651 research outputs found
Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System
The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an
extension of the well-known Vehicle Routing Problem (VRP), which takes into
account the dynamic nature of the problem. This aspect requires the vehicle
routes to be updated in an ongoing manner as new customer requests arrive in
the system and must be incorporated into an evolving schedule during the
working day. Besides the vehicle capacity constraint involved in the classical
VRP, DVRPTW considers in addition time windows, which are able to better
capture real-world situations. Despite this, so far, few studies have focused
on tackling this problem of greater practical importance. To this end, this
study devises for the resolution of DVRPTW, an ant colony optimization based
algorithm, which resorts to a joint solution construction mechanism, able to
construct in parallel the vehicle routes. This method is coupled with a local
search procedure, aimed to further improve the solutions built by ants, and
with an insertion heuristics, which tries to reduce the number of vehicles used
to service the available customers. The experiments indicate that the proposed
algorithm is competitive and effective, and on DVRPTW instances with a higher
dynamicity level, it is able to yield better results compared to existing
ant-based approaches.Comment: 10 pages, 2 figure
Dynamic vehicle routing with time windows in theory and practice
The vehicle routing problem is a classical combinatorial optimization
problem. This work is about a variant of the vehicle routing problem
with dynamically changing orders and time windows. In real-world
applications often the demands change during operation time. New orders
occur and others are canceled. In this case new schedules need to be
generated on-the-fly. Online optimization algorithms for dynamical
vehicle routing address this problem but so far they do not consider
time windows. Moreover, to match the scenarios found in real-world
problems adaptations of benchmarks are required. In this paper, a
practical problem is modeled based on the procedure of daily routing of a
delivery company. New orders by customers are introduced dynamically
during the working day and need to be integrated into the schedule. A
multiple ant colony algorithm combined with powerful local search
procedures is proposed to solve the dynamic vehicle routing problem with
time windows. The performance is tested on a new benchmark based on
simulations of a working day. The problems are taken from Solomon’s
benchmarks but a certain percentage of the orders are only revealed to
the algorithm during operation time. Different versions of the MACS
algorithm are tested and a high performing variant is identified.
Finally, the algorithm is tested in situ: In a field study, the
algorithm schedules a fleet of cars for a surveillance company. We
compare the performance of the algorithm to that of the procedure used
by the company and we summarize insights gained from the implementation
of the real-world study. The results show that the multiple ant colony
algorithm can get a much better solution on the academic benchmark
problem and also can be integrated in a real-world environment
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Resource constrained routing and scheduling: Review and research prospects
In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects
New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201
A spatial decomposition based math-heuristic approach to the asset protection problem
This paper addresses the highly critical task of planning asset protection activities during uncontrollable wildfires known in the literature as the Asset Protection Problem (APP). In the APP each asset requires a protective service to be performed by a set of emergency response vehicles within a specific time period defined by the spread of fire. We propose a new spatial decomposition based math-heuristic approach for the solution of large-scale APPs. The heuristic exploits the property that time windows are geographically correlated as fire spreads across a landscape. Thus an appropriate division of the landscape allows the problem to be decomposed into smaller more tractable sub-problems. The main challenge then is to minimise the difference between the final locations of vehicles from one division to the optimal starting locations of the next division. The performance of the proposed approach is tested on a set of benchmark instances from the literature and compared to the most recent Adaptive Large Neighborhood Search (ALNS) algorithm developed for the APP. The results show that our proposed solution approach outperforms the ALNS algorithm on all instances with comparable computation time. We also see a trend with the margin of out-performance becoming more significant as the problems become larger
Route Planning for Long-Term Robotics Missions
Many future robotic applications such as the operation in large uncertain environment depend on a more autonomous robot. The robotics long term autonomy presents challenges on how to plan and schedule goal locations across multiple days of mission duration. This is an NP-hard problem that is infeasible to solve for an optimal solution due to the large number of vertices to visit. In some cases the robot hardware constraints also adds the requirement to return to a charging station multiple times in a long term mission. The uncertainties in the robot model and environment require the robot planner to account for them beforehand or to adapt and improve its plan during runtime. The problem to be solved in this work is how to plan multiple day routes for a robot where all predefined locations must be visited only a single time and at each route the robot must start and return to the same initial position while respecting the daily maximum operation time constraint. The proposed solution uses problem definitions from the delivery industry and compares various metaheuristic based techniques for planning and scheduling the multiple day routes for a robotic mission. Therefore the problem of planning multiple day routes for a robot is modeled as a time constrained Vehicle Routing Problem where the robot daily plan is limited by how long the robot with a full charge can operate. The costs are modeled as the time a robot takes to move among locations considering robot and environment characteristics. The solution for this method is obtained in a two step process where a greedy initial solution is generated and then a local search is performed using meta-heuristic based methods. A custom time window formulation with respect to the theoretical maximum daily route is presented to add human expert input, priorities or expiration time to the planned routes allowing the planner to be flexible to various robotic applications. This thesis also proposes an intermediary mission control layer, that connects the daily route plan to the robot navigation layer. The goal of the Mission Control is to monitor the robot operation, continuously improve its route and adapt to unexpected events by dropping waypoints according to some defined penalties. This is an iterative process where optimization is performed locally in real time as the robot traverse its goals and offline at the end of each day with the remaining vertices. The performance of the various meta-heuristic and how optimization improves over time are analysed in several robotic route planning and scheduling scenarios. Two robotic simulation environments were built to demonstrate practical application of these methods. An unmanned ground vehicle operated fully autonomously using the presented methods in a simulated underground stone mine environment where the goal is to inspect the pillars for structural failures and a farm environment where the goal is to pollinate flowers with an attached robotic arm. All the optimization methods tested presented significant improvement in the total route costs compared to the initial Path-Cheapest-Arc solution. However the Guided Local Search presented a smaller standard deviation among the methods in most situations. The time-windows allowed for a seamless integration with an expert human input and the mission control layer, forced the robot to operate within the mission constraints by dynamically choosing the routes and the necessity of dropping some of the vertices
Dynamic vehicle routing with time windows in theory and practice
The vehicle routing problem is a classical combinatorial optimization
problem. This work is about a variant of the vehicle routing problem
with dynamically changing orders and time windows. In real-world
applications often the demands change during operation time. New
orders occur and others are canceled. In this case new schedules
need to be generated on-the-fly. Online optimization algorithms for
dynamical vehicle routing address this problem but so far they do
not consider time windows. Moreover, to match the scenarios found
in real-world problems adaptations of benchmarks are required. In
this paper, a practical problem is modeled based on the procedure
of daily routing of a delivery company. New orders by customers are
introduced dynamically during the working day and need to be integrated
into the schedule. A multiple ant colony algorithm combined with
powerful local search procedures is proposed to solve the dynamic
vehicle routing problem with time windows. The performance is tested
on a new benchmark based on simulations of a working day. The problems
are taken from Solomon's benchmarks but a certain percentage of the
orders are only revealed to the algorithm during operation time.
Different versions of the MACS algorithm are tested and a high performing
variant is identified. Finally, the algorithm is tested in situ:
In a field study, the algorithm schedules a fleet of cars for a surveillance
company. We compare the performance of the algorithm to that of the
procedure used by the company and we summarize insights gained from
the implementation of the real-world study. The results show that
the multiple ant colony algorithm can get a much better solution
on the academic benchmark problem and also can be integrated in a
real-world environment.Algorithms and the Foundations of Software technolog
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
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
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