1,721,049 research outputs found
Capacity Management in Hotel Industry for Turkey
No full textThe hotel industry is capital-intensive with high operating leverage in providing accommodations. Examining final costs play very important role in determining of overcapacity and undercapacity. Turkey's experiencing growth in tourism and has remarkable increases in accommodation. This situation possesses great importance for the capital groups investing in this area. How these groups and local tradesman implement a strategy on capacity management is the main question. Single period inventory model is used at the analysis phase and several interests are examined on this topic. Future Room demand is estimated by ARIMA and in estimating cost of undersupply earnings before taxes is used per room night sold. Combining the derived cost ratio with the future room demand and probability distribution estimated from the ARIMA method, the optimal hotel room capacity can be determined. The purpose of the study is to examine hotel room supply and demand for optimal capacity in Turkey
A hybrid algorithm for total tardiness minimisation in flexible job shop: genetic algorithm with parallel VNS execution
No full textThis paper addresses the flexible-job-shop scheduling problem (FJSP) with the objective of minimising total tardiness. FJSP is the generalisation of the classical job-shop scheduling problem. The difference is that in the FJSP problem, the operations associated with a job can be processed on any set of alternative machines. We developed a new algorithm by hybridising genetic algorithm and variable neighbourhood search (VNS). The genetic algorithm uses advanced crossover and mutation operators to adapt the chromosome structure and the characteristics of the problem. Parallel-executed VNS algorithm is used in the elitist selection phase of the GA. Local search in VNS uses assignment of operations to alternative machines and changing of the order of the selected operation on the assigned machine to increase the result quality while maintaining feasibility. The purpose of parallelisation in the VNS algorithm is to minimise execution time. The performance of the proposed method is validated by numerical experiments on several representative problems and compared with adapted constructive heuristic algorithms' (earliest due date, critical ratio and slack time per remaining operation) results
A cost-sensitive decision tree approach for fraud detection
No full textWith the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems. (C) 2013 Elsevier Ltd. All rights reserved
A coordinated scheduling problem for the supply chain in a flexible job shop machine environment
No full textIn this study, a new coordinated scheduling problem is proposed for the multi-stage supply chain network. A multi-product and multi-period supply chain structure has been developed, including a factory, warehouses, and customers. Furthermore, the flexible job shop scheduling problem is integrated into the manufacturing part of the supply chain network to make the structure more comprehensive. In the proposed problem, each product includes a sequence of operations and is processed on a set of multi-functional machines at the factory to produce the final product. Final products are delivered to the warehouses to meet customers' demands. If the demands of customers are not fulfilled, the shortage in the form of backorder may occur at any period. The problem is expressed as a bi-objective mixed-integer linear programming (MILP) model. The first objective function is to minimize the total supply chain costs. On the other hand, the second objective function aims to minimize the makespan in all periods. A numerical example is presented to evaluate the performance of the proposed MILP model. Five multi-objective decision-making (MODM) methods, namely weighted sum, goal programming, goal attainment, LP metric, and max-min, are used to provide different alternative solutions to the decision-makers. The performance of the methods is evaluated according to both objective function values and CPU time criteria. In order to select the best solution technique, the displaced ideal solution method is applied. The results reveal that the weighted sum method is the best among all MODM methods
Prediction of medical waste generation using SVR, GM (1,1) and ARIMA models: a case study for megacity Istanbul
No full textPurpose Estimation of the amount of waste to be generated in the coming years is critical for the evaluation of existing waste treatment service capacities. This study was conducted to evaluate the performance of various mathematical modeling methods to forecast medical waste generation of Istanbul, the largest city in Turkey. Methods Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Grey Modeling (1,1) and Linear Regression (LR) analysis were used to estimate annual medical waste generation from 2018 to 2023. A 23-year data from 1995 to 2017 provided from the Istanbul Metropolitan Municipality's affiliated environmental company ISTAC Company were utilized to examine the forecasting accuracy of methods. Different performance measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R-2) were used to evaluate the performance of these models. Results ARIMA (0,1,2) model with the lowest RMSE (763.6852), MAD (588.4712), and MAPE (11.7595) values and the highest R-2(0.9888) value showed a superior prediction performance compared to SVR, Grey Modeling (1,1), and LR analysis. The results obtained from the models indicated that the total amount of annual medical waste to be generated will increase from about 26,400 tons in 2017 to 35,600 tons in 2023. Conclusions ARIMA (0,1,2) model developed in this study can help decision-makers to take better measures and develop policies regarding waste management practices in the future
A research survey: review of AI solution strategies of job shop scheduling problem
