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Gotowa bibliografia na temat „Task assignment”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Artykuły w czasopismach na temat "Task assignment"

Zhang, Li. "Complementarity, Task Assignment, and Incentives." Journal of Management Accounting Research 15, no. 1 (January 1, 2003): 225–46. http://dx.doi.org/10.2308/jmar.2003.15.1.225.

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This paper studies the effect of complementarity on task assignment decisions and the design of optimal incentives for employees in charge of multiple tasks. A principal hires two identical agents. Each agent performs two tasks involving complementarities when the tasks vary in the informativeness of their performance measures. The principal tension governing how tasks are assigned lies between the heterogeneity loss due to aggregate performance measures under broad task assignments and the resulting positive information externalities. Although the aggregate performance measures under broad task assignments preclude tailoring the strength of incentives to the nature of the task, these measures are more informative about agent efforts when tasks are complementary. This enhanced informativeness improves contracting efficiency by mitigating the implicit costs of the heterogeneity loss. The analysis applies not only to the task assignment decisions, but also more broadly to other organizational structure decisions.

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Wang, Wenfei, Maolong Lv, Le Ru, Bo Lu, Shiguang Hu, and Xinlong Chang. "Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases." Aerospace 9, no. 9 (September 1, 2022): 491. http://dx.doi.org/10.3390/aerospace9090491.

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Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.

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Bethke, Brett, Mario Valenti, and Jonathan How. "UAV Task Assignment." IEEE Robotics & Automation Magazine 15, no. 1 (March 2008): 39–44. http://dx.doi.org/10.1109/m-ra.2007.914931.

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He, Xiaoxue, Yubo Wang, Xu Zhao, Tiancong Huang, and Yantao Yu. "Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing." Sensors 24, no. 2 (January 19, 2024): 651. http://dx.doi.org/10.3390/s24020651.

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Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user’s participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user’s profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user’s income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

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Lujak, Marin, Alessio Salvatore, Alberto Fernández, Stefano Giordani, and Kendal Cousy. "How to fairly and efficiently assign tasks in individually rational agents’ coalitions? Models and fairness measures." Computer Science and Information Systems, no. 00 (2023): 75. http://dx.doi.org/10.2298/csis230119075l.

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An individually rational agent will participate in a multiagent coalition if the participation, given available information and knowledge, brings a payoff that is at least as high as the one achieved by not participating. Since agents? performance and skills may vary from task to task, the decisions about individual agent-task assignment will determine the overall performance of the coalition. Maximising the efficiency of the one-on-one assignment of tasks to agents corresponds to the conventional linear sum assignment problem, which considers efficiency as the sum of the costs or benefits of individual agent-task assignments obtained by the coalition as a whole. This approach may be unfair since it does not explicitly consider fairness and, thus, is unsuitable for individually rational agents? coalitions. In this paper, we propose two new assignment models that balance efficiency and fairness in task assignment and study the utilitarian, egalitarian, and Nash social welfare for task assignment in individually rational agents? coalitions. Since fairness is a relatively abstract term that can be difficult to quantify, we propose three new fairness measures based on equity and equality and use them to compare the newly proposed models. Through functional examples, we show that a reasonable trade-off between efficiency and fairness in task assignment is possible through the use of the proposed models.

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Li, Yunhui, Liang Chang, Long Li, Xuguang Bao, and Tianlong Gu. "TASC-MADM: Task Assignment in Spatial Crowdsourcing Based on Multiattribute Decision-Making." Security and Communication Networks 2021 (August 20, 2021): 1–14. http://dx.doi.org/10.1155/2021/5448397.

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The methodology, formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desired objectives, is the most fundamental challenge in spatial crowdsourcing. Many task assignment approaches have been proposed to improve the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel cost. However, these approaches have two shortcomings: (1) these approaches are commonly based on the attributes influencing the result of task assignment. However, different tasks may have different preferences for individual attributes; (2) the performance and efficiency of these approaches are expected to be improved further. To address the above issues, we proposed a task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), with the dual objectives of improving the performance as well as the efficiency. Specifically, the proposed approach jointly considers the attributes on the quality of the worker and the distance between the worker and the task, as well as the influence differences caused by the task’s attribute preference. Furthermore, it can be extended flexibly to scenarios with more attributes. We tested the proposed approach in a real-world dataset and a synthetic dataset. The proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset; the TASC-MADM approach yields better performance than the other two algorithms in the task assignment rate and the CPU cost.

