229 research outputs found
Optimization of vehicle routing and scheduling with travel time variability - application in winter road maintenance
This study developed a mathematical model for optimizing vehicle routing and scheduling, which can be used to collect travel time information, and also to perform winter road maintenance operations (e.g., salting, plowing). The objective of this research was to minimize the total vehicle travel time to complete a given set of service tasks, subject to resource constraints (e.g., truck capacity, fleet size) and operational constraints (e.g., service time windows, service time limit).
The nature of the problem is to design vehicle routes and schedules to perform the required service on predetermined road segments, which can be interpreted as an arc routing problem (ARP). By using a network transformation technique, an ARP can be transformed into a well-studied node routing problem (NRP). A set-partitioning (SP) approach was introduced to formulate the problem into an integer programming problem (I PP). To solve this problem, firstly, a number of feasible routes were generated, subject to resources and operational constraints. A genetic algorithm based heuristic was developed to improve the efficiency of generating feasible routes. Secondly, the corresponding travel time of each route was computed. Finally, the feasible routes were entered into the linear programming solver (CPL EX) to obtain final optimized results.
The impact of travel time variability on vehicle routing and scheduling for transportation planning was also considered in this study. Usually in the concern of vehicle and pedestrian\u27s safety, federal, state governments and local agencies are more leaning towards using a conservative approach with constant travel time for the planning of winter roadway maintenance than an aggressive approach, which means that they would rather have a redundancy of plow trucks than a shortage. The proposed model and solution algorithm were validated with an empirical case study of 41 snow sections in the northwest area of New Jersey. Comprehensive analysis based on a deterministic travel time setting and a time-dependent travel time setting were both performed. The results show that a model that includes time dependent travel time produces better results than travel time being underestimated and being overestimated in transportation planning.
In addition, a scenario-based analysis suggests that the current NJDOT operation based on given snow sector design, service routes and fleet size can be improved by the proposed model that considers time dependent travel time and the geometry of the road network to optimize vehicle routing and scheduling. In general, the benefit of better routing and scheduling design for snow plowing could be reflected in smaller minimum required fleet size and shorter total vehicle travel time. The depot location and number of service routes also have an impact on the final optimized results. This suggests that managers should consider the depot location, vehicle fleet sizing and the routing design problem simultaneously at the planning stage to minimize the total cost for snow plowing operations
Exact and heuristic approaches for multi-component optimisation problems
Modern real world applications are commonly complex, consisting of multiple subsystems
that may interact with or depend on each other. Our case-study about wave
energy converters (WEC) for the renewable energy industry shows that in such a
multi-component system, optimising each individual component cannot yield global
optimality for the entire system, owing to the influence of their interactions or the
dependence on one another. Moreover, modelling a multi-component problem is
rarely easy due to the complexity of the issues, which leads to a desire for existent
models on which to base, and against which to test, calculations. Recently,
the travelling thief problem (TTP) has attracted significant attention in the Evolutionary
Computation community. It is intended to offer a better model for multicomponent
systems, where researchers can push forward their understanding of
the optimisation of such systems, especially for understanding of the interconnections
between the components. The TTP interconnects with two classic NP-hard
problems, namely the travelling salesman problem and the 0-1 knapsack problem,
via the transportation cost that non-linearly depends on the accumulated weight
of items. This non-linear setting introduces additional complexity. We study this
nonlinearity through a simplified version of the TTP - the packing while travelling
(PWT) problem, which aims to maximise the total reward for a given travelling tour.
Our theoretical and experimental investigations demonstrate that the difficulty of a
given problem instance is significantly influenced by adjusting a single parameter,
the renting rate, which prompted our method of creating relatively hard instances
using simple evolutionary algorithms. Our further investigations into the PWT
problem yield a dynamic programming (DP) approach that can solve the problem in
pseudo polynomial time and a corresponding approximation scheme. The experimental
investigations show that the new approaches outperform the state-of-the-art
ones. We furthermore propose three exact algorithms for the TTP, based on the DP
of the PWT problem. By employing the exact DP for the underlying PWT problem
as a subroutine, we create a novel indicator-based hybrid evolutionary approach for
a new bi-criteria formulation of the TTP. This hybrid design takes advantage of the
DP approach, along with a number of novel indicators and selection mechanisms
to achieve better solutions. The results of computational experiments show that the
approach is capable to outperform the state-of-the-art results.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201
PARAMETER-LESS AND METAPHOR-LESS METAHEURISTIC ALGORITHM SUGGESTION FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS
Many optimization problems are complex, challenging and take a significant amount of computational effort to solve. These problems have gained the attention of researchers and they have developed lots of metaheuristic algorithms to use for solving these problems. Most of the developed metaheuristic algorithms are based on some metaphors. For this reason, these algorithms have algorithm-specific parameters to reflect the nature of the inspired metaphor. This violates the algorithm's simplicity and brings extra workload to execute the algorithm. However, the optimization problems can also be solved with simple, useful, metaphor-less and algorithm-specific parameter-less metaheuristic algorithms. So, it is the essential motivation behind this study. We present a novel metaheuristic algorithm called Discrete Rao Algorithm (DRA) by updating some components of the generic Rao algorithm to solve the combinatorial optimization problems. To evaluate the performance of the DRA, we perform experiments on Traveling Salesman Problem (TSP) which is the well-known combinatorial optimization problem. The experiments are performed on different sized benchmark problems in the literature. The computational results show that the developed algorithm has obtained high quality solutions in a reasonable computation time and it is competitive with other algorithms in the literature for solving the TSP
Quantum annealing for vehicle routing and scheduling problems
Metaheuristic approaches to solving combinatorial optimization problems have many attractions.
