Browsing by Author "Hekimoğlu, Mustafa"

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Citation - WoS: 3
Citation - Scopus: 3
Admission control for a capacitated supply system with real-time replenishment information
(Elsevier, 2023) Hekimoğlu, Mustafa; Hekimoglu, Mustafa; Dekker, Rommert
Control towers can provide real-time information on logistic processes to support decision making. The question however, is how to make use of it and how much it may save. We consider this issue for a company supplying expensive spare parts and which has limited production capacity. Besides deciding on base stock levels, it can accept or reject customers. The real-time status information is captured by a k-Erlang distributed replenishment lead time. First we model the problem with patient customers as an infinite-horizon Markov decision process and minimize the total expected discounted cost. We prove that the optimal policy can be characterized using two thresholds: a base work storage level that determines when ordering takes place and an acceptance work storage level that determines when demand of customers should be accepted. In a numerical study, we show that using real-time status information on the replenishment item and adopting admission control can lead to significant cost savings. The cost savings are highest when the optimal admission threshold is a work storage level with a replenishment item halfway in process. This finding is different from the literature, where it is stated that the cost increase of ignoring real-time information is negligible under either the lost sales or the backordering case. Next we study the problem where customers are of limited patience. We find that the optimal admission policy is not always of threshold type. This is different from the literature which assumes an exponential production lead time.
Citation - WoS: 6
Citation - Scopus: 7
Assortment Optimization With Log-Linear Demand: Application at a Turkish Grocery Store
(Elsevier, 2019) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Sevim, İsmail; Aksezer, Çağlar Sezgin; Durmuş, İpek
In retail sector product variety increases faster than shelf spaces of retail stores where goods are presented to consumers. Hence assortment planning is an important task for sustained financial success of a retailer in a competitive business environment. In this study we consider the assortment planning problem of a retailer in Turkey. Using empirical point-of-sale data a demand model is developed and utilized in the optimization model. Due to nonlinear nature of the model and integrality constraint we find that it is difficult to obtain a solution even for moderately large product sets. We propose a greedy heuristic approach that generates better results than the mixed integer nonlinear programming in a reasonably shorter period of time for medium and large problem sizes. We also proved that our method has a worst-case time complexity of O(n 2 )while other two well-known heuristics’ complexities are O(n 3 )and O(n 4 ). Also numerical experiments reveal that our method has a better performance than the worst-case as it generates better results in a much shorter run-times compared to other methods. © 2019 Elsevier Ltd
Citation - Scopus: 0
Blockchain Technology in Loyalty Program Applications
(Association for Computing Machinery, 2022) Bozkurt,H.I.; Hekimoğlu, Mustafa; Gemici,S.; Yucekaya,A.; Hekimoglu,M.
Loyalty programs provide income to the user as a result of their expenditures. It helps businesses if the customer ensures continuity. A loyalty program is based on the bilateral relationship between the customer and the business. It is important to be sure of the reliability of this bilateral relationship and to be able to follow it. Today, many companies are trying to incorporate Blockchain technology into their systems because they pay attention to reliability and transparency. In this study, a loyalty ecosystem based on Blockchain technology, which will include member businesses, change the shopping and reward experience, and encourage employees is explained. We propose an application named Decentralized Loyalty Token (DLOT) to be used by entities where the community is together and the spending potential is high such as restaurants, cafes, and e-commerce platforms that want to be included in this structure. Users will earn rewards while spending DLOT Tokens and can store and accumulate the rewards indefinitely or use them for any payment. In addition, users will be able to convert the loyalty Tokens into a digital value in other businesses included in Wallet. The details of the proposed Blockchain-based loyalty system are explained along with benefits to customers and entities. © 2022 ACM.
Citation - Scopus: 0
A Comparative Application of Machine Learning Approaches To Win-Back Lost Customers
(Institute of Electrical and Electronics Engineers Inc., 2023) Yildirim, S.; Hekimoğlu, Mustafa; Yucekaya, A.D.; Hekimoglu, M.; Ozcan, B.
