Start Team Orienteering Problem (MSTOP), a mission re-planning problem where vehicles are initially located away from the depot and have different amounts of fuel. We consider/assume the goal of multiple vehicles is to travel to maximize the sum of collected profits under resource (e.g., time, fuel) consumption constraints. Such re-planning problems occur in a wide range of intelligent UAS applications where changes in the mission environment force the operation of multiple vehicles to change from the original plan. To solve this problem with deep reinforcement learning (RL), we develop a policy network with self-attention on each partial tour and encoder-decoder attention between the partial tour and the remaining nodes. We propose a modified REINFORCE algorithm where the greedy rollout baseline is replaced by a local mini-batch baseline based on multiple, possibly non-duplicate sample rollouts. By drawing multiple samples per training instance, we can learn faster and obtain a stable policy gradient estimator with significantly fewer instances. The proposed training algorithm outperforms the conventional greedy rollout baseline, even when combined with the maximum entropy objective. The efficiency of our method is further demonstrated in two classical problems – the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP). The experimental results show that our method enables models to develop more effective heuristics and performs competitively with the state-of-the-art deep reinforcement learning methods. [ABSTRACT FROM AUTHOR]

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Copyright of SPORMETRE: The Journal of Physical Education & Sport Sciences / Beden Eğitimi ve Spor Bilimleri Dergisi is the property of SPORMETRE: The Journal of Physical Education & Sport Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a major real‐world problem, snow plowing has been studied extensively. However, most studies focus on deterministic settings with little urgency yet enough time to plan. In contrast, we assume a severe snowstorm with little known data and little time to plan. We introduce a novel time‐dependent multi‐visit dynamic safe street snow plowing problem and formulate it on a rolling‐horizon‐basis. To solve this problem, we develop an adaptive large neighborhood search as the underlying method and validate its efficacy on team orienteering arc routing problem benchmark instances. We create real‐world‐based instances for the city of Vienna and examine the effect of (i) different snowstorm movements, (ii) having perfect information, and (iii) different information‐updating intervals and look‐aheads for the rolling horizon method. Our findings show that different snowstorm movements have no significant effect on the choice of rolling horizon settings. They also indicate that (i) larger updating intervals are beneficial, if prediction errors are low, and (ii) larger look‐aheads are better suited for larger updating intervals and vice versa. However, we observe that less look‐ahead is needed when prediction errors are low. [ABSTRACT FROM AUTHOR]

In the Set Orienteering Problem, a single vehicle, leaving from and returning to a depot, has to serve some customers, each one associated with a given spacial location. Customers are grouped in clusters and a given prize is collected once a customer in a cluster is visited. The prize associated with a cluster can be collected at most once. Travel times among locations are provided, together with a maximum available mission time, which normally makes it impossible to visit all the clusters. The target is to design a route for the vehicle that maximizes the total prize collected within the given time limit. In this study, building on the recent literature, we present new preprocessing rules and a new constraint programming model for the problem. Thanks to the symmetry exploitation carried out by the constraint programming solver, new state-of-the-art results are established. [ABSTRACT FROM AUTHOR]

We extend the classical problem setting of the orienteering problem (OP) to incorporate multiple drones that cooperate with a truck to visit a subset of the input nodes. We call this problem the OP with multiple drones (OP-mD). Drones have a limited battery endurance, and thus, they can either move together with the truck at no energy cost for the battery or be launched by the truck onto short flights that must start and end at different customer locations. A drone serves exactly one customer per flight. Moreover, the truck and the drones must wait for each other at the landing locations. A customer prize can be collected at most once, either upon visiting it by the truck or upon serving it by a drone. Similarly to the OP, we maximize the total collected prize under the condition that the truck and the drones return to the depot within a given amount of time. We provide a mixed-integer linear programming formulation for the OP-mD and devise a tailored branch-and-cut algorithm based on a novel decomposition of the problem. We solve instances of the OP-mD with up to 50 nodes within one hour of CPU time with a standard computational setup. Finally, we adapt our framework to solve closely related problems in the literature and compare the resulting computational performance with that of previous studies. [ABSTRACT FROM AUTHOR]