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Li, Xiang, Yan Zhao, Xiaofang Zhou, and Kai Zheng. "Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing." Data Science and Engineering 5, no. 4 (September 15, 2020): 375–90. http://dx.doi.org/10.1007/s41019-020-00142-0.

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Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely.

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Mir, Shantona, and Sukanto Roy. "Assignment Again! Investigating the Psychological Perspectives of Assignment and Assignment Deadlines among Tertiary Level Students." English Language Teaching Perspectives 9, no. 1-2 (August 13, 2024): 117–30. http://dx.doi.org/10.3126/eltp.v9i1-2.68725.

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This paper aims to study university students' psychological perspectives on assignment deadlines. The purpose of this study is to investigate the issues faced by students before assignment submission and their relation to the education system, programme of study, personalities of teachers, and the importance of assignments. All in all, this paper delves into students' psychological barriers regarding assignments and the problems teachers face while assigning assignments. A mixed method is applied to conduct the research. The quantitative data has been collected using a survey among students from 8 different universities, and the qualitative data has employed interviews with teachers from 2 different universities. Procrastination, task management, pressure, and stress have been found to play the greatest role in facilitating the obstacles students face while completing an academic task. From the collected data, it is also recorded that students take into account multiple factors when it comes to assignments and their deadlines, i.e., not enough practical knowledge gain, the teaching style of the teacher, length of the assignment, time allocation, methods used in different programme of study, lack of interest in a course, demotivation, discouragement, and hard courses. A common view among the interviewees is that teachers’ lack of exposure to technology and deficient knowledge of teaching may be the reasons that students develop such attitudes toward assignments. In addition, this paper also addresses feasible solutions and recommendations to this very issue for the betterment of students and enhancing the education system.

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Li, Xin, Wenyang Gan, Pang Wen, and Daqi Zhu. "Multi-AUV Kinematic Task Assignment Based on Self-Organizing Map Neural Network and Dubins Path Generator." Sensors 24, no. 19 (September 30, 2024): 6345. http://dx.doi.org/10.3390/s24196345.

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To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV’s yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.

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Zhao, Yan, Jinfu Xia, Guanfeng Liu, Han Su, Defu Lian, Shuo Shang, and Kai Zheng. "Preference-Aware Task Assignment in Spatial Crowdsourcing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2629–36. http://dx.doi.org/10.1609/aaai.v33i01.33012629.

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With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

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Rozprawy doktorskie na temat "Task assignment"

Manoharan, Sathiamoorthy. "Task assignment in parallel processor systems." Thesis, University of Edinburgh, 1993. http://hdl.handle.net/1842/6568.

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A generic object-oriented simulation platform is developed in order to conduct experiments on the performance of assignment schemes. The simulation platform, called Genesis, is generic in the sense that it can model the key parameters that describe a parallel system: the architecture, the program, the assignment scheme and the message routing strategy. Genesis uses as its basis a sound architectural representation scheme developed in the thesis. The thesis reports results from a number of experiments assessing the performance of assignment schemes using Genesis. The comparison results indicate that the new assignment scheme proposed in this thesis is a promising alternative to the work-greedy assignment schemes. The proposed scheme has a time-complexity less than those of the work-greedy schemes and achieves an average performance better than, or comparable to, those of the work-greedy schemes. To generate an assignment, some parameters describing the program model will be required. In many cases, accurate estimation of these parameters is hard. It is thought that inaccuracies in the estimation would lead to poor assignments. The thesis investigates this speculation and presents experimental evidence that shows such inaccuracies do not greatly affect the quality of the assignments.

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Monori, Akos. "Task assignment optimization in SAP Extended WarehouseManagement." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3598.

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Nowadays in the world of mass consumption there is big demand for distributioncenters of bigger size. Managing such a center is a very complex and difficult taskregarding to the different processes and factors in a usual warehouse when we want tominimize the labor costs. Most of the workers’ working time is spent with travelingbetween source and destination points which cause deadheading. Even if a worker knowsthe structure of a warehouse well and because of that he or she can find the shortest pathbetween two points, it is still not guaranteed that there won’t be long traveling timebetween the locations of two consecutive tasks. We need optimal assignments betweentasks and workers.In the scientific literature Generalized Assignment Problem (GAP) is a wellknownproblem which deals with the assignment of m workers to n tasks consideringseveral constraints. The primary purpose of my thesis project was to choose a heuristics(genetic algorithm, tabu search or ant colony optimization) to be implemented into SAPExtended Warehouse Management (SAP EWM) by with task assignment will be moreeffective between tasks and resources.After system analysis I had to realize that due different constraints and businessdemands only 1:1 assingments are allowed in SAP EWM. Because of that I had to use adifferent and simpler approach – instead of the introduced heuristics – which could gainbetter assignments during the test phase in several cases. In the thesis I described indetails what ware the most important questions and problems which emerged during theplanning of my optimized assignment method.