Today's consumer is more knowledgeable and conscious than in the past. For this reason, it is quite possible for consumers to leave their service/product providers and start receiving service from another service/product provider. Without a recovery strategy, companies often do not target their lost disloyal customer portfolio correctly and encounter the problem of lost customers. Lost customers can cause loss both in economic terms and in terms of business potential. At the same time, lost customers can also be considered as profits given to rival companies. What if the companies could foresee lost customers who would not want to receive service from them again? Could companies win back their customers? At this point, the article proposes using machine learning methods to recover lost customers for service providers. The customers that are likely to be lost in the future are estimated using the article's past stories of an automotive company's lost customers. The data used is completely real. LGBM, XGBoost, and Random Forest methods were used to estimate lost customers. Finally, the authors select the machine learning with the highest predictive success for customer recovery and discuss why this method might have worked well. © 2023 IEEE.
Citation - WoS: 0
Citation - Scopus: 0
Decoding Compositional Complexity: Identifying Composers Using a Model Fusion-Based Approach With Nonlinear Signal Processing and Chaotic Dynamics
(Pergamon-elsevier Science Ltd, 2024) Mirza, Fuat Kaan; Baykaş, Tunçer; Baykas, Tuncer; Hekimoğlu, Mustafa; Hekimoglu, Mustafa; Pekcan, Mehmet Önder; Pekcan, Onder; Tuncay, Gonul Pacaci
Music, a universal medium that effortlessly transcends the confines of language and culture, serves as a vessel for the distinctive expression of a composer's ingenuity, particularly palpable through the elaborate symphony of melodies, harmonies, and rhythms. This phenomenon is acutely observable in the realm of Turkish Classical Music, where the identification of individual composers poses a formidable challenge due to a confluence of diverse stylistic expressions and sophisticated techniques. Shaped by centuries of cultural interchanges, this genre is celebrated for its convoluted rhythmic frameworks and deep melodic modes, often exhibiting fractal characteristics that compound the complexity of composer classification based on mere audio signals. In response to these complexities, this study introduces an advanced analytical paradigm that amalgamates Multi-resolution analysis, spectral entropy assessments, and a spectrum of multidimensional chaotic and statistical descriptors. By invoking chaos theory, the research delineates distinct patterns and features inherent to musical compositions, subsequently deploying these discoveries for composer categorization. Employing a model fusion-based strategy, the approach utilizes esteemed base estimators for section-level probabilistic determinations, subsequently amalgamated at the song level through a Long Short-Term Memory (LSTM) neural network model to classify a corpus of 380 compositions from 15 distinct composers. The results of this study not only highlight the efficacy of chaos-based approaches in Musical Information Retrieval but also provide a nuanced understanding of the unique characteristics of Turkish Classical Music, thus advancing the boundaries of how musicological data is scrutinized and conceptualized within scholarly discourse.
Demand Classification for Spare Parts Supply Chains in the Presence of Three Dimensional Printers
(Kadir Has Üniversitesi, 2022) İşler, Zülal; Hekimoğlu, Mustafa; Hekimoğlu, Mustafa
Three-dimensional printers (3DPs) are currently the source of the supply chain and are used to ensure spare parts supply in case of shortages. However, the reliability of the part produced in 3DP is lower than the original part supplied by the original equipment manufacturer (OEM). Failure of parts creates demand and the failure probability of original and printed part is different than each other. Thus, knowing the total demand distribution have great importance in optimizing the order quantity given to the OEM in the presence of 3DPs. In this study, the demand distribution of system failures has been determined by using the distribution classification methods put forward by Ord (1967) and Adan et al. (1995). In line with the results, according to study of Ord(1967), demand distribution is found as Hypergeometric and Binomial distribution. Discrete distribution family of Adan et al. (1995) gives Binomial distribution for the system demand. All results are tested with chi-square test and likelihood ratio test.