They sidestep the issue of combinatorial explosion; they return good results; they are often
conceptually simple and straight forward to implement. There are also shortcomings. Optimal
solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of
experimentation; and conceptual differences between metaheuristics make absolute comparisons
of performance difficult. There is also the difficulty of configuration of the algorithm - the process
of identifying precise values for the parameters which control the optimization process.
Quantum annealing is a metaheuristic which is the quantum counterpart of the well known
classical Simulated Annealing algorithm for combinatorial optimization problems. This research
investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult
problem of practical significance within industries such as logistics and workforce scheduling. The
work devises spin encoding schemes for routing and scheduling problem domains, enabling an
effective quantum annealing algorithm which locates new solutions to widely used benchmarks.
The performance of the metaheuristic is further improved by the development of an enhanced
tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further
enhanced by taking advantage of multiprocessor environments, using threading techniques to
parallelize the optimization workload. The work also shows quantum annealing applied successfully
in an industrial setting to generate solutions to complex scheduling problems, results which
created extra savings over an incumbent optimization technique. Components of the intellectual
property rendered in this latter effort went on to secure a patent-protected status
Estimation of Noisy Cost Functions by Conventional and Adjusted Simulated Annealing Techniques
L'algorithme de recuit simulé est largement utilisé dans la communauté d'optimisation pour résoudre divers types de problèmes, discrets et continus.
L'objectif de cette thèse est d'analyser le recuit simulé dans des environnements déterministes et stochastiques pour des problèmes discrets.
Les objectifs précis sont de classer des problèmes clés, d'offrir des suggestions et des recommandations à suivre en utilisant l'algorithme de recuit simulé et de recuit simulé sous bruit.
Plus spécifiquement, des problèmes apparaissent en optimisation en présence de bruit, et sur la manière de le contrôler.
Nous proposons la méthode de recuit simulé bruité (NSA: Noisy Simulated Annealing), basée sur la modification de l'algorithme de Metropolis-Hastings présentée par Ceperlay and Dewing, qui surpasse les techniques de recuit simulé analogues, délivrant des solutions numériques similaires, à coût réduit.
Nous considérons les principales approches qui traitent le bruit dans le cadre du recuit simulé afin d'en extraire leurs attributs distinctifs et de produire une comparaison plus pertinente.
Nous évaluons ensuite les performances numériques de l'approche sur des instances du problème du voyageur de commerce.
Les résultats obtenus montrent un clair avantage pour le recuit simulé bruité, en présence de bruit.The Simulated Annealing (SA) algorithm is extensively used in the optimization community for solving various kinds of problems, discrete and continuous.
This thesis aims to analyze SA in both deterministic and stochastic environments for discrete problems.
Precise objectives are to classify key problems, offer suggestions and recommendations to be undertaken by using SA and Simulated Annealing Under Noise (SAUN).
More specifically, problems appear in optimization due to the existence of noise when evaluating the objective function, and how to control this noise. We propose a method, called Noisy Simulated Annealing (NSA), based on the Metropolis-Hasting algorithm modification presented by Ceperlay and Dewing, that outperforms analogous SA techniques, delivering similar numerical solutions, at a reduced cost.
We consider the main approaches in the SA setting that handle noise in order to extract their distinctive attributes and make the comparison more relevant.
We next assess the numerical performance of the approach on traveling salesman problem instances.
The outcomes of our tests show a clear advantage for NSA when solving different problems to get high-quality solutions in presence of noise
Quantization-Based Optimization: Alternative Stochastic Approximation of Global Optimization
In this study, we propose a global optimization algorithm based on quantizing
the energy level of an objective function in an NP-hard problem. According to
the white noise hypothesis for a quantization error with a dense and uniform
distribution, we can regard the quantization error as i.i.d. white noise. From
stochastic analysis, the proposed algorithm converges weakly only under
conditions satisfying Lipschitz continuity, instead of local convergence
properties such as the Hessian constraint of the objective function. This shows
that the proposed algorithm ensures global optimization by Laplace's condition.
Numerical experiments show that the proposed algorithm outperforms conventional
learning methods in solving NP-hard optimization problems such as the traveling
salesman problem.Comment: 25 pages, 3 figures, NeurIPS 2022 workshop OPT 2022 (14th Annual
Workshop on Optimization for Machine Learning
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