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Brunet, Luc (Luc P. V. ). "Consensus-based auctions for decentralized task assignment." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44926.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 137-147). This thesis addresses the decentralized task assignment problem in cooperative autonomous search and track missions by presenting the Consensus-Based class of assignment algorithms. These algorithm make use of information consensus routines to converge on the assignment rather than the situational awareness of the fleet. A market-based approach is used as the mechanism for task selection, while the novel consensus stage of the algorithms allow for fast distributed conflict resolution. Three separate algorithms belonging to the Consensus-Based class of assignment strategies will be presented. The first is the Consensus-Based Auction Algorithm (CBAA), which is a single assignment auction strategy that is shown to be bounded within 50% of the optimal solution, while an upper-bound on convergence is presented. Two multi-assignment algorithms are then presented as extensions of the CBAA. The iterative CBAA executes the single assignment algorithm multiple times in order to build an assignment with multiple tasks. The second algorithm is the more general Consensus-Based Bundle Algorithm (CBBA) in which agents build a candidate bundle of tasks and bid on each task individually based on the improvement in score achieved by adding it to the bundle. Both algorithms are shown to be lower bounded by 50% optimality, while convergence bounds are derived based on the network topology. Numerical results show that the bundle algorithm performs much better than the iterative approach while providing faster convergence times. It is also compared with the Prim Allocation (PA) auction algorithm where it is shown to exhibit much faster convergence times and give better assignments. The CBBA is also implemented in the CSAT simulation test-bed developed by Aurora Flight Sciences in conjunction with MIT, and shown to produce faster response times and better tracking performance than the currently used RDTA algorithm. by Luc Brunet. S.M.

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Lavoie, Marco Carleton University Dissertation Information and Systems Science. "Task assignment in a DSP multiprocessor environment." Ottawa, 1990.

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Alighanbari, Mehdi 1976. "Robust and decentralized task assignment algorithms for UAVs." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42177.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007. Includes bibliographical references (p. 149-158). This thesis investigates the problem of decentralized task assignment for a fleet of UAVs. The main objectives of this work are to improve the robustness to noise and uncertainties in the environment and improve the scalability of standard centralized planning systems, which are typically not practical for large teams. The main contributions of the thesis are in three areas related to distributed planning: information consensus, decentralized conflict-free assignment, and robust assignment. Information sharing is a vital part of many decentralized planning algorithms. A previously proposed decentralized consensus algorithm uses the well-known Kalman filtering approach to develop the Kalman Consensus Algorithm (KCA), which incorporates the certainty of each agent about its information in the update procedure. It is shown in this thesis that although this algorithm converges for general form of network structures, the desired consensus value is only achieved for very special networks. We then present an extension of the KCA and show, with numerical examples and analytical proofs, that this new algorithm converges to the desired consensus value for very general communication networks. Two decentralized task assignment algorithms are presented that can be used to achieve a good performance for a wide range of communication networks. These include the Robust Decentralized Task Assignment (RDTA) algorithm, which is shown to be robust to inconsistency of information across the team and ensures that the resulting decentralized plan is conflict-free. A new auction-based task assignment algorithm is also developed to perform assignment in a completely decentralized manner where each UAV is only allowed to communicate with its neighboring UAVs, and there is no relaying of information. (cont.) In this algorithm, only necessary information is communicated, which makes this method communication-efficient and well-suited for low bandwidth communication networks. The thesis also presents a technique that improves the robustness of the UAV task assignment algorithm to sensor noise and uncertainty about the environment. Previous work has demonstrated that an extended version of a simple robustness algorithm in the literature is as effective as more complex techniques, but significantly easier to implement, and thus is well suited for real-time implementation. We have also developed a Filter-Embedded Task assignment (FETA) algorithm for accounting for changes in situational awareness during replanning. Our approach to mitigate "churning" is unique in that the coefficient weights that penalize changes in the assignment are tuned online based on previous plan changes. This enables the planner to explicitly show filtering properties and to reject noise with desired frequencies. This thesis synergistically combines the robust and adaptive approaches to develop a fully integrated solution to the UAV task planning problem. The resulting algorithm, called the Robust Filter Embedded Task Assignment (RFETA), is shown to hedge against the uncertainty in the optimization data and to mitigate the effect of churning while replanning with new information. The algorithm demonstrates the desired robustness and filtering behavior, which yields superior performance to using robustness or FETA alone, and is well suited for real-time implementation. The algorithms and theorems developed in this thesis address important aspects of the UAV task assignment problem. The proposed algorithms demonstrate improved performance and robustness when compared with benchmarks and they take us much closer to the point where they are ready to be transitioned to real missions. by Mehdi Alighanbari. Ph.D.