Consider deploying a team of robots in order to visit sites in a risky environment (i.e., where a robot might be lost during a traversal), subject to team-based operational constraints such as limits on team composition, traffic throughputs, and launch constraints. We formalize this problem using a graph to represent the environment, enforcing probabilistic survival constraints for each robot, and using a matroid (which generalizes linear independence to sets) to capture the team-based operational constraints. The resulting "Matroid Team Surviving Orienteers" (MTSO) problem has broad applications for robotics such as informative path planning, resource delivery, and search and rescue. We demonstrate that the objective for the MTSO problem has submodular structure, which leads us to develop two polynomial time algorithms which are guaranteed to find a solution with value within a constant factor of the optimum. The second of our algorithms is an extension of the accelerated continuous greedy algorithm, and can be applied to much broader classes of constraints while maintaining bounds on suboptimality. In addition to in-depth analysis, we demonstrate the efficiency of our approaches by applying them to a scenario where a team of robots must gather information while avoiding dangers in the Coral Triangle and characterize scaling and parameter selection using a synthetic dataset. [ABSTRACT FROM AUTHOR]

This paper addresses a real‐world multi‐period orienteering problem arising in a large Italian company that needs to patrol an area in order to provide security services to a set of customers. Each customer requires different services on a weekly basis. Some services are mandatory, while others are optional. It might be impossible to perform all optional services, and each of them is assigned a score when performed. The challenge is to determine a set of routes, one per day, that maximizes a weighted sum of the total collected score and total working time, while meeting several operational constraints, including hard time windows, maximum riding time, minimum number of services performed, and minimum time between two consecutive visits for the same service at the same customer. To solve the problem, we propose an iterated local search that invokes at each iteration an inner variable neighborhood descent procedure. Computational tests performed on a large number of real‐world instances prove that the developed algorithm is very efficient, and finds in a short time solutions that are consistently better than those produced by a mathematical model, and those in use at the company. [ABSTRACT FROM AUTHOR]

Copyright of SEMS-Journal is the property of Schwiez Zschr Sportmed Sporttraumatologie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study is to examine the data on publication type, publication year, publication language, research method, data collection tools, sample type and research topics in academic studies related to orienteering sport in Türkiye between 2001-2021. Content analysis method, one of the qualitative research methods, has been used as the research model. The data were collected by literature review and analyzed using explanatory content analysis technique. As a result, it has been determined that the number of studies has increased as of 2017, the cognitive aspect of orienteering sports has come to the fore as a subject, and the number of doctoral dissertations is insufficient. In the studies examined, Turkish as a language, quantitative studies as a method, and athletes as a sample type are more numerous. According to the results of this study, the following suggestions can be made: The number of doctoral theses, the number of studies in English language that can be published in international refereed journals, the number of studies conducted by qualitative and mixed methods, studies related to different groups such as referees, coaches and managers should be increased. Orientation sport-specific attitude scale or questionnaire development studies should be done. A wider literature review should be made by considering the current developments and the relations of orienteering sports with different disciplines. Since orienteering sport has cognitive characteristics and can be used as an educational tool, studies related to different disciplines should be carried out. [ABSTRACT FROM AUTHOR]

Copyright of Revista Andaluza de Medicina del Deporte is the property of Centro Andaluza de Medicina del Deporte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Spor Hekimligi Dergisi/Turkish Journal of Sports Medicine is the property of Turkish Journal of Sports Medicine / Spor Hekimleri Dernegi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The basic characteristic of vehicle routing problems with profits (VRPP) is that locations to be visited are not predetermined. On the contrary, they are selected in pursuit of maximizing the profit collected from them. Significant research focus has been directed toward profitable routing variants due to the practical importance of their applications and their interesting structure, which jointly optimizes node selection and routing decisions. Profitable routing applications arise in the tourism industry aiming to maximize the profit score of attractions visited within a limited time period. In this paper, a new VRPP is introduced, referred to as the bus sightseeing problem (BSP). The BSP calls for determining bus tours for transporting different groups of tourists with different preferences on touristic attractions. Two interconnected decision levels have to be jointly tackled: assignment of tourists to buses and routing of buses to the various attractions. A mixed‐integer programming formulation for the BSP is provided and solved by a Benders decomposition algorithm. For large‐scale instances, an iterated local search based metaheuristic algorithm is developed with some tailored neighborhood operators. The proposed methods are tested on a large family of test instances, and the obtained computational results demonstrate the effectiveness of the proposed solution approaches. [ABSTRACT FROM AUTHOR]

Location-based games have been around since 2000 across various fields, including education, health, and entertainment. The main challenge facing such games is content generation. In contrast to normal games, content in location-based games is inherently dependent on location. The biggest challenge is the availability of the content globally. Other challenges include player engagement, enjoyable interactions with the real-world environment, safety, and customizability based on player performance and preference. While crowdsourcing has often been adopted as a tool for content creation, this approach requires quality control. Designing highquality content requires detailed guidelines. In this paper, we introduce design principles for the creation of high-quality content that can survive for long periods of time. These principles are derived from ten years of experience running our in-house orienteering treasure-hunt game called O-Mopsi, which represents a case study in this paper. O-Mopsi allows players to visit pre-defined locations. The design principles are expected to be generalizable to other location-based games as well as to the creation of sightseeing tours more generally. [ABSTRACT FROM AUTHOR]

