26 research outputs found
Interactive Evolutionary Multi-Objective Optimization Algorithms: Development, Improvements, Benchmarking and Analysis of Performance
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Energy consumption optimization for sustainable flexible robotic cells: Proposing exact and metaheuristic methods
Many manufacturing companies are always looking for a way to reduce energy consumption by utilizing energy-efficient production methods. These methods can be different depending on the type of products and production technology. For instance, one of the ways to increase energy efficiency and keep the precision of production is to use robots for the transportation of the parts among the machines and loading/unloading the machines. This technology is affordable compared to the technologies used in manufacturing companies. Manufacturing companies that rely on robotics technology must have a strategy to reduce energy costs and at the same time increase production by adjusting the intensity of processing or controlling the production rate. This study presents an exact solution method for flexible robotic cells to control the production rate and minimize energy consumption, which aims to both reduce electricity prices and minimize greenhouse gas (GHG) emissions under a lead time of production. Then, considering the NP-hardens nature of the problem, a heuristic solution method based on the genetic algorithm (GA) is proposed. Using the proposed approach, manufacturing companies will be able to make more accurate decisions about processing intensity and process scheduling while ensuring sustainability
Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution:A case study
A Data-Driven Approach to Support the Understanding and Improvement of Patient’s Journeys: A Case Study Using Electronic Health Records of an Emergency Department
Expert System Assessment of Work-Related Musculoskeletal Disorders for Video Display Terminal Users
Assignment of Medical Staff to Operating Rooms in Disaster Preparedness:1 A Novel Stochastic Approach
How to generate species with positive concentrations for all positive times?
Given a reaction (network) we are looking for minimal sets of species
starting from which all the species will have positive concentrations for all
positive times in the domain of existence of the solution of the induced
kinetic differential equation. We present three algorithms to solve the
problem. The first one essentially checks all the possible subsets of the sets
of species. This can obviously work for only a few dozen species because of
combinatorial explosion. The second one is based on an integer programming
reformulation of the problem. The third one walks around the state space of the
problem in a systematic way and produces all the minimal sets of the
advantageous initial species, and works also for large systems. All the
algorithms rely heavily on the concept of Volpert indices, used earlier for the
decomposition of overall reactions [Kov\'acs \textit{et al., Physical Chemistry
Chemical Physics} 2004, 6, 1236]. Relations to the permanence hypothesis,
possible economic or medical uses of the solution of the problem are analyzed,
and open problems are formulated at the end.Comment: 19 pages, 7 figure