1,713 research outputs found
An ESPC algorithm based approach to solve inventory deployment problem
Global competitiveness has enforced the hefty industries to become more customized. To compete in the market they are targeting the customers who want exotic products, and faster and reliable deliveries. Industries are exploring the option of satisfying a portion of their demand by converting strategically placed products, this helps in increasing the variability of product produced by them in short lead time. In this paper, authors have proposed a new hybrid evolutionary algorithm named Endosymbiotic-Psychoclonal (ESPC) algorithm to determine the amount and type of product to stock as a semi product in inventory. In the proposed work the ability of previously proposed Psychoclonal algorithm to exploit the search space has been increased by making antibodies and antigen more cooperative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results obtained, are compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained, and convergence time required to reach the optimal /near optimal value of the solution
Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach
Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution
A hybrid meta-heuristic approach for buffer allocation in remanufacturing environment
Remanufacturing system is complicated due to its stochastic nature. Random customer demand, return product rate and system unreliability contribute to this complexity. Remanufacturing systems with unreliable machines usually contain intermediate buffers which are used to decouple the machines, thereby, reducing mutual interference due to machine breakdowns. Intermediate buffers should be optimized to eliminate waste of resources and avoid loss of throughput. The Buffer Allocation Problem (BAP) deals with allocating optimally fixed amount of available buffers to workstations located in manufacturing or remanufacturing systems to achieve specific objectives. Optimal buffer allocation in manufacturing and remanufacturing systems not only minimizes holding cost and stock space, but also makes facilities planning and remanufacturing decisions to be effectively coordinated. BAP in a non-deterministic environment is certainly one of the most difficult optimization problems. Therefore, a mathematical framework is provided to model the dependence of throughput on buffer capacities. Obviously, based on the survey undertaken, not only there exists no algebraic relation between the objective function and buffer size but the current literature does not offer analytical results for buffer capacity design in remanufacturing environment. Decomposition principle, expansion method for evaluating system performance and an efficient hybrid Meta-heuristic search algorithm are implemented to find an optimal buffer allocation for remanufacturing system. The proposed hybrid Simulated Annealing (SA) with Genetic Algorithm (GA) is compared to pure SA and GA. The computational experiments show better quality, more accurate, efficient and reliable solutions obtained by the proposed hybrid algorithm. The improvement obtained is more than 4.18 %. Finally, the proposed method is applied on toner cartridge remanufacturing company as a case study, and the numerical results from hybrid algorithm are presented and compared with results from SA and GA
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 kysyntÀ ohjaa erilaisia toimitusketju- ja logistiikkasijaintipÀÀtöksiÀ, ja agenttipohjainen mallinnusmenetelmÀ (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. TÀmÀ vÀitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmÀÀn ja verkon optimointiin tÀllaisten ongelmien ratkaisemiseksi, ja sisÀltÀÀ kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin.
EnsimmÀinen tapaustutkimus esittelee kuinka kÀyttÀmÀllÀ vÀestötiheyksiÀ Norjassa, Suomessa ja Ruotsissa voidaan mÀÀrittÀÀ strategioita jakelukeskusten (DC) sijaintiin kÀyttÀmÀllÀ matkan etÀisyyden minimoimista. Kullekin skenaariolle kehitetÀÀn matka-aikakartat. LisÀksi analysoidaan nÀistÀ kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetÀÀn viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerÀtÀÀn useilta alueilta ja kuljetetaan lÀhimpÀÀn kerÀyspisteeseen. TÀmÀ tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistÀÀ kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenÀÀn kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetÀÀn erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekÀ monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan kÀytössÀ olevat kuorma-autot, sekÀ varastomÀÀrÀt ja ajetut matkat. LisÀksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumÀÀrÀstÀ sekÀ tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen mÀÀrÀ. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa kÀytetÀÀn ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. RatkaisumenetelmÀÀ kÀytetÀÀn dataan, joka on perÀisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiÀ suorituskykyindikaattoreita.
ABM-menetelmÀ, toisin kuin monet muut mallintamismenetelmÀt, on tÀysin rÀÀtÀlöitÀvissÀ oleva menetelmÀ, joka voi sisÀltÀÀ laajasti erilaisia prosesseja ja elementtejÀ. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan jÀrjestelmÀn toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed
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