The assignment of airport resources can significantly affect the quality of service provided by airlines and airports. High quality assignments can support airlines and airports in adhering to published schedules by m...
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The assignment of airport resources can significantly affect the quality of service provided by airlines and airports. High quality assignments can support airlines and airports in adhering to published schedules by minimising changes or delays while waiting for resources to become available. In this paper, we consider the problem of assigning available baggage sorting stations to flights which have already been scheduled and allocated to stands. A model for the problem is presented, and the different objectives which have to be considered are highlighted. A number of constructive algorithms for sorting station assignments are then presented and their effects are compared and analysed when different numbers of sorting stations are available. It can be observed that appropriate algorithm selection is highly dependent upon whether or not reductions in service time are permitted and upon the flight density in relation to the number of sorting stations. Finally, since these constructive approaches produce different solutions which are better for different trade-offs of the objectives, we utilise these as initial solutions for an evolutionary algorithm as well as for an Integer Linear Programming model in CPLEX. We show that in both cases they are helpful for improving the results which are obtainable within reasonable solution times.
We analyze algorithmic and computational aspects of biological phenomena, such as replication and programmed death, in the context of machine learning. We use two different measures of neuron efficiency to develop mac...
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We analyze algorithmic and computational aspects of biological phenomena, such as replication and programmed death, in the context of machine learning. We use two different measures of neuron efficiency to develop machine learning algorithms for adding neurons to the system (i.e., replication algorithm) and removing neurons from the system (i.e., programmed death algorithm). We argue that the programmed death algorithm can be used for compression of neural networks and the replication algorithm can be used for improving performance of the already trained neural networks. We also show that a combined algorithm of programmed death and replication can improve the learning efficiency of arbitrary machine learning systems. The computational advantages of the bio-inspired algorithms are demonstrated by training feedforward neural networks on the MNIST dataset of handwritten images.
We address the Container Pre-Marshalling Problem (CPMP). The CPMP consists in ordering containers in stacks such that the retrieval of these containers is carried out without additional movements. The ordering has to ...
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We address the Container Pre-Marshalling Problem (CPMP). The CPMP consists in ordering containers in stacks such that the retrieval of these containers is carried out without additional movements. The ordering has to be done in a minimum number of steps. Target-guided constructive heuristics report very good results in a short time. At each step, they select one poorly located container and rearrange it to an adequate position by a sequence of movements. The sequence of movements is generally generated by following a set of rules. In this work, we propose a different and more direct approach. Whenever possible, ordered stacks are filled by directly moving badly placed containers into them such that the containers become well placed. If it is not possible, then a stack is emptied or reduced to have more available slots, and the process is repeated. Unlike target-guided algorithms which rigidly adhere to a predefined sequence of movements for each badly placed container, our fill-and-reduce approach maintains the capacity to adapt to the evolving situation making choices based on the current state of the container stacks. The algorithm has shown superior performance compared to traditional target-guided heuristics, particularly in larger instances of classical benchmark sets. Furthermore, when embedded in a beam search algorithm, it reports the best results compared to traditional techniques that do not use machine learning.
This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructi...
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This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive-pruning strategy, and different training samples for individual NN's learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble;(2) maintaining accuracy and diversity of NNs at the same time;and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.
This article presents the design and evaluation of transit networks using a route expansion heuristic and agent-based travel demand simulation. The route expansion mechanism is a type of constructive heuristic algorit...
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This article presents the design and evaluation of transit networks using a route expansion heuristic and agent-based travel demand simulation. The route expansion mechanism is a type of constructive heuristic algorithm that derives new transit routes by inserting neighbouring nodes into existing routes with the aim of improving the demand coverage. The resulting networks are evaluated with an agent-based travel demand simulation model. The use of agent-based modelling is a departure from the existing route expansion literature and indeed the broader transit network design discipline in which the four step model has been extensively used. The procedure is tested on a bus rapid transit network in the City of Cape Town in South Africa. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.
Transit Network Design Problem is a multi-disciplinary problem that is considered one of the most intractable problems for real size networks. In the late 90s, Meta-heuristics started to prove more reliability to the ...
