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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
A Case Study
Funding Information: Inês A. Ferreira acknowledges the financial support from Fundação para a Ciência e Tecnologia (FCT) for funding PhD Grant-REF: SFRH/BD/145448/2019. Helena Carvalho acknowledges Fundação para a Ciência e Tecnologia (FCT) for its financial support through the project UIDB/00667/2020 (UNIDEMI). Carina Pimentel acknowledges Fundação para a Ciência e Tecnologia (FCT) within the R&D Units Project Scope UIDB/00319/2020. Publisher Copyright: © 2023 by the authors.The number of variants of the vehicle routing problem (VRP) has grown rapidly in the last decades. Among these, VRPs with time window constraints are among the most studied ones. However, the literature regarding VRPs that concerns the delivery and installation of products is scarce. The main aim of this study was to propose a heuristic approach for the route planning process of a company whose focus is on furniture delivery and assembly and, thus, contributing to the research around the Delivery and Installation Routing Problem. The case study method was used, and two scenarios were compared: the current scenario (showing the routes created by the company worker); and the future scenario (representing the routes created by the heuristic). Results show that the proposed heuristic approach provided a feasible solution to the problem, allowing it to affect customers and teams without compromising the teams’ competencies and respecting all constraints.publishersversionpublishe
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints
This article addresses the dynamic multi-skill workforce scheduling and
routing problem with time windows and synchronization constraints (DWSRP-TW-SC)
inherent in the on-demand home services sector. In this problem, new service
requests (tasks) emerge in real-time, necessitating a constant reevaluation of
service team task plans. This reevaluation involves maintaining a portion of
the plan unaltered, ensuring team-task compatibility, addressing task
priorities, and managing synchronization when task demands exceed a team's
capabilities. To address the problem, we introduce a real-time optimization
framework triggered upon the arrival of new tasks or the elapse of a set time.
This framework redesigns the routes of teams with the goal of minimizing the
cumulative weighted throughput time for all tasks. For the route redesign phase
of this framework, we develop both a mathematical model and an Adaptive Large
Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational
study to assess the performance of our proposed ALNS-based reoptimization
framework and to examine the impact of reoptimization strategies, frozen period
lengths, and varying degrees of dynamism. Our contributions provide practical
insights and solutions for effective dynamic workforce management in on-demand
home services
Dynamic planning of mobile service teams’ mission subject to orders uncertainty constraints
This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods
Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms
This paper reports on the results of the VeRoLog Solver Challenge 2016–2017: the third solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics Optimization. The authors are the winners of second and third places, combined with members of the challenge organizing committee. The problem central to the challenge was a rich VRP: expensive and, therefore, scarce equipment was to be redistributed over customer locations within time windows. The difficulty was in creating combinations of pickups and deliveries that reduce the amount of equipment needed to execute the schedule, as well as the lengths of the routes and the number of vehicles used. This paper gives a description of the solution methods of the above-mentioned participants. The second place method involves sequences of 22 low level heuristics: each of these heuristics is associated with a transition probability to move to another low level heuristic. A randomly drawn sequence of these heuristics is applied to an initial solution, after which the probabilities are updated depending on whether or not this sequence improved the objective value, hence increasing the chance of selecting the sequences that generate improved solutions. The third place method decomposes the problem into two independent parts: first, it schedules the delivery days for all requests using a genetic algorithm. Each schedule in the genetic algorithm is evaluated by estimating its cost using a deterministic routing algorithm that constructs feasible routes for each day. After spending 80 percent of time in this phase, the last 20 percent of the computation time is spent on Variable Neighborhood Descent to further improve the routes found by the deterministic routing algorithm. This article finishes with an in-depth comparison of the results of the two approaches
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