Search Results (57)

This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. The proposed method builds on the transformer architecture and is trained to construct routes that adapt to real-time variations, such as traffic and environmental changes. A key contribution of this work is the model’s ability to generalize across problem instances with varying numbers of nodes and vehicles, eliminating the need for retraining when problem size changes. To assess performance, a comprehensive set of experiments involving 27,000 synthetic instances is conducted, comparing the RL model with a variable neighborhood search metaheuristic. The results indicate that the RL model achieves competitive solution quality while requiring significantly less computational time. Moreover, the RL approach consistently produces feasible solutions across all dynamic instances, demonstrating strong robustness in meeting time constraints. These findings suggest that learning-based methods can offer efficient, scalable, and adaptable solutions for routing problems in dynamic and uncertain environments. Full article

In the context of climate change, it becomes of utmost importance to limit the negative impact of industrial activities on carbon emissions, water stress, biodiversity loss, and natural resources depletion. Whether we consider the situation from a product-centered perspective (life cycle, R&D&I process, tools, methods, design, production, etc.) or from a human-centered perspective (habits, practices, fixation, strategic orientations, emotional sensitivity, etc.), coming years will represent a formidable upheaval for companies. To support this transition, various tools assessing products’ impact have been developed over the past decade. They aim at guiding decision makers, integrating new criteria to assess project success, and promoting the development and industrialization of solutions answering pressing environmental issues. If assessment is a key factor of success, it has become clear that processes and practices also need to evolve for practitioners to properly integrate sustainable requirements from the initial stages of their project. In that context, biomimicry, the approach aimed at taking nature as a model to support the design of more sustainable solutions, has been the center of growing interest. However, no integrated methods exist in the cosmetics sector to assess if a product is properly developed through biomimicry. This missing framework led to difficulties for cosmetic companies to support eco-design through biomimicry. In this article, we present a method called Biomim’Index developed by L’Oréal research and innovation sustainable development team to address three objectives: (i) to characterize cosmetic technologies according to whether they are based on bioinspiration, biomimetics or biomimicry; (ii) to guide the project’s leaders to identify key steps to improve existing cosmetic technologies through biomimicry; and (iii) to support the integration of biomimicry as an operational approach towards the development of new sustainable cosmetic technologies. This method, focusing on the problem-driven biomimetic approach is based on a combination of procedural requirements from the biomimetics TC288 18458:2015 ISO norm and environmental design requirements from L’Oréal for the Future (L4TF) commitments. Results present a proof of concept to outline the method’s efficiency and limits to support innovative eco-designed projects and value cosmetic technologies designed through biomimicry. Full article

This paper addresses the team orienteering problem applied to unmanned aerial vehicles (UAVs), considering obstacle avoidance and environmental factors such as wind conditions and payload weight. The objective is to optimize UAV routes to maximize collected rewards while adhering to operational constraints. To achieve this, we employ a simheuristic algorithm for the overall route optimization, while integrating the A* algorithm to determine feasible paths between nodes that avoid obstacles in a 2D grid-based environment. Then, a feedforward neural network estimates travel time based on UAV speed, wind conditions, trajectory distance, and payload weight. This estimation is incorporated into the optimization process to improve route planning accuracy. Numerical experiments evaluate the impact of various parameters, including obstacle placement, UAV speed, wind conditions, and payload weight. These experiments include maps with 30 to 100 points of interest and varying obstacle densities and show that our hybrid method improves solution quality by up to $15\%$ in total profit compared to a baseline approach. Furthermore, computation times remain within 5–10% of the baseline, showing that the added predictive layer maintains computational efficiency. Full article

This paper introduces the Enhanced Team-Oriented Swarm Optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm Optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly encountered in nature-inspired optimization approaches. ETOSO enhances TOSO by integrating innovative strategies for exploration and exploitation, resulting in a simplified algorithm that demonstrates superior performance across a broad spectrum of benchmark functions, particularly in high-dimensional search spaces. A comprehensive comparative evaluation and statistical tests against 26 established nature-inspired optimization algorithms (NIOAs) across 15 benchmark functions and dimensions (D = 2, 5, 10, 30, 50, 100, 200) confirm ETOSO’s superiority relative to solution accuracy, convergence speed, computational complexity, and consistency. Full article

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model. Full article

This paper addresses the team orienteering problem (TOP) with vehicles equipped with electric batteries under dynamic travel conditions influenced by weather and traffic, which impact travel times between nodes and hence might have a critical effect on the battery capacity to cover the planned route. The study incorporates a novel approach for solving the dynamic TOP, comparing two solution methodologies: a merging heuristic and a reinforcement learning (RL) algorithm. The heuristic combines routes using calculated savings and a biased-randomized strategy, while the RL model leverages a transformer-based encoder–decoder architecture to sequentially construct solutions. We perform computational experiments on 50 problem instances, each subjected to 200 dynamic conditions, for a total of 10,000 problems solved. The results demonstrate that while the deterministic heuristic provides an upper bound for rewards, the RL model consistently yields robust solutions with lower variability under dynamic conditions. However, the dynamic heuristic, with a 20 s time limit for solving each instance, outperformed the RL model by $3.35\%$ on average. The study highlights the trade-offs between solution quality, computational resources, and time when dealing with dynamic environments in the TOP. Full article

