Deadline TSP

Farbstein, Boaz; Levin, Asaf

doi:10.1016/j.tcs.2018.11.016

Cited by 3 publications

(7 citation statements)

References 4 publications

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“…The time complexity of this approximation algorithm is O(D 2 max βn 10 ), where D max is the maximum deadline, β is the total price of the graph, and n is the number of vertices. Farbstein et al [10] solve the deadline-TSP with contiguous deadlines. They propose an O 1 + , α 1+ -approximation algorithm.…”

Section: The Deadline-tspsmentioning

confidence: 99%

“…Constraint (8) indicates that the principal agent serves at most one vertex at any time, and constraint (9) indicates that the principal agent serves a task at a vertex except initial vertex v 0 at most one time. Constraint (10) states that the moving distance of the principal agent from v i to v j is not less than the minimum distance between them. Constraints ( 11)-( 14) link z i,j and x i,t , x j,t .…”

Section: Problem Formalizationmentioning

confidence: 99%

“…Let CP denote the deadline-TSP [10]. Then, a reduction from the CP to an instance of the SP is provided.…”

Section: Hybrid Combination Algorithmmentioning

confidence: 99%

“…If and only if the optimum of the CP can be found in polynomial time can the optimum of the corresponding SP with η = 0 be found in polynomial time. However, the CP is proved to be N P-hard [10]. Therefore, the principal-assistant collaboration problem with deadlines in the 1-principal-1-assistant scenario is N P-hard.…”

Section: Hybrid Combination Algorithmmentioning

confidence: 99%

“…If and only if the optimal solution of the deadline-TSP can be found in polynomial time, the optimal solution of the principal-assistant collaboration problem with deadlines can be found in polynomial time. However, Farbstein and Levin [10] proved that the deadline-TSP is N P-hard in the graph metrics or tree metrics, and thus the principal-assistant collaboration problem with deadlines is N P-hard. The challenges of the principal-assistant collaboration problem with deadlines involve routing and cooperation [4,7].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Zhou

Xing

2021

Algorithms

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Principal–assistant agent teams are often employed to solve tasks in multiagent collaboration systems. Assistant agents attached to the principal agents are more flexible for task execution and can assist them to complete tasks with complex constraints. However, how to employ principal–assistant agent teams to execute time-critical tasks considering the dependency between agents and the constraints among tasks is still a challenge so far. In this paper, we investigate the principal–assistant collaboration problem with deadlines, which is to allocate tasks to suitable principal–assistant teams and construct routes satisfying the temporal constraints. Two cases are considered in this paper, including single principal–assistant teams and multiple principal–assistant teams. The former is formally formulated in an arc-based integer linear programming model. We develop a hybrid combination algorithm for adapting larger scales, the idea of which is to find an optimal combination of partial routes generated by heuristic methods. The latter is defined in a path-based integer linear programming model, and a branch-and-price-based (BP-based) algorithm is proposed that introduces the number of assistant-accessible tasks surrounding a task to guide the route construction. Experimental results validate that the hybrid combination algorithm and the BP-based algorithm are superior to the benchmarks in terms of the number of served tasks and the running time.

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Section: The Deadline-tspsmentioning

confidence: 99%

Section: Problem Formalizationmentioning

confidence: 99%

“…Let CP denote the deadline-TSP [10]. Then, a reduction from the CP to an instance of the SP is provided.…”

Section: Hybrid Combination Algorithmmentioning

confidence: 99%

Section: Hybrid Combination Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Zhou

Xing

2021

Algorithms

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Delivery Route Optimization with automated vehicle in smart urban environment

Luo

Ding

et al. 2020

Theoretical Computer Science

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Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

D’Emidio,

Delfaraz,

Di Stefano

et al. 2024

Applied Sciences

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The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L’Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows.

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Deadline TSP

Cited by 3 publications

References 4 publications

Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Delivery Route Optimization with automated vehicle in smart urban environment

Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment

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