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Tovstokorenko, O. Y., R. O. Gamzayev, and M. V. Tkachuk. "Case-Based Recommender for intelligent task assignment in software development." Thesis, NTU "KhPI", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38049.

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Luo, Lingzhi. "Distributed Algorithm Design for Constrained Multi-robot Task Assignment." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/426.

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The task assignment problem is one of the fundamental combinatorial optimization problems. It has been extensively studied in operation research, management science, computer science and robotics. Task assignment problems arise in various applications of multi-robot systems (MRS), such as environmental monitoring, disaster response, extraterrestrial exploration, sensing data collection and collaborative autonomous manufacturing. In these MRS applications, there are realistic constraints on robots and tasks that must be taken into account both from the modeling perspective and the algorithmic perspective. From the modeling aspect, such constraints include (a) Task group constraints: where tasks form disjoint groups and each robot can be assigned to at most one task in each group. One example of the group constraints comes from tightly-coupled tasks, where multiple micro tasks form one tightly-coupled macro task and need multiple robots to perform each simultaneously. (b) Task deadline constraints: where tasks must be assigned to meet their deadlines. (c) Dynamically-arising tasks: where tasks arrive dynamically and the payoffs of future tasks are unknown. Such tasks arise in scenarios like searchrescue, where new victims are found dynamically. (d) Robot budget constraints: where the number of tasks each robot can perform is bounded according to the resource it possesses (e.g., energy). From the solution aspect, there is often a need for decentralized solution that are implemented on individual robots, especially when no powerful centralized controller exists or when the system needs to avoid single-point failure or be adaptive to environmental changes. Most existing algorithms either do not consider the above constraints in problem modeling, are centralized or do not provide formal performance guarantees. In this thesis, I propose methods to address these issues for two classes of problems, namely, the constrained linear assignment problem and constrained generalized assignment problem. Constrained linear assignment problem belongs to P, while constrained generalized assignment problem is NP-hard. I develop decomposition-based distributed auction algorithms with performance guarantees for both problem classes. The multi-robot assignment problem is decomposed into an optimization problem for each robot and each robot iteratively solving its own optimization problem leads to a provably good solution to the overall problem. For constrained linear assignment problem, my approaches provides an almost optimal solution. For constrained generalized assignment problem, I present a distributed algorithm that provides a solution within a constant factor of the optimal solution. I also study the online version of the task allocation problem with task group constraints. For the online problem, I prove that a repeated greedy version of my algorithm gives solution with constant factor competitive ratio. I include simulation results to evaluate the average-case performance of the proposed algorithms. I also include results on multi-robot cooperative package transport to illustrate the approach.

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Ottensmeyer, Mark Peter. "Telerobotic surgery : feedback time delay effects on task assignment." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10972.

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Holmes, Carol Anne. "Equipment selection and task assignment for multiproduct assembly system design." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14936.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1987. MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. Bibliography: leaves 81-82. by Carol Anne Holmes. Ph.D.

