Showing papers by "Yaşar University published in 2022"

Parallel Workforce Assignment Problem for Battery Production

Abstract: This paper studies workforce assignment problem for battery production in a company in Turkey. Several types of batteries are produced in the studied company. Mostly, the operations are semi-automated. In the production process, the workers are assigned to multiple operations irregularly based on the priority of productions. In the company, average utilization of worker is low, and average cycle time of a product is high due to inefficient allocation of the workforce within the operations. In order to analyze the main system problem, we simulate the system and observe the queue lengths to identify the bottlenecks. By dynamic assignment of workers at stations based on real time queue conditions, the workloads can be balanced throughout the production lines. In this project, a simulation-based system improvement is completed by applying: (i) dynamic utilization of workforce to reduce average cycle time of a battery, (ii) assignment of parallel workforce where they can work for the same operation simultaneously, and (iii) observation of real-time queue lengths of stations. Three dynamic assignment policies are developed and compared with each other. The best policy providing minimum cycle time for a battery production is selected to be the best.

Single and multigeneration Rankine cycles with aspects of thermodynamical modeling, energy and exergy analyses and optimization: A key review along with novel system description figures

Abstract: The energy crises caused by the rapidly increasing population density around the world and the economic, environmental and health threats, that have reached significant dimensions, emphasize the importance of the concept of sustainable energy more and more every day. For this reason, to ensure the sustainability of energy, not only sustainable energy sources, but also optimum system designs are developed, which can be integrated with these sources and where waste heat recovery mechanisms are effective. At this point, Rankine cycle systems (RCSs) are an extremely good opportunity to close this gap due to their structural features. In this study, we grouped the RCSs existing in the literature and made a comprehensive evaluation of these systems from broad perspectives, such as exergo-economics, exergoenvironment, optimization, and system design, results and effects. The potential system designs revealed by compiling studies in the literature for the system type in question, through the novel general system description figures, were drawn with a completely original approach. For comparing the system designs in the studies examined with each other more easily and seeing the possible effects, the systems considered were originally and completely drawn by the authors of this paper, based on the principle of standardization. We summarized the examined study in terms of system design, the applied energy, exergy, economic analyzes and optimization processes, the obtained general results, input and output parameters affecting the system and their interaction with system components in a single table with novel flow chart tables. We presented an effective, easy-to-understand comparison method based on a strong visualization principle and expect that in this way one can inspire potential studies and understand easily the gaps in the literature. Also, we prepared a novel comprehensive comparison table for all types of RCSs in terms of techno-economic and environmental considerations. The main findings indicated that the maximum power, heating and cooling output rates, thermal and exergy efficiency values, mean total production cost rate, payback period and greenhouse gas emission ranges were 1040–329750 kW, 15.2–2500 kW, 567–22500 kW, 12.8–73.8%, 51.6–75.5%, 85.39 $/h, 3.6 years and 0.098 t/MWh, respectively, for cogeneration RCSs. The mean greenhouse gas emission, total production cost rate and payback period values were lower compared to other RCS types with higher exergy efficiencies and production outputs. It may be concluded that in terms of exergoeconomic and environmental perspectives, cogeneration RCSs form a better optimum configuration for many cases with utilization of influent waste heat recovery opportunities compared to other choices.

A Literature Review on Supplier Selection Problem and Fuzzy Logic

Abstract: Given the recent increasing competition in global market, supplier selection and evaluation has attracted a great deal of attention especially at academic levels. Supplier selection problem is a complex problem since there exist a great number of unpredictable and uncontrollable factors which have a huge impact on decision-making process. Due to this complexity, there are several criteria that must be taken into consideration such as cost, quality, on-time delivery, proximity of suppliers, long-term relationship etc. Although some of these criteria (quantitative) can be expressed using pure numeric scales, some (qualitative) are linguistic due to the human assessments which contain some degree of subjectivity. Since involvement of human assessment causes vagueness for deterministic models, the authors apply fuzzy logic which enables the decision makers to be able to convert their linguistic expressions into fuzzy numbers with the help of fuzzy membership functions. Considering that fuzzy logic plays a vital role in solving multi-criteria supplier selection problem, this paper aims to present a review of supplier selection problem and its relation with fuzzy logic. In this paper, several studies that highlight supplier selection problem and the importance of fuzzy logic involvement in the problem have been reviewed. An analysis of multi-criteria decision-making methods for supplier selection problem is conducted.

