A HYBRID ALGORITHM FOR THE UNCERTAIN INVERSE p-MEDIAN LOCATION PROBLEM

In this paper, we investigate the inverse p-median location  problem with variable edge lengths and variable vertex weights on networks in which the vertex weights and modification costs are the independent uncertain variables. We propose a  model for the uncertain inverse p-median location problem  with tail value at risk objective. Then, we show that  it  is NP-hard. Therefore,  a hybrid particle swarm optimization  algorithm is presented  to obtain   the approximate optimal solution of the proposed model. The algorithm contains expected value simulation and tail value at risk simulation

A methodology for energy prediction and optimization of a system based on the Energy Hub Concept using Particle Swarms

In this paper, a methodology for the energy prediction for the different consumptions of a system based in the Energy Hub concept is presented. The methodology that has been used for the energy prediction is based on an Adaptive Neuro-Fuzzy Inference System. An optimization method based on Particle Swarms has been used to minimize the energy cost of a system with multiple sources such as, photovoltaic, electrical grid and natural gas.Peer ReviewedPostprint (author's final draft

Numerical optimization for vibration and noise of the wheel based on PSO-GA method

Currently, those reported researches conducted optimal design for the wheel only in order to reduce the tread wear and increase the service life, but they did not consider the wheel vibration and radiation noise which seriously influence people’s life and did not achieve obvious noise reduction effects. Aiming at this question, a multi-body dynamic model of the high-speed train was established, and the vertical and radial force was extracted to input into the finite element model of the wheel to compute its vibration characteristics. Then, the wheel was conducted on a multi-objective optimization based on particle swarm optimization improved by genetic algorithm (PSO-GA) method. Finally, the optimized vibration results were mapped to the acoustic element model to compute the radiation noise of the wheel. The computational model was also validated by experimental test. In order to observe the optimized effect, the optimized results were compared with those of the traditional GA and PSO method. Solutions of the traditional GA and PSO method were relatively dispersed during iterations and the algorithm could easily fall into the locally optimal solution. The optimized results of PSO-GA method were obviously better. Compared with the original wheel, the vibration acceleration was reduced by 22.9 %, and the mass was reduced by 1.1 %. Finally, the optimized vibration was mapped to the boundary element model to compute the radiation noise of the wheel, and the computational results were compared with the original wheel. Radiation noises of the original wheel were obviously more than that of the optimized wheel, and there were a lot of obvious peak noises in the original wheel. Radiation noises of the optimized wheel only had two obvious noise peaks in the analyzed frequency. Therefore, a wheel with low noises and lightweight was achieved in this paper

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Hub Covering Location Problem Considering Queuing and Capacity Constraints

In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in placeswhere the entrance flows are more than a predetermined value.A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network.On modeling, a nonlinear mathematical program is presented.Then, the nonlinear constraints are convertedto linear ones.Due to the computational complexity of the problem,genetic algorithm (GA),particle swarm optimization (PSO)based heuristics, and improved hybrid PSO are developedto solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is appliedin order to tune the parameters. Finally,the efficiency ofthe proposed heuristicsis studied while designing a number of test problems with different sizes.The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the proble

Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory

In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels

A new bi-objective model of the urban public transportation hub network design under uncertainty

n/