Search Results (4)

Fumigation effectively manages pests, yet manual spraying poses long-term health risks to operators, making autonomous fumigation robots safer and more efficient. Path planning is a crucial aspect of deploying autonomous robots; it primarily focuses on minimizing energy consumption and maximizing operational time. The Payload and Energy-aware Tactical Allocation Loop (PETAL) algorithm integrates a genetic algorithm to search for waypoint permutations, applies a 2-OPT (two-edge exchange) local search to refine those routes, and leverages an energy cost function that reflects payload weight changes during spraying. This combined strategy minimizes travel distance and reduces energy consumption across extended fumigation missions. To evaluate its effectiveness, a comparative study was performed between PETAL and prominent algorithms such as A*, a hybrid Dijkstra with A*, random search, and a greedy distance-first approach, using both randomly generated environments and a real-time map from an actual deployment site. The PETAL algorithm consistently performed better than baseline algorithms in simulations, demonstrating significant savings in energy usage and distance traveled. On a randomly generated map, the PETAL algorithm achieved 6.05% higher energy efficiency and 23.58% shorter travel distance than the baseline path-planning algorithm. It achieved 15.69% and 31.66% in energy efficiency and distance traveled saved on a real-time map, respectively. Such improvements can diminish operator exposure, extend mission durations, and foster safer, more efficient urban pest control. Full article

This paper investigates the systematic and complete usage of k-opt permutations with $k=2\dots 6$ in application to local optimization of symmetric two-dimensional instances up to ${10}^7$ points. The proposed method utilizes several techniques for accelerating the processing, such that good tours can be achieved in limited time: candidates selection based on Delaunay triangulation, precomputation of a sparse distance matrix, two-level data structure, and parallel processing based on multithreading. The proposed approach finds good tours (excess of 0.72–8.68% over best-known tour) in a single run within 30 min for instances with more than ${10}^5$ points and specifically 3.37% for the largest examined tour containing ${10}^7$ points. The new method proves to be competitive with a state-of-the-art approach based on the Lin–Kernigham–Helsgaun method (LKH) when applied to clustered instances. Full article

The twenty-first century has been full of fundamental changes in consumers’ behavior patterns, especially with the use of diverse social media knowledge-sharing platforms. Therefore, companies have highlighted the significance of knowledge sharing and the importance of social network use in purchasing processes. Accordingly, his paper tries to reveal how consumer purchase behavior (CPB) can be affected by consumer knowledge sharing behavior (CKSB) and the moderating role played by value co-creation dimensions, which are citizenship behavior (CB) and participation behavior (PB), within a sustainable e-commerce field. To test our hypotheses deducted from the literature review, we opted for the PLS-SEM method. We also employed other innovative approaches, such as the IPMA matrix, MAICOM test, FIMIX approach, and CTA analysis, to evaluate the outer and inner model. Our statistical population covered individuals living in Hungary with at least one online purchase involvement. We distributed the questionnaire via various online platforms and, finally, 433 completed questionnaires were prepared for analysis. The results showed that CPB, CB, and PB are positively influenced by the CKSB. However, the link between CPB and CB was not confirmed. As for the moderating role of gender, the permutation test was applied to compare male and female groups and see the difference between them. With a focus on CKSB, this study contributes to the success of international marketing strategies to attain higher competitive advantages. Full article

The traveling salesman problem (TSP) has been widely studied for the classical closed-loop variant. However, very little attention has been paid to the open-loop variant. Most of the existing studies also focus merely on presenting the overall optimization results (gap) or focus on processing time, but do not reveal much about which operators are more efficient to achieve the result. In this paper, we present two new operators (link swap and 3–permute) and study their efficiency against existing operators, both analytically and experimentally. Results show that while 2-opt and relocate contribute equally in the closed-loop case, the situation changes dramatically in the open-loop case where the new operator, link swap, dominates the search; it contributes by 50% to all improvements, while 2-opt and relocate have a 25% share each. The results are also generalized to tabu search and simulated annealing. Full article