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Transit Network Design Problem is a multi-disciplinary problem that is considered one of the most intractable problems for real size networks. In the late 90s, Meta-heuristics started to prove more reliability to the problem. Genetic Algorithm (GA) is one of the popular Meta-heuristics which is usually implemented because it is simply adapted to the problem. In this study, GA is presented as a complete constructive multi-objective algorithm that creates its own routes from scratch then assembles the routes into efficient transit networks. Finally, it handles the multi-criteria nature of the problem until producing the optimal (near optimal) Pareto front solutions. A new frequency setting algorithm is also developed based on simulation results at the bus stop level which takes the bi-level decision making of both users and operators implicitly. Experimental studies on two real size networks are conducted to validate the methodology performance and robustness. (C) 2018 Elsevier Ltd. All rights reserved.
This article presents the design and evaluation of transit networks using a route expansion heuristic and agent-based travel demand simulation. The route expansion mechanism is a type of constructive heuristic algorit...
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This article presents the design and evaluation of transit networks using a route expansion heuristic and agent-based travel demand simulation. The route expansion mechanism is a type of constructive heuristic algorithm that derives new transit routes by inserting neighbouring nodes into existing routes with the aim of improving the demand coverage. The resulting networks are evaluated with an agent-based travel demand simulation model. The use of agent-based modelling is a departure from the existing route expansion literature and indeed the broader transit network design discipline in which the four step model has been extensively used. The procedure is tested on a bus rapid transit network in the City of Cape Town in South Africa.
The single container loading problem consists of a container that has to be filled with a set of boxes. The objective of the problem is to maximize the total volume of the loaded boxes. For solving the problem, constr...
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The single container loading problem consists of a container that has to be filled with a set of boxes. The objective of the problem is to maximize the total volume of the loaded boxes. For solving the problem, constructive approaches are the most successful. A key element of these approaches is related to the selection of the box to load next. In this work, we propose a new evaluation function for ranking boxes. Our function rewards boxes that fit well in the container, taking into account the previously placed ones. To construct a more robust function, we consider some other well-known evaluation criteria such as the volume of the block and the estimated wasted volume in the free space of the container. Our approach shows promising results when compared with other state-of-the-art algorithms on a set of 1600 well known benchmark instances. (C) 2017 Elsevier Ltd. All rights reserved.
SyDPaCC is a set of libraries for the Coq proof assistant. It allows to write naive functional programs (i.e. with high complexity) that are considered as specifications, and to transform them into more efficient vers...
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SyDPaCC is a set of libraries for the Coq proof assistant. It allows to write naive functional programs (i.e. with high complexity) that are considered as specifications, and to transform them into more efficient versions. These more efficient versions can then be automatically parallelised before being extracted from Coq into source code for the functional language OCaml together with calls to the Bulk Synchronous Parallel ML library. In this paper we present a new core version of SyDPaCC for the development of parallel programs correct-by-construction using the theory of list homomorphisms and algorithmic skeletons implemented and verified in Coq. The framework is illustrated on the maximum prefix sum problem.
constructive and destructive parsimonious extreme learning machines (CP-ELM and DP-ELM) were recently proposed to sparsify ELM. In comparison with CP-ELM, DP-ELM owns the advantage in the number of hidden nodes, but i...
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constructive and destructive parsimonious extreme learning machines (CP-ELM and DP-ELM) were recently proposed to sparsify ELM. In comparison with CP-ELM, DP-ELM owns the advantage in the number of hidden nodes, but it loses the edge with respect to the training time. Hence, in this paper an equivalent measure is proposed to accelerate DP-ELM (ADP-ELM). As a result, ADP-ELM not only keeps the same hidden nodes as DP-ELM but also needs less training time than CP-ELM, which is especially important for the training time sensitive scenarios. The similar idea is extended to regularized ELM (RELM), yielding ADP-RELM. ADP-RELM accelerates the training process of DP-RELM further, and it works better than CP-RELM in terms of the number of hidden nodes and the training time. In addition, the computational complexity of the proposed accelerating scheme is analyzed in theory. From reported results on ten benchmark data sets, the effectiveness and usefulness of the proposed accelerating scheme in this paper is confirmed experimentally. (C) 2015 Elsevier B.V. All rights reserved.
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