Objective: Taking orienteering as an example, this study aimed to reveal the effects of mental rotation on orienteers' map representation and their brain processing characteristics. Methods: Functional near-infrared spectroscopic imaging (fNIRS) was used to explore the behavioral performance and cortical oxyhemoglobin concentration changes of map-represented cognitive processing in orienteering athletes under two task conditions: normal and rotational orientation. Results: Compared to that in the normal orientation, athletes' task performance in the rotated orientation condition was significantly decreased, as evidenced by a decrease in correct rate and an increase in reaction time; in the normal orientation condition, blood oxygen activation in the dorsolateral prefrontal lobe was significantly greater than that in the ventral prefrontal lobe, which was significantly correlated with the correct rate. With rotating orientation, the brain oxygen average of each region of interest was enhanced, and the brain region specifically processed was the ventral prefrontal lobe, specifically correlating with the correct rate. Conclusions: Mental rotation constrains the map representation ability of athletes, and map representation in rotational orientation requires more functional brain activity for information processing. Ventral lateral prefrontal lobe activation plays an important role in the map representation task in rotational orientation. [ABSTRACT FROM AUTHOR]

Career development is a lifelong journey that involves a series of choices, decisions and transitions. In today's fast-paced and ever-changing world, effective decision making is a crucial skill for career success. The ability to make informed and effective decisions plays an important role for individuals to choose the right career paths, make strategic choices in one's profession and adapt to unforeseen challenges. Making career decisions in an appropriate and correct way will facilitate individuals to struggle with career obstacles they face throughout their lives. For this purpose, the relationship between pre-service teachers' career decisionmaking processes and orienteering practices was examined in this study. Phenomenology, one of the qualitative research approaches, was used in the research process. Ten pre-service teachers studying at the faculty of education participated in the research. Semi-structured interview questions consisting of 5 questions were used as data collection tools. The data obtained were analysed using qualitative data analysis methods. In this context, content analysis was used. As a result of the application, it was determined that orienteering practices had positive effects on the career decision-making processes of pre-service teachers. [ABSTRACT FROM AUTHOR]

The orienteering problem (OP) and prize‐collecting traveling salesman problem (PCTSP) are two typical TSPs with profits, in which each vertex has a profit and the goal is to visit several vertices to optimize the collected profit and travel costs. The OP aims to collect the maximum profit without exceeding the given travel cost. The PCTSP seeks to minimize the travel costs while ensuring a minimum profit threshold. This study introduces a hybrid genetic algorithm that addresses both the OP and PCTSP under a unified framework. The algorithm combines an extended edge‐assembly crossover operator to produce promising offspring solutions, and an effective local search to ameliorate each offspring solution. The algorithm is further enforced by diversification‐oriented mutation and population‐diversity management. Extensive experiments demonstrate that the method competes favorably with the best existing methods in terms of both the solution quality and computational efficiency. Additional experiments provide insights into the roles of the key components of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Online Journal of Recreation & Sports is the property of Online Journal of Recreation & Sports and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Research in Sports Science is the property of Aves Yayincilik Ltd. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mediterranean Journal of Sport Science (MJSS) is the property of Mediterranean Journal of Sport Science (MJSS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper discusses an orienteering optimization problem where a vehicle using electric batteries must travel from an origin depot to a destination depot while maximizing the total reward collected along its route. The vehicle must cross several consecutive regions, with each region containing different types of charging nodes. A charging node has to be selected in each region, and the reward for visiting each node—in terms of a 'satisfactory' charging process—is a binary random variable that depends upon dynamic factors such as the type of charging node, weather conditions, congestion, battery status, etc. To learn how to efficiently operate in this dynamic environment, a hybrid methodology combining simulation with reinforcement learning is proposed. The reinforcement learning component is able to make informed decisions at each stage, while the simulation component is employed to validate the learning process. The computational experiments show how the proposed methodology is capable of design routing plans that are significantly better than non-informed decisions, thus allowing for an efficient management of the vehicle's battery under such dynamic conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Sport for All & Recreation / Herkes Için Spor ve Rekreasyon is the property of Journal of Sport for All & Recreation / Herkes icin Spor ve Rekreasyon Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heterogeneous unmanned vehicles (UVs) are used in various defense and civil applications. Some of the civil applications of UVs for gathering data and monitoring include civil infrastructure management, agriculture, public safety, law enforcement, disaster relief, and transportation. This paper presents a two-stage stochastic model for a fuel-constrained UV mission planning problem with multiple refueling stations under uncertainty in availability of UVs. Given a set of points of interests (POI), a set of refueling stations for UVs, and a base station where the UVs are stationed and their availability is random, the objective is to determine route for each UV starting and terminating at the base station such that overall incentives collected by visiting POIs is maximized. We present an outer approximation based decomposition algorithm and the skewed variable neighborhood search heuristic to solve large instances, and perform extensive computational experiments using random instances. Additionally, a data driven simulation study is performed using robot operating system (ROS) framework to corroborate the use of the stochastic programming approach. [ABSTRACT FROM AUTHOR]