No full textThis paper focus on artificial intelligence approaches to NP-hard job shop scheduling (JSS) problem. In the literature successful approaches of artificial intelligence techniques such as neural network, genetic algorithm, multi agent systems, simulating annealing, bee colony optimization, ant colony optimization, particle swarm algorithm, etc. are presented as solution approaches to job shop scheduling problem. These studies are surveyed and their successes are listed in this article
Assessing the social sustainable supply chain indicators using an integrated fuzzy multi-criteria decision-making methods: a case study of Turkey
No full textSustainability of environmental, economic, and social systems aims to not destroy the resources that future generations should have in today's shacks. However, for many years, the concept of sustainability has only been examined in the economic and environmental contexts, and the concept of social sustainability has been neglected. The social dimension, which is a very important factor besides economic and biophysical environment within the social structure, is one of the most basic pillars of sustainability. In this study, we focused on the example of companies in the automotive industry in Turkey to evaluate them in terms of social sustainability. Many automotive manufacturers and suppliers are operating in Turkey. However, some of them are local, and some of them are working with regional companies. This differentiation affects the level of self-development of companies. Therefore, the case study for the evaluation of Turkey's social sustainability will provide the opportunity to achieve significant results. In this study, four companies that are in a supplier status in the automotive sector were considered by the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (FTOPSIS), which are Multi-Criteria Decision-Making (MCDM) methods. The obtained results revealed the situation in terms of the social sustainability of the automotive industry companies in Turkey. Finally, there were evaluations of what kind of improvements should be made in the framework of social sustainability indicators based on the case study results
Centrality based solution approaches for median-type incomplete hub location problems
No full textHub location problems are of those main issues which are focused on by researchers from different aspects for the last three decades especially along with the growth of transportation networks in the world. However, several methods developed for addressing hub location problems do not perform well for large-scale networks because of their computational complexity. This study presents heuristic methodologies based on characteristic features which affect the design of incomplete hub networks. The main idea of this methodology is predicated upon the analysis of characteristics of hub locations. In this respect, the focus was placed on centrality measures, which are frequently used in social networks. Capacity constraints were not addressed, and single-allocation hub location problems were analyzed in this study focused on p-median problems which are the most common problems in the literature. Such characteristics of hub locations as the allocation across the distribution network distance and demand quantities were assessed based on centrality measures, and simple heuristic methods were developed. These methods evaluate nodes across distribution networks from diverse aspects such as centrality, distance, flow, and specify the level of importance of nodes to the networks. Candidate hub nodes are divided into sub-sets in terms of the level of importance and are inserted into the original model as constraints, and then the model is solved under these constraints, which are produced based on certain specifications. In order to test the performance of the proposed methods, data sets such as CAB, AP, URAND and TR which were frequently used in the literature were employed. It was ascertained that the proposed methodology provided good quality solutions in a short solution time. Moreover, the proposed method enables the detailed analysis of hub locations across the network and reduce the problem size. That being the case, it offers valuable opportunities in terms of both the quality of solutions and the solution time for p-hub median problems
An ant colony optimization approach for the proportionate multiprocessor open shop
No full textMultiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time
Water and energy minimization in industrial processes through mathematical programming: A literature review
No full textWater and energy are significant resources for industrial processes. While energy is the main source for the heating and cooling of water to be used in manufacturing processes, water is used to produce energy and as a cleaning agent in the production process, a pollution diluent or as part of the final product. This situation indicates that water and energy are intertwined in industrial processes and they should be considered simultaneously. Mathematical programming methods and pinch analysis are used for water and energy minimization in industrial processes. This paper depends on a comprehensive literature search of the databases of Google Scholar and the Web of Science for publications relating to water and energy minimization, heat integrated water network, and mathematical programming. This study presents a detailed overview on mathematical programming methods used in the literature regarding water network synthesis problems, their proposed approaches, and the improvements achieved through mathematical programming in sectoral case studies published from 2014 to 2019. In addition, heat integrated water network problems are examined according to isothermal and nonisothermal mixing. Several research gaps are also noted, with regards to the assumptions of the mathematical models, the sectoral applications of water network synthesis problems and sensitivity analysis of case studies. The paper ends with a critical analysis outlining the current situation and the need for future research for the integration of water and heat losses into the existing mathematical models. (C) 2020 Elsevier Ltd. All rights reserved
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