Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Distributed Services with Foreseen and Unforeseen Tasks: The Mobile Re-allocation Problem

In this paper we deal with a common problem found in the operations of security and preventive/corrective maintenance services: that of routing a number of mobile resources to serve foreseen and unforeseen tasks during a shift. We define the (Mobile Re-Allocation Problem) MRAP as the problem of devising a routing strategy to maximize the expected weighted number of tasks served on time. For obtaining a solution to the MRAP, we propose to solve successively a multi-objective optimization problem called the stochastic Team Orienteering Problem with Multiple Time Windows (s-TOP-MTW) so as to consider information about known tasks and the arrival process of new unforeseen tasks. Solving successively the s-TOP-MTW we find that considering information about the arrival process of new unforeseen tasks may aid in maximizing the expected proportion of tasks accomplished on time.location;reliability;routing;distributed services

A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all of the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu-search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.Comment: 18 page

Determining reliable solutions for the team orienteering problem with probabilistic delays

In the team orienteering problem, a fixed fleet of vehicles departs from an origin depot towards a destination, and each vehicle has to visit nodes along its route in order to collect rewards. Typically, the maximum distance that each vehicle can cover is limited. Alternatively, there is a threshold for the maximum time a vehicle can employ before reaching its destination. Due to this driving range constraint, not all potential nodes offering rewards can be visited. Hence, the typical goal is to maximize the total reward collected without exceeding the vehicle’s capacity. The TOP can be used to model operations related to fleets of unmanned aerial vehicles, road electric vehicles with limited driving range, or ride-sharing operations in which the vehicle has to reach its destination on or before a certain deadline. However, in some realistic scenarios, travel times are better modeled as random variables, which introduce additional challenges into the problem. This paper analyzes a stochastic version of the team orienteering problem in which random delays are considered. Being a stochastic environment, we are interested in generating solutions with a high expected reward that, at the same time, are highly reliable (i.e., offer a high probability of not suffering any route delay larger than a user-defined threshold). In order to tackle this stochastic optimization problem, which contains a probabilistic constraint on the random delays, we propose an extended simheuristic algorithm that also employs concepts from reliability analysis.This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033), the Barcelona City Council and Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020–2023 (grant 21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).Peer ReviewedPostprint (published version

Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays

[EN] In the team orienteering problem, a fixed fleet of vehicles departs from an origin depot towards a destination, and each vehicle has to visit nodes along its route in order to collect rewards. Typically, the maximum distance that each vehicle can cover is limited. Alternatively, there is a threshold for the maximum time a vehicle can employ before reaching its destination. Due to this driving range constraint, not all potential nodes offering rewards can be visited. Hence, the typical goal is to maximize the total reward collected without exceeding the vehicle's capacity. The TOP can be used to model operations related to fleets of unmanned aerial vehicles, road electric vehicles with limited driving range, or ride-sharing operations in which the vehicle has to reach its destination on or before a certain deadline. However, in some realistic scenarios, travel times are better modeled as random variables, which introduce additional challenges into the problem. This paper analyzes a stochastic version of the team orienteering problem in which random delays are considered. Being a stochastic environment, we are interested in generating solutions with a high expected reward that, at the same time, are highly reliable (i.e., offer a high probability of not suffering any route delay larger than a user-defined threshold). In order to tackle this stochastic optimization problem, which contains a probabilistic constraint on the random delays, we propose an extended simheuristic algorithm that also employs concepts from reliability analysis.This work has been partially funded by the Spanish Ministry of Science (PID2019-111100RBC21-C22/AEI/10.13039/501100011033), the Barcelona City Council and Fundacio "la Caixa" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001), and the Generalitat Valenciana (PROMETEO/2021/065).Herrera, EM.; Panadero, J.; Carracedo-Garnateo, P.; Juan-Pérez, ÁA.; Pérez Bernabeu, E. (2022). Determining Reliable Solutions for the Team Orienteering Problem with Probabilistic Delays. Mathematics. 10(20). https://doi.org/10.3390/math10203788102

The Orienteering Problem under Uncertainty Stochastic Programming and Robust Optimization compared

The Orienteering Problem (OP) is a generalization of the well-known traveling salesman problem and has many interesting applications in logistics, tourism and defense. To reflect real-life situations, we focus on an uncertain variant of the OP. Two main approaches that deal with optimization under uncertainty are stochastic programming and robust optimization. We will explore the potentialities and bottlenecks of these two approaches applied to the uncertain OP. We will compare the known robust approach for the uncertain OP (the robust orienteering problem) to the new stochastic programming counterpart (the two-stage orienteering problem). The application of both approaches will be explored in terms of their suitability in practice