Copyright of Studia Kinanthropologica is the property of University of South Bohemia, Faculty of Education, Department of Physical Education & Sport and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a team orienteering problem with time windows considering multiple deliverymen (TOPTWMD), which arises from practical situations where firms deliver goods to customers in congested urban areas. In addition to the routing and scheduling decisions, the crew size for each route also has to be decided. Adapted from iterative three-component heuristic (I3CH), an enhanced heuristic is designed for this problem. Integrating two effective methods and the alteration between feasible and infeasible regions, the proposed heuristic finally consists of three components, including (i) an iterated local search in feasible region, (ii) a tabu search in infeasible region, and (iii) a route recombination for a high-quality solution. Besides, due to the particularity of deliverymen assignment, some adaptive changes must be made accordingly compared with the original I3CH. The TOPTWMD instances are generated by modifying the Solomon instances. To evaluate the performance of the enhanced heuristic, computational experiments are conducted on a set of 138 instances for comparing enhanced heuristic with CPLEX and original I3CH. Results demonstrate the effectiveness and the stability of the proposed algorithm. Moreover, additional analyses verify that the joint use of the three components and the alteration between feasible and infeasible regions make significant contributions to the performance of the enhanced heuristic. [ABSTRACT FROM AUTHOR]

Orienteering is an enjoyable sporting activity, which is believed to contribute to its participants' problem-solving skills. People who are engaged in orienteering is expected to hold a map correctly, find their exact place on the map, read the map, make a plan and carry it out to reach the checkpoint, as well as checking the accuracy of the destination. Such steps to be followed during orienteering resemble those of Polya's problem-solving method in education. From this standpoint, this study focused on Polya's problem-solving steps in the field of mathematics, in relation to the outdoor sport of orienteering. On the whole, the present study aimed to examine fourth-grade students at primary school concerning their involvement in an orienteering process as to the steps of Polya's problem-solving method. A case study method was employed from among qualitative research approaches, and a program implementation was adopted as one of the sub-types of the case study method. In this context, the study sample was selected with the convenience sampling technique, from among the purposive sampling techniques. Observations, semi-structured interviews, and head cameras were used as data collection tools. The results indicated that the steps of Polya's problem-solving method can be used for problem-solving during the orienteering process. Based on the results, it is recommended that practical studies be conducted with similar or different research methods so as to contribute to the field. [ABSTRACT FROM AUTHOR]

Wildfires are a natural phenomenon that regularly occurs in many terrestrial ecosystems. Due to global warming, the rate and the span of wildfires have remarkably increased during the last years, causing important economic losses and human casualties. Several initiatives have been undertaken in the last years in order to apply operations research tools to help firefighting teams schedule and optimize their protection activities when dealing with wildfires. In this context, a recent variant of the Team Orienteering Problem, referred to as the Asset Protection Problem, was proposed in van der Merwe et al. (2015). In this problem, firefighting teams provide a protective service to a set of assets endangered by wildfires. These activities can be performed by a heterogeneous fleet of vehicles and occur within specific time intervals estimated on the basis of fire fronts progression. This problem incorporates three additional constraints: time windows, synchronized visits, and compatibility constraints between vehicles and assets. In this paper, we propose a hybrid approach that combines a Greedy Randomized Adaptive Search Procedure coupled with an Iterated Local Search (GRASP×ILS) and a post-optimization phase based on a set covering formulation. Interestingly, GRASP×ILS incorporates an adaptive candidate list-based insertion heuristic and a Variable Neighborhood Descent search procedure. Detailed computational tests were carried out on benchmark instances from the literature. The results show that our method outperforms the other methods in the literature, since it improves all the best-known solutions on medium- and large-size instances, while maintaining shorter computational times. [ABSTRACT FROM AUTHOR]