Citation - WoS: 4
Citation - Scopus: 5
Dual Sourcing Models With Stock-Out Dependent Substitution
(Elsevier, 2023) Hekimoglu, Mustafa; Hekimoğlu, Mustafa; Scheller-Wolf, Alan
Companies use different criteria such as lead time, cost and quality to evaluate suppliers; often using multiple suppliers with the aim of reducing stockout risk. But in many industries there may be significant differences between the quality levels of different suppliers. Thus quality-sensitive companies may prefer an item from a primary supplier, but be forced to accept substitute products of lesser quality in case of a stock-out. Motivated by an example in the aviation industry, we introduce a Dual Sourcing problem With Stock-out dependent substitution (DSWS) which includes quality differences. Due to nonconvexity of the multi-period model, analytical characterization of the optimal policy appears intractable. To overcome this problem, we prove a relation between the optimal cost of DSWS and costs of three other problems -dual sourcing without substitution and single sourcing problems with and without backlogging. This leads us to propose the use of the dual index policy (and a variant) as heuristics for DSWS, and to develop an algorithm for parameter optimization of our heuristics. Extensive numerical experiments show that the dual index policy outperforms all other candidate solutions from the literature by at least 8%. Our experiments show that the utilization of the back-up supplier leads to substantial cost savings and service rate increase, especially in case of high differences between holding cost rates of different quality items.& COPY; 2023 Elsevier B.V. All rights reserved.
Evaluation of Various Machine Learning Methods To Predict Istanbul’s Freshwater Consumption
(2023) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Çetin, Ayse Irem; Kaya, Burak Erkan
Planning, organizing, and managing water resources is crucial for urban areas and metropolitans. Istanbul is one of the largest megacities, with a population of over 15 million. The large volume of water demand and increasing scarcity of clean water resources make long-term planning necessary for this city, as sustained water supply requires large-scale investment projects. Successful investment plans require accurate projections and forecasting for freshwater demand. This study considers different machine learning methods for freshwater demand forecasting for Istanbul. Using monthly consumption data provided by the municipality since 2009, we compare forecasting accuracies of ARIMA, Holt-Winters, Artificial Neural Networks, Recursive Neural Networks, Long-Short Term Memory, and Simple Recurrent Neural Network models. We find that the monthly freshwater demand of Istanbul is best predicted by Multi-Layer Perceptron and Seasonal ARIMA. From the predictive modeling perspective, this result is another indication of the combined usage of conventional forecasting models and novel machine learning techniques to achieve the highest forecasting accuracy.
Citation - WoS: 30
Citation - Scopus: 35
Evaluation of Water Supply Alternatives for Istanbul Using Forecasting and Multi-Criteria Decision Making Methods
(Elsevier Ltd, 2020) Savun Hekimoğlu, Başak; Hekimoğlu, Mustafa; Erbay, Barbaros; Hekimoğlu, Mustafa; Burak, Selmin
Water scarcity is one of the most serious problems of the future due to increasing urbanization and water demand. Urban water planners need to balance increasing water demand with water resources that are under increasing pressure due to climate change and water pollution. Decision makers are forced to select the most appropriate water management alternative with respect to multiple, conflicting criteria based on short and long term projections of water demand in the future. In this paper, we consider water management in Istanbul, a megacity with a population of 15 million. Purpose: The purpose of this paper is to develop a method combining demand forecasting with multi-criteria decision making (MCDM) methods to evaluate five different water supply alternatives with respect to seven criteria using opinions of experts and stakeholders from different sectors. Methodology: To combine forecasting with MCDM, we design a data collection method in which we share our demand forecasts with our experts. For demand forecasting, we compare Holt-Winters, Seasonal Autoregressive Integrated Moving Average (S-ARIMA), and feedforward Artificial Neural Network (ANN) models and select S-ARIMA as the best forecasting model for monthly water consumption data. Generated demand projections are shared with experts from different sectors and collected data is evaluated with Fuzzy Theory using two distinct MCDM models: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). Also our analyses are complemented with two sensitivity analyses. Findings: Our results indicate that greywater reuse is the best alternative to satisfy the growing water demand of the city whereas all experts find desalination and inter-basin water transfer as the least attractive solutions. In addition, we adopt the PROMETHEE GDSS procedure to obtain a GAIA plane indicating consensus among experts. Furthermore, we find that our results are moderately sensitive to the number of experts and they are insensitive to changes in experts’ evaluations. Novelty: To the best of our knowledge, our study is the first one incorporating water demand and supply management concepts into the evaluation of alternatives. From a methodological perspective, water demand projections have never been used in an MCDM study in the literature. Also, this paper contributes to the literature with a mathematical construction of consensus and Monte Carlo simulations for the sufficiency of experts consulted in a study.
Citation - Scopus: 5
Forecasting Hourly Electricity Demand Under Covid-19 Restrictions
(Econjournals, 2022) Kök, A.; Yücekaya, Ahmet Deniz; Yükseltan, E.; Bilge, Ayşe Hümeyra; Hekimoğlu, M.; Hekimoğlu, Mustafa; Aktunc, E.A.; Yücekaya, A.; Bilge, A.