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Alighanbari, Mehdi 1976. "Task assignment algorithms for teams of UAVs in dynamic environments." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17754.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Operations Research Center, 2004. Includes bibliographical references (p. 113-118). For many vehicles, obstacles, and targets, coordination of a fleet of Unmanned Aerial Vehicles (UAVs) is a very complicated optimization problem, and the computation time typically increases very rapidly with the problem size. Previous research proposed an approach to decompose this large problem into task assignment and trajectory problems, while capturing key features of the coupling between them. This enabled the control architecture to solve an assignment problem first to determine a sequence of waypoints for each vehicle to visit, and then concentrate on designing paths to visit these pre-assigned waypoints. Although this approach greatly simplifies the problem, the task assignment optimization was still too slow for real-time UAV operations. This thesis presents a new approach to the task assignment problem that is much better suited for replanning in a dynamic battlefield. The approach, called the Receding Horizon Task Assignment (RHTA) algorithm, is shown to achieve near-optimal performance with computational times that are feasible for real-time implementation. Further, this thesis extends the RHTA algorithm to account for the risk, noise, and uncertainty typically associated with the UAV environment. This work also provides new insights on the distinction between UAV coordination and cooperation. The benefits of these improvements to the UAV task assignment algorithms are demonstrated in several simulations and on two hardware platforms. by Mehdi Alighanbari. S.M.

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Książki na temat "Task assignment"

Wassenhove, Luk N. van. A set partitioning heuristic for the generalized assignment problem. Fontainebleau: INSEAD, 1991.

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Teays, Terry. Blazhko effect: Final report for contract NAS5-31840, task assignment 5788. [Washington, DC: National Aeronautics and Space Administration, 1996.

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DiMarco, John D. Network-based heuristics for task assignment in large-scale distributed systems. Toronto: University of Toronto, Dept. of Computer Science, 1995.

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Dimarco, John D. Network-based heuristics for task assignment in large-scale distributed systems. Ottawa: National Library of Canada, 1995.

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United States. National Aeronautics and Space Administration., ed. Short period Type II Cepheids: Final report for contract NAS5-32081, task assignment 4109. [Washington, DC: National Aeronautics and Space Administration, 1996.

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United States. National Aeronautics and Space Administration., ed. Microwave remote sensing and radar polarization signatures of nuatural fields: Final report under contract NASS 5-30116, task assignment 5183. [Washington, DC: National Aeronautics and Space Administration, 1989.

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Volkov, Vladimir, Yaroslava Bulgakova, and Vera Semiletova. Normal physiology: situational tasks and assignments. ru: INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2162062.

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The textbook is designed to help students work independently while studying the course of normal physiology. The presented tasks and assignments are arranged in the order of study of the material and have varying degrees of difficulty. They are based on both classical and modern experimental data and are accompanied by sample solutions and responses. These materials are convenient to use in preparation for practical and final work, as well as for professional biological and medical Olympiads and quizzes. Meets the requirements of the latest generation of federal state educational standards for higher education. For students of higher educational institutions studying in the field of Medicine, as well as for practitioners interested in the basics of physiology. Thanks to the descriptions and explanations of physiological experiments and entertaining facts, it can be interesting not only for medical professionals, but also for a wider audience of readers.

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Nicol, David. Static assignment of complex stochastic tasks using stochastic majorization. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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United States. General Accounting Office. Office of Information Management and Communications., ed. Mission support project: Analyzing user requirements for assignment management tasks. [Washington, D.C.]: General Accounting Office, 1992.

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Części książek na temat "Task assignment"

Weyns, Danny. "Task Assignment." In Architecture-Based Design of Multi-Agent Systems, 123–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01064-4_6.

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Takahara, Yasuhiko, and Mihajlo Mesarovic. "Task Assignment Coordination." In Organization Structure, 137–54. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0213-1_8.

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Macdonald, Ian, Catherine Burke, and Karl Stewart. "Task Formulation and Assignment." In Systems Leadership, 187–98. Second edition. | Abingdon, Oxon; New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315178486-14.

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Moore, Brandon J., and Kevin M. Passino. "Task Assignment for Mobile Agents." In Cooperative Control of Distributed Multi-Agent Systems, 109–38. Chichester, UK: John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470724200.ch6.

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Macdonald, Ian, Catherine Burke, and Karl Stewart. "Task Assignment, Feedback and Review." In The Work of Management, 234–48. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003459118-22.

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Orleans, Luis Fernando, Carlo Emmanoel de Oliveira, and Pedro Furtado. "Task Assignment on Parallel QoS Systems." In Web Information Systems Engineering – WISE 2007, 543–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76993-4_46.

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Gonçalves, Nelson, and João Sequeira. "Multirobot Task Assignment in Active Surveillance." In Progress in Artificial Intelligence, 310–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04686-5_26.