Spare Parts Inventory Management System in a Service Sector Company

Abstract: Spare parts inventory management is crucial in the success of a service providing company. In this study, the spare parts of a service provider in the automotive sector are classified according to their characteristics in groups and different inventory control policies are applied to the categorized groups. The Analytical Hierarchy Process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods, is used to classify the spare parts into groups. As a result of the application of AHP, classes of spare parts are determined according to the VED analysis, classifying the spare parts according to their criticality. Furthermore, the ABC analysis performed by the company was improved by using cost and demand criteria. After performing both analysis, three new classes of spare parts are determined with the combination of ABC and VED classification techniques. For each class, an appropriate inventory control policy is decided according to the spare parts importance and criticality. Based on the literature review, the \(({\varvec{R}},{\varvec{S}},{\varvec{s}})\) inventory control policy is chosen to be applied in each class, taking into consideration the review period, order up-to-level and reorder point of items. In the inventory control model, the review period for the same class items is assumed to be constant based on the information provided by the company. For verification purposes, necessary cost calculations including total ordering and holding costs are performed by means of Microsoft Excel. In order to be able to vastly observe the system behavior, different cost scenarios are generated by increasing and decreasing the service level and review period of the system. Using, OptQuest, an optimization tool, embedded into ARENA simulation software, the different scenarios were analyzed and the total minimum cost is reached. For supporting the daily operations of the company, a user-friendly decision support system is built, where the end-user can easily add/remove spare parts to/from the system, classify them and compare the results of inventory control policies with the current system. The DSS will also assist the company to manage and control their real-time inventory and perform spare parts stock level tracking and decide when to place orders.

AI and Quality of Service Driven Attack Detection, Mitigation and Energy Optimization: A Review of Some EU Project Results

Abstract: Abstract This article summarizes briefly the contributions presented in this EuroCyberSecurity Workshop 2021 which is organized as part of the series of International Symposia on Computer and Information Sciences (ISCIS), with the support of the European Commission funded IoTAC Project, that was held on November and in NIce, France, and sponsored by the Institute of Teoretical and Applied Informatics of the Polish Academy of Sciences. It also summarizes some of the research contributions of several EU Projects including NEMESYS, GHOST, KONFIDO, SDK4ED and IoTAC, primarily with a cybersecurity and Machine Learning orientation. Thus subjects covered include the cybersecurity of Mobile Networks and of the Internet of Things (IoT), the design of IoT Gateways and their performance, the security of networked health systems that provide health services to individuals across the EU Member states, as well as the issues of energy consumption by ICT which are becoming increasingly important, including in the cybersecurity perspective, as we focus increasingly on climate change and the needed transition towards highly reduced emissions. Many of the techniques and results discussed in this article are based either on Machine Learning (ML) methods, or on methods for the performance modeling and optimization of networked and distributed computer systems.

Shipment Planning: A Case Study for an Apparel Company

Abstract: This study includes the analysis of the shipment planning problem for a large textile company in Turkey. The aim is to develop an effective solution method to the problem and thus reduce the shipping costs associated with internal logistics operations. A formal definition of the problem is made and all the parameters and inputs are clearly explained and determined. An original mixed-integer programming model has been proposed to solve the problem although it is not practical in real-world problems since it requires a very long time to get the optimal solution. Therefore, a heuristic method is developed to obtain near-optimal solutions. The performance of the heuristic method has been tested and verified. A simple decision support system with user-friendly interfaces has been developed to implement the heuristic method as a practical shipment planning tool.

Drought Modelling Using Artificial Intelligence Algorithms in Izmir District

Abstract: The world's water resources are decreasing day by day due to factors such as climate change, drought, inefficient pricing policies implemented by the government, population growth, uncontrolled water consumption, technological developments, and industrialization. A decrease in water resources causes water scarcity in the long-term period. This study is conducted to analysis the meteorological drought, in Izmir district, Turkey. Inspired by the real-life problem, drought estimation models are developed through artificial neural network-based artificial intelligence techniques incorporating a decision support system. The Z-score index (ZSI) values are computed using precipitation data collected from five meteorological station in Kucuk Menderes basin, and several developed models are compared according to the variety of statistical performance metrics.

Capacitated Vehicle Routing Problem with Time Windows

Abstract: Since distribution activities have great importance for firms, supply management is a widely studied concept in many sectors. This study demonstrates an application of a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). The problem is in the form of a fixed destination multi depots visited by multi-travelling salesmen and the distance-travel time matrix is assumed to be asymmetric. The objective of the problem is to minimize the longest route time of each vehicle. This is achieved by developing a mixed-integer linear programming model (MILP) for the problem. Additionally, since the problem is NP-hard, a general heuristic method is developed to solve the problem for larger instances in negligible computational times. Results show that the balance between the individual route times of the vehicles is provided and the time window limit is ensured. The paper also discusses the results and presents concluding remarks.

Uniform Parallel Machine Scheduling with Sequence Dependent Setup Times: A Randomized Heuristic

Abstract: We consider the uniform parallel machine scheduling problem with sequence-dependent setup times to minimize the total completion times. This problem is known to be NP-hard. We propose a simple randomized heuristic with an improvement subroutine. We analyze the performance of the proposed heuristic through a computational study. Our computational study indicates that the heuristic performs well in terms of optimality gap and solution time .