The Multi-Location Transshipment Problem with Positive Replenishment Lead Times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm.Transshipment;Infinitesimal Perturbation Analysis (IPA);Simulation Optimization

CHARACTERIZATIONS OF SOME SPECIAL QUATERNIONIC CURVES

We derive a general differential equation satisfed by the distance function for quaternionic curves in Euclidean 4-space. By using this differential equation, we express characterizations of some special quaternionic curves such as spherical curves and rectifying curves. Lastly, we reconsider the characterization of a quaternionic  general helix

Microencapsulation for Clinical Applications and Transplantation by Using Different Alginates

Microencapsulation has been the most frequently used technique for several different disciplines such as cell-based therapies and/or transplantation. Technology is based on the idea of combining and coating a material or isolating from an external source. Microencapsulation may be performed with different materials and, among natural biocompatible materials, alginate-based microencapsulation technique is the most appropriate material for microencapsulation. The structural components of alginate materials are the derivatives of alginic acid, which is found in brown algae as an intercellular gel matrix. This alginate is preferred for clinical applications due to its safety in human studies. Therefore, the choice and the combined system need to be carefully optimized to achieve biocompatible application through cell microencapsulation especially for long term. Specifications of alginate such as primary source, isolation process, viscosity, and purity contribute to improve its biocompatibility. Clinically, cell microencapsulation is the major contribution to the field of transplantation by its technique and additionally provides local immune isolation. This chapter discusses the potential benefits of clinically suitable alginates and their applications. This promising technology may highlight its considerable potential for patients that require transplantation and/or replacement therapy in the future

The Multi-Location Transshipment Problem with Positive Replenishment Lead Times

Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm

Usporedba performansi modela ARIMAX, ANN i hibridizacije ARIMAX-ANN u predviđanju prodaje za industriju namještaja

Manufacturing firms aim to increase their profits and reduce costs in a competitive and rapidly changing market. One of the most important ways to reach these goals is to forecast sales correctly. Furniture manufacturing, which is considered a prosperous and growing industry in Turkey, has an increasing trend related to the growth in construction and associated industries, increase in urban migration and increase in per capita income. Accuracy of sales forecasting in furniture industry is affected by external factors, such as consumer confidence index, producer price index, month of the year and number of vacation days as well as the time factor itself. This study aims to develop an Autoregressive Integrated Moving Average with external variables (ARIMAX) to forecast the total monthly sales of furniture products of a well-known manufacturer in Turkey. As a follow up study, a performance comparison between ARIMAX, artificial neural networks (ANNs) and ARIMAX-ANN hybridization is performed. In conclusion, results of performance measures demonstrate that hybrid model developed for each amount of product sales give better accuracy values than single methods. Overall, it is proved that using the ARIMAX and hybridization of this method with ANN are applicable for forecasting monthly sales of furniture products

Evaluation of hospital disaster preparedness by a multi-criteria decision making approach: The case of Turkish hospitals

Considering the unexpected emergence of natural and man-made disasters over the world and Turkey, the importance of preparedness of hospitals, which are the first reference points for people to get healthcare services, becomes clear. Determining the level of disaster preparedness of hospitals is an important and necessary issue. This is because identifying hospitals with low level of preparedness is crucial for disaster preparedness planning. In this study, a hybrid fuzzy decision making model was proposed to evaluate the disaster preparedness of hospitals. This model was developed using fuzzy analytic hierarchy process (FAHP)-fuzzy decision making trial and evaluation laboratory (FDEMATEL)-technique for order preference by similarity to ideal solutions (TOPSIS) techniques and aimed to determine a ranking for hospital disaster preparedness. FAHP is used to determine weights of six main criteria (including hospital buildings, equipment, communication, transportation, personnel, flexibility) and a total of thirty-six sub-criteria regarding disaster preparedness. At the same time, FDEMATEL is applied to uncover the interdependence between criteria and sub-criteria. Finally, TOPSIS is used to obtain ranking of hospitals. To provide inputs for TOPSIS implementation, some key performance indicators are established and related data is gathered by the aid of experts from the assessed hospitals. A case study considering 4 hospitals from the Turkish healthcare sector was used to demonstrate the proposed approach. The results evidenced that Personnel is the most important factor (global weight = 0.280) when evaluating the hospital preparedness while Flexibility has the greatest prominence (c + r = 23.09

A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation)

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop the accelerator before damage occurs. We describe the acceleratoroperation, related Machine Learning research, the prediction performance required to abort beamwhile maintaining operations, the monitoring device and its data, and the Siamese method andits results. These results show that the researched method can be applied to improve acceleratoroperations.Comment: 11 pages, 15 figures, for PR-A

Primjena umjetnih neuronskih mreža uz pomoć Bayesova pravila učenja u predviđanju prodaje za industriju namještaja

Most organizations in manufacturing environments aim to increase their profi ts and reduce costs against competitive and rapidly changing market conditions. Accuracy of sales forecasting is undoubtedly a successful way to reach the aforementioned goals. At the same time, this enables executives to improve customer satisfaction, reduce lost sales and plan production efficiently. As a growing industry in Turkey, furniture manufacturing has an increased product demand in relation to the recent growth in construction and related industries, increase in urban population and increase in person-level income. Therefore, accurate sales forecasting systems in this industry are more focused on the special and calendar factors, such as consumer confidence index, producer price index, time of the year and number of vacation days. In this paper, an artificial neural network (ANN) based forecasting model is proposed by using MATLAB for processing total monthly sales data of a corporate furniture manufacturer located in the Black Sea region of Turkey. The method is a component of ANN, namely Bayesian regularization. The proposed model is applied to monthly sales figures of a corporate furniture manufacturing company. In conclusion, the results of performance measures show that using the ANN model based on Bayesian rules training is an applicable choice for forecasting of monthly sales of the observed furniture factory.Cilj većine proizvodnih organizacija jest povećanje dobiti i smanjenje troškova u skladu s konkurentnim i promjenjivim tržišnim uvjetima. Točnost predviđanja prodaje nesumnjivo je uspješan način postizanja navedenih ciljeva. Istodobno, to povećava zadovoljstvo korisnika, učinkovito smanjuje izgubljenu prodaju i omogućuje bolje planiranje proizvodnje. U proizvodnji namještaja, industriji koja se u Turskoj sve jače razvija, bilježi se povećana potražnju proizvoda, u skladu s nedavnim rastom građevinskih i srodnih industrija, s povećanjem broja urbanog stanovništva i s rastom osobnih prihoda. Stoga precizni sustavi predviđanja prodaje u industriji namještaja više pozornosti usmjeravaju na posebne i kalendarske čimbenike poput indeksa povjerenja potrošača, indeksa proizvođačkih cijena, doba godine i broja dana odmora u godini. U ovom je radu predložen model predviđanja na temelju umjetne neuronske mreže (ANN) uz pomoć MATLAB-a za obradu podataka ukupne mjesečne prodaje proizvođača uredskog namještaja koji se nalazi u Crnomorskoj regiji u Turskoj. Metoda je komponenta ANN-a, tj. Bayesova regulacija. Predloženi se model primjenjuje na podatke o mjesečnoj prodaji tvrtke za proizvodnju uredskog namještaja. Zaključno, rezultati mjerenja uspješnosti pokazuju da je primjena ANN modela utemeljenoga na Bayesovim pravilima dobar izbor za prognoziranje mjesečne prodaje promatrane tvornice namještaja