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Goyal, Rahul, Tushar Sharma, and Ritu Tiwari. "Priority Based Multi Robot Task Assignment." In Lecture Notes in Computer Science, 554–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30976-2_67.

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Liu, Shan Fan, and Mary Lou Soffa. "Parallel task assignment by graph partitioning." In PARLE '92 Parallel Architectures and Languages Europe, 965–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55599-4_144.

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Rochian, Vlad, Cosmin Bonchis, and Ionut Tepeneu. "Worker-to-Task Skill-Based Assignment." In Advanced Information Networking and Applications, 157–68. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28694-0_15.

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Streszczenia konferencji na temat "Task assignment"

Lavaur, Thomas, Déborah Conforto Nedelmann, Corentin Chauffaut, Jérôme Lacan, and Caroline P. C. Chanel. "Verifiable Multi-Agent Multi-Task Assignment." In 2024 IEEE Secure Development Conference (SecDev), 1–12. IEEE, 2024. http://dx.doi.org/10.1109/secdev61143.2024.00006.

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Bhavya, B., Hawraa Ali Sabah, Zainab Failhal lami, Manar Kareem Lafta, Ahmed Read Al-Tameemi, and D. Rajkumar Pillay. "Supervised Learning in Task Assignment Systems." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES), 1–5. IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877511.

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Zhou, Yuhang, Baoheng Zhang, Qingzhong Yan, Xiang Wu, and Yuming Bo. "Task Assignment Algorithm Based on Improved MADDPG." In 2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 326–32. IEEE, 2024. https://doi.org/10.1109/icaice63571.2024.10864003.

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Hajaj, Chen, and Yevgeniy Vorobeychik. "Adversarial Task Assignment." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/526.

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The problem of task assignment to workers is of long-standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, assigning jobs to robots, and crowdsourcing. Extensive research into this problem generally addresses important issues such as uncertainty and incentives. However, the problem of adversarial tampering with the task assignment process has not received as much attention. We are concerned with a particular adversarial setting in task assignment where an attacker may target a set of workers in order to prevent the tasks assigned to these workers from being completed. For the case when all tasks are homogeneous, we provide an efficient algorithm for computing the optimal assignment. When tasks are heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an algorithm for solving it approximately. Our theoretical results are accompanied by extensive simulation results showing the effectiveness of our algorithms.

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Tang, Feilong. "Optimal Complex Task Assignment in Service Crowdsourcing." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/217.

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Existing schemes cannot assign complex tasks to the most suitable workers because they either cannot measure skills quantitatively or do not consider assigning tasks to workers who are the most suitable but unavailable temporarily. In this paper, we investigate how to realize optimal complex task assignment. Firstly, we formulate the multiple-skill based task assignment problem in service crowdsourcing. We then propose a weighted multi-skill tree (WMST) to model multiple skills and their correlations. Next, we propose the acceptance expectation to uniformly measure the probabilities that different categories of workers will accept and complete specified tasks. Finally, we propose an acceptance-expectation-based task assignment (AE-TA) algorithm, which reserves tasks for the most suitable workers even unavailable temporarily. Comprehensive experimental results demonstrate that our WMST model and AE-TA algorithm significantly outperform related proposals.

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Yusuke Morihiro, Toshiyuki Miyamoto, and Sadatoshi Kumagai. "An initial task assignment method for tasks assignment and routing problem." In SICE Annual Conference 2007. IEEE, 2007. http://dx.doi.org/10.1109/sice.2007.4421039.

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Liu, Shuo, Bohan Feng, Dan Yu, and Youyi Bi. "An Integrated Task and Path Planning Approach for Mobile Robots in Smart Factory." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95364.

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Abstract Mobile robots are being widely used in smart manufacturing, and efficient task assignment and path planning for these robots is an area of high interest. In previous studies, task assignment and path planning are usually solved as separate problems, which can result in optimal solutions in their respective fields, but not necessarily optimal as an integrated problem. Meanwhile, precedence constraints exist between sequential processing operations and material delivery tasks in the manufacturing environment. Thus, those planning methods developed for warehousing and logistics may not simply apply to the environment of smart factories. In this paper, we propose an integrated task and path planning approach based on Looking-backward Search Strategy (LSS) and Regret-based Search Strategy (RSS). In the stage of task assignment, the real paths for mobile robots are identified based on the Cooperative A* (CA*) algorithm and the time and energy consumed by mobile robots and machining centers are calculated. Then a greedy strategy working with LSS or RSS is used to search reasonable task assignments in time-series, which can generate a joint optimal solution for both task assignment and path planning. We verify the validity of the proposed approach in a simulated smart factory and the results show that our approach can improve the operation efficiency of the smart factory and save the time and energy consumption effectively.

