This work is focused on workforce scheduling for assembly lines with the additional constraint of workforce distancing. The aim is to warrant the necessary safety and health requirements due to COVID-19. The research ...
详细信息
This work is focused on workforce scheduling for assembly lines with the additional constraint of workforce distancing. The aim is to warrant the necessary safety and health requirements due to COVID-19. The research stems within an industrial case in which a methodology has been developed with the objectives of i) developing a constraintoptimization model considering the social distancing of workers as part of the workforce scheduling requirements and ii) investigating how the workforce distancing can affect certain production performances. Through an empirical investigation the impact of distancing on workforce allocation is appraised in terms of daily production capacity. Then, different distancing thresholds are assessed to seek the optimal balance among production performances and safety requirements. The research resulted in a tool able to adapt the scheduling sequence to those health/safety situations where the production manager needs to minimize losses in terms of production capacity, warranting the safest working conditions.
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control (CAC) combined with resource allocation in adaptive mul...
详细信息
ISBN:
(纸本)9781424448203
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control (CAC) combined with resource allocation in adaptive multimedia wireless networks;this not only decides whether to accept or reject a request call, but also determines the allocated bandwidth to that requesting call. The objective is to maximize the network revenue and maintain predefined QoS constraints. The QoS constraints are classified as two categories: long period constraints and instantaneous constraints. Long period constraints are handled by a constraint handling method called Superiority of Feasible Points. Instantaneous constraints and system limitations are handled by an External Supervisor.
Particle swarm optimization is a global random search algorithm that is simulated by mimicking the behavior of migration and aggregation of birds. In order to improve the global search ability of the algorithm, this p...
详细信息
ISBN:
(纸本)9781538652145
Particle swarm optimization is a global random search algorithm that is simulated by mimicking the behavior of migration and aggregation of birds. In order to improve the global search ability of the algorithm, this paper proposes a new inertia weight. For the constrained optimizationproblem, this paper controls the number of particles that violate the constraint conditions, and proposes a new particle selection method to improve the ability of the particle swarm algorithm to search for boundaries. Finally, experiments were performed using three benchmark functions, and the results show that the optimization speed of the improved particle swarm algorithm has been greatly improved.
In June 2008, the Eclipse open platform released a new dependency management system called p2. That system was based on the translation of the dependency management problem into a pseudo-Boolean optimizationproblem, ...
详细信息
In June 2008, the Eclipse open platform released a new dependency management system called p2. That system was based on the translation of the dependency management problem into a pseudo-Boolean optimizationproblem, to be handled by the Sat4j solver. Since then, p2 has been more tightly integrated with Sat4j, the platform opened a public plugin repository (the Eclipse marketplace) which relies on p2 to install the available plugins and their dependencies, and became the favorite way to install plugins in the Eclipse community. This paper summarizes the issues raised by Eclipse dependency management, its pseudo-Boolean encoding within p2, its extension for Linux package management with p2cudf, and concludes with lessons learned on using research software in production systems.
This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimizationproblem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO)...
详细信息
This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimizationproblem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO) cooperation algorithm to generate an optimal path. The objective function of the UAV flight path is modeled to have the shortest length satisfying strict multiple threat area constraints. The aconstrained method using the level comparison strategy is integrated into both FWA and PSO to enhance their superior constraint-handling ability. To increase the population diversity, the whole population is divided to fireworks and particles, which perform search operation in parallel. A new mutation strategy in the fireworks is adopted to avoid falling into the local optimum. Information sharing mechanism between fireworks and particles isestablished to make the population achieve the excellent global optimization performance. Several numerical simulations are carried out and the results show that our proposed algorithm performs well in obtaining high quality solutions and handling constraints. (C) 2021 Elsevier Masson SAS. All rights reserved.
The patient bed assignment problem consists of managing, in the best possible way, a set of beds with particular features and assigning them to a set of patients with special requirements. This assignment problem can ...
详细信息
The patient bed assignment problem consists of managing, in the best possible way, a set of beds with particular features and assigning them to a set of patients with special requirements. This assignment problem can be seen an optimizationproblem, of which the intended aims are usually to minimize the number of internal movements within a unit and to maximize bed usage according to the levels of criticality of the patients, among others. The usual approaches for solving this problem follow a traditional model based on the constraint programming paradigm, mainly using hard constraints. However, in real-life problems, constraints that should ideally be satisfied are often violated. In this paper, we present a new model for the patient bed assignment problem based on the minimum sum of unsatisfied constraints. This technique enables the consideration of soft constraints in the potential solutions that exhibit the best performance. The aim is to find the assignment that minimizes a weighted sum of the unsatisfied constraints. To this end, we use an autonomous binary version of the bat algorithm, which is an optimization technique inspired by the bio-sonar behaviour of microbats, to find the best set of potential solutions without requiring any expert user knowledge to achieve an efficient solution process. To validate our proposal, we use our model to solve problem instances based on data from several hospitals, and we perform a detailed comparative statistical analysis with a traditional constraint programming solver and several well-known optimization algorithms, including the classic bat algorithm. Promising results show that our approach is capable of efficiently solving 30 instances with decreased solution times. (C) 2019 Elsevier B.V. All rights reserved.
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control(CAC) combined with resource allocation in adaptive mult...
详细信息
This paper propose a novel learning approach that applies NeuroEvolution of Augmenting Topology (NEAT) based learning algorithm to resolve Call Admission Control(CAC) combined with resource allocation in adaptive multimedia wireless networks;this not only decides whether to accept or reject a request call,but also determines the allocated bandwidth to that requesting *** objective is to maximize the network revenue and maintain predefined QoS *** QoS constraints are classified as two categories:long period constraints and instantaneous *** period constraints are handled by a constraint handling method called Superiority of Feasible *** constraints and system limitations are handled by an External Supervisor.
Background: In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them...
详细信息
Background: In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets. Results: This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56% and 62% of database searching time while losing only a small amount of high-quality spectra. Conclusions: Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.
暂无评论