This paper presents the results of a protocol study exploring the impact of various digital technologies on team collaborative design processes. Previous studies have suggested that compared to traditional methods such as sketching, digital technologies can provide further benefits for collaborative processes. However, there persists a lack of understanding about the impacts of digital technologies on such processes, particularly in relation to emerging significant digital technologies such as immersive Virtual Reality (VR). Therefore, this study aims to fill that gap by exploring team collaboration behaviours of two groups of professionals working in two digital design environments—desktop 3D modelling with Revit and immersive VR using Hyve-3D—as well as their behaviours during traditional sketching sessions for benchmarking purposes. Utilising protocol analysis method, the think-aloud data of participants was recorded, transcribed and coded using an adapted collaborative practice model. Team collaboration activities are broadly categorised as ‘Content’ or ‘Process’: content referring to design task-based activities, while process refers to activities related to the organising of group processes. The results suggest that during the design collaboration process, designers allocated the majority of their efforts towards process-oriented design activities. Differences between design environments only had a minor impact on the amount of effort expended on process-oriented activities and content-oriented activities. Moreover, traditional sketching design environments were shown to be potentially beneficial for problem-solution and associated negotiation activities. Additionally, immersive environments were associated with a reduction in the designers’ cognitive effort that was expended on exploring the design environment. Full article

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. Full article

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. Full article

We consider a two-group social conflict under the corporates’ research and development (R&D) business processes. Conflict participants are divided into two groups depending on their attitude to new ideas, technologies, and behavioral style for R&D creative problems—innovators and adapters. We reveal the contradiction that arises between the need to include both types of employees in one project team and their objectively antagonistic positions regarding the methods and approaches to R&D processes. The proposed research methodology is based on a modern post-non-classical paradigm formed on the principles of coherence, interdisciplinarity, openness, and nonlinearity, as well as a sociophysical approach to the social conflicts modeling. We use the general theories of magnetism, paramagnetism, and functions of P. Langevin and L. Brillouin to describe the dynamics of group participants’ preferences regarding the style of conflict behavior. The analogy of paramagnetism, consisting in the orienting effect of the magnetic field, is used to describe social groups interactions that have not only their own interests, but are also influenced by the opinions of opposite social groups. A two-dimensional, four-parameter map represents the dynamics of group conflict. Modeling results show that regardless of the initial states and with certain parameters of intra-group and intergroup interactions, the trajectories eventually converge to an attractor (limit cycle) in a two-dimensional space. No non-periodic or chaotic modes are identified in the two-group conflict, which determines the controllability of the described conflict. The results of the simulation experiments are used as decision support and contradictions resolution aimed at forming the required modes of the corporates’ research and development business processes and ensuring the group participants’ cohesion and depolarization. The results of testing the model at an industrial enterprise are presented. Full article

Accurate specimen marking is crucial during breast cancer surgery to avoid misorientation, which can lead to inadequate re-excision and tumor recurrence. We studied the marking methods at various breast cancer centers to create a tool that would prevent specimen misorientation. An online questionnaire was used to survey marking procedures at major breast cancer centers in Hungary, and a tool was developed using a troubleshooting method. Twelve out of twenty units responded (60%). Nine use an institutionally standardized marking system. Less than half of the surgical teams found specimen mammograms to be unambiguous. In more than 70% of departments, pathologists were uncertain about breast specimen orientation. Ambiguous marking methods caused orientation errors in half of the cases, while unclear marking directions caused the rest. Most pathologists (85%) and surgeons (75%) believed that coronal plane specimen mammography would help solve the problem. A plastic specimen plate has been developed to anchor breast tissue to a coronal breast scheme as seen in mammography images, providing clear localization information throughout the surgical process. There is a lack of standardization in breast specimen orientation and marking in Hungary. An optimized orientation toolkit is being developed to ensure consistent interpretation of specimen mammograms by surgeons and pathologists. Full article

Object-oriented programming (OOP) has long been a dominant paradigm in software development, but it is not without its challenges. One major issue is the problem of tight coupling between objects, which can hinder flexibility and make it difficult to modify or extend code. Additionally, the complexity of managing inheritance hierarchies can lead to rigid and fragile designs, making it hard to maintain and evolve the software over time. This paper introduces a software development pattern that seeks to offer a renewed approach to writing code in object-oriented (OO) environments. Addressing some of the limitations of the traditional approach, the Puzzle Pattern focuses on extreme modularity, favoring writing code exclusively in building blocks that do not possess a state (e.g., Java interfaces that support concrete methods definitions in interfaces starting from version 8). Concrete classes are subsequently assembled through the implementation of those interfaces, reducing coupling and introducing a new level of flexibility and adaptability in software construction. The highlighted pattern offers significant benefits in software development, promoting extreme modularity through interface-based coding, enhancing adaptability via multiple inheritance, and upholding the SOLID principles, though it may pose challenges such as complexity and a learning curve for teams. Full article

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. Full article

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. Full article

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. Full article