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Dang, Hung, Tuan Nguyen, and Hien To. "Maximum Complex Task Assignment." In International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2539150.2539243.

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Zhu, Wei, and Rongling Zhou. "Personalized Learning Task Assignment." In 2022 9th International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2022. http://dx.doi.org/10.1109/dsa56465.2022.00155.

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Xia, Jinfu, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang, and Kai Zheng. "Profit-driven Task Assignment in Spatial Crowdsourcing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/265.

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In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i.e., expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.

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Raporty organizacyjne na temat "Task assignment"

Harchol-Balter, Mor. Task Assignment With Unknown Duration. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada368426.

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Basik, Kevin J. Small-Group Leader Assignment: Effects Across Different Degrees of Task Interdependence,. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada327895.

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Shima, Tal, Pantelis Isaiah, and Yoav Gottlieb. Motion Planning and Task Assignment for Unmanned Aerial Vehicles Cooperating with Unattended Ground Sensors. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada619854.

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Schroeder, Bianca, and Mor Harchol-Balter. Evaluation of Task Assignment Policies for Supercomputing Servers: The Case for Load Unbalancing and Fairness. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada377091.

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Trabia, M. B., M. Kiley, J. Cardle, and M. Joseph. Report on task assignment No. 3 for the Waste Package Project; Parts A & B, ASME pressure vessel codes review for waste package application; Part C, Library search for reliability/failure rates data on low temperature low pressure piping, containers, and casks with long design lives. Office of Scientific and Technical Information (OSTI), July 1991. http://dx.doi.org/10.2172/138422.

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Wiedeking, Mathis, and Paraskevi Dimitriou. Updating Photonuclear Data Library and Generating a Reference Database for Photon Strength Functions. IAEA Nuclear Data Section, March 2019. http://dx.doi.org/10.61092/iaea.cbxm-8pve.

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A summary is given of the 3rd Research Coordination Meeting (RCM) of the IAEA Co-ordinated Research Project (CRP) on Updating the Photonuclear Data Library and Generating a Reference Database for Photon Strength Functions. Participants presented their progress reports, reviewed the list of actions assigned at the previous meeting, agreed on the remaining task assignments necessary to achieve the goals of the CRP and agreed on the content and type of publication for the final CRP report. A summary of the presentations and discussions is presented in this report.

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Zhylenko, Tetyana I. Auto Checker of Higher Mathematics - an element of mobile cloud education. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3895.

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We analyzed the main cloud services in the article. We also described the main contribution of mobile cloud technology to education. The article presents the author’s development from the field of mobile cloud education in higher mathematics. The design architecture of this application is described in detail: QR generator and scanner, authorization, sending tasks. Block diagrams and images are presented that clearly demonstrate the operation of the application. We showed an example of solving the integral from the section of integral calculus for higher mathematics and showed how to download the answer in the form of a QR code and find out whether it is correct or incorrect (this can be seen by the color on the smart phone screen). It is shown how this technology helps the teacher save time for checking assignments completed by students. This confirms its effectiveness. Such an application provides students and teachers with the ability to store and process data on a cloud computing platform.

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García Marrugo, Alexandra I., Katherine Olston, Josh Aarts, Dashiell Moore, and Syed Kaliyadan. SCANA: Supporting students’ academic language development at The University of Sydney. Journal of the Australian and New Zealand Student Services Association, June 2023. http://dx.doi.org/10.30688/janzssa.2023-2-01.

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In 2021, the Learning Hub at The University of Sydney launched the Student Communication and Needs Analysis (SCANA). This program of support consists of a screening language task and associated support interventions in first year units of study (UoS). The self-marking online screening tool developed by the Language Testing Research Centre at The University of Melbourne classifies students into three bands, with Band 1 identifying students at risk of academic failure due to insufficient language proficiency. All students in selected UoS are encouraged to take SCANA and offered academic language support according to their needs. Students identified in Band 1 are advised to attend discipline-specific support targeting the language issues associated with written assignments. These students are also informed about other offerings, such as one-on-one consultations, generic academic workshops, peer-facilitated programs, and self-access resources. Students in Bands 2 and 3 are also offered options according to their level. The results from Semester 1 2022 showed that students identified in Band 1 who attended at least two support workshops obtained, on average, 12 more points in their final grade and were up to five times less likely to fail than those in Band 1 who did not attend any workshops. These promising results have motivated faculty to expand the program from seven UoS in 2021 to 32 UoS in 2023.

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Kantar. Testing the impact of overt and covert ordering interventions on sustainable consumption choices: a randomised controlled trial. Food Standards Agency, February 2023. http://dx.doi.org/10.46756/sci.fsa.gtc137.

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The production, transportation, storage, and waste of food products have a significant impact on the environment. The UK government’s National Food Strategy (Dimbleby, 2021), an independent review of England’s entire food system, recommends that multiple interventions are required for healthy and sustainable diets to create a long-term shift in our food culture. Online supermarkets constitute an increasingly large share of grocery shopping, 12.6% of grocery sales were made online in March 2022 compared with just 8.0% three years ago (McKevitt, 2022). Therefore, it is important to understand how interventions in online shopping environments affect consumer choices in relation to the sustainability of products. We investigated whether a specific choice architecture intervention – displaying products in an ascending order of their carbon footprint – in an online supermarket environment can shift consumer choices towards more sustainable options compared to when products are randomly ordered. We also examined whether the effect of the ordering intervention differs when the ordering is overt, where information about the ordering is explicit, compared to when it is covert and participants are not told about the ordering. We conducted a three-arm parallel-group randomised trial using a sample of 1842 online panel participants from England, Wales, and Northern Ireland, who had previously shopped online, representative of age, gender and ethnicity. Participants completed a shopping task for a meal for two, choosing one product from each of the six prespecified product categories in a simulated online supermarket environment. Six products were shown in a vertical list on each product category page. Products were randomly ordered for the control arm, whereas the products were ordered by carbon footprint in the covert and overt ordering arms. In addition, in the overt ordering arm, the following statement was displayed in a box at the top of each product page: “The products on this page have been ordered from the most environmentally sustainable to the least environmentally sustainable. This is to make it easier for you to choose a more sustainable product if you wish.”. Participants were randomly allocated to one of the three arms with equal probability. The primary outcome was whether one of the three most sustainable products was chosen in each product category. Participants were blinded to the objective of the trial and experimental conditions, other than their own, until the debrief at the end, and our analyst was blinded to the assignment of experimental arms during initial data analyses. The key findings are as follows: • Covert ordering did not affect consumer choices, because there was no effect of a product's position in the list on choice, contrary to evidence in the literature. • Choices seemed to be mainly driven by prior preferences, suggesting preferences for grocery products might be too ingrained to be changed by subtle rearrangements of choice architecture like the ordering interventions. • There was no significant difference between the effects of covert and overt ordering interventions. • Only a small proportion of people correctly identified that the products were ordered by sustainability when the information was explicitly given, highlighting the difficulty of conveying information in the online shopping environment. There was no effect of the covert ordering intervention on the probability of choosing more sustainable products versus less sustainable products, compared with the control arm (OR = 0.97, 95% CI 0.88-1.07, p-value = 0.533). Furthermore, our hypothesis that the effects of the covert ordering intervention and overt ordering intervention do not differ could not be rejected (p-value = 0.594). Contrary to our assumptions, analysis of the control condition showed that the positioning of products had no effect on choices, which may explain why re-ordering products also had no effect. In the overt condition, only 19.5% of people correctly answered that the products were ordered according to sustainability in a follow-up question, suggesting that they didn’t notice the statement. The results suggest that preferences for grocery products might be too ingrained to be changed by subtle rearrangements of choice architecture like the ordering interventions. Choices seemed to be mainly driven by prior preferences over the options, rather than by their position on a list, and the majority of participants did not notice a statement about the sustainability ordering in the overt-ordering arm, despite it being pinned to the top of every page. Nevertheless, the results could be specific to the details of the trial design in this study; therefore, whilst we did not find a significant effect we cannot rule out the potential for ordering interventions to cause an effect. In particular, it would be worth determining how to get participants to pay more attention to the statement about the sustainability ordering and then investigating how people respond to it. As this is the first study looking at the effects of product ordering interventions based on the environmental impact in an online shopping environment, more research is needed to strengthen the evidence base and our understanding of whether, and in what context, such interventions could work.

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