The rapid spread of the COVID-19 pandemic has severely impacted many sectors including the electricity sector. The restrictions such as lockdowns, remote-working, and-schooling significantly altered the consumers’ behaviors and demand structure especially due to a large number of people working at home. Accurate demand forecasts and detailed production plans are crucial for cost-efficient generation and transmission of electricity. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the impact of the restrictions on total demand using a multiple linear regression model. In addition, the model is utilized to forecast the electricity demand in pandemic conditions and to analyze how different types of restrictions impact the total electricity demand. It is found that among three levels of COVID-19 restrictions, age-specific restrictions and the complete lockdown have different effects on the electricity demand on weekends and weekdays. In general, new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches as COVID-19 significantly changed the consumer behavior, which appears as altered daily and weekly load profiles of the country. Long-term policy implications for the energy transition and lessons learned from the COVID-19 experience are also discussed. © 2022, Econjournals. All rights reserved.
Citation - Scopus: 0
A Framework To Forecast Electricity Consumption of Meters Using Automated Ranking and Data Preprocessing
(Econjournals, 2023) Guzel, T.; Hekimoğlu, Mustafa; Çınar, H.; Çenet, M.N.; Oguz, K.D.; Yucekaya, A.; Hekimoglu, M.
Forecasting electricity consumption is crucial for the operation planning of distribution companies and suppliers and for the success of deregulated electricity markets as a whole. Distribution companies often need consumption forecasting for meters to better plan operations and demand fulfillment. Although it is easier to forecast the aggregated demand for a region, meter based demand forecasting brings challenging issues such as non-uniform usage and uncertain customer consumption patterns. The stochastic nature of the demand for electricity, along with parameters such as temperature, humidity, and work habits, eventually causes deviations from the expected demand. In this paper, real meter data from a regional distribution company is used to cluster the customer using their non-uniform usage and automated ranking mechanism is proposed to select the best method to forecast the consumption. The proposed end-to-end methodology includes data processing, missing value detection and filling, abnormal value detection, and mass reading for meters and is applied to regional data for the period 2017-2018 and provides a powerful tool to forecasts the demand in hourly and daily horizons using only the past demand data. Besides proposing effective methodologies for data preprocessing, 10 different regression methods, 7 regressors, 5 machine learning methods that include LSTM and Ar-net models are used to forecast the meter based consumption. The hourly forecasting errors in the demand, in the Mean Absolute Percentage Error (MAPE) norm, are <4% for most customer groups. The meter based forecast is then aggregated to reach a final demand which is then used for operation and demand planning. The proposed framework can be considered reliable and practical in the circumstances needed to make demand and operation decisions. © 2023, Econjournals. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 17
The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey
(Elsevier Sci Ltd, 2022) Bilge, Ayşe Hümeyra; Hekimoğlu, Mustafa; Yucekaya, A.; Bilge, A.; Aktunc, E. Agca; Hekimoglu, M.
The rapid spread of COVID-19 has severely impacted many sectors, including the electricity sector. The reliability of the electricity sector is critical to the economy, health, and welfare of society; therefore, supply and demand need to be balanced in real-time, and the impact of unexpected factors should be analyzed. During the pandemic, behavioral restrictions such as lockdowns, closure of factories, schools, and shopping malls, and changing habits, such as shifted work and leisure hours at home, significantly affected the demand structure. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the estimated impact of the restrictions on total demand and daily demand profile. A modulated Fourier Series Expansion evaluates deviations from normal conditions in the aggregate demand and the daily consumption profile. The aggregate demand shows a significant decrease in the early phase of the pandemic, during the period March-June 2020. The shape of the daily demand curve is analyzed to estimate how much demand shifted from daytime to night-time. A population-based restriction index is proposed to analyze the relationship between the strength and coverage of the restrictions and the total demand. The persistency of the changes in the daily demand curve in the post-contingency period is analyzed. These findings imply that new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches in the future. The longterm policy implications for the energy transition and lessons learned from the COVID-19 pandemic experience are also presented.
Citation - WoS: 2
Citation - Scopus: 3
The Implementation of Smart Contract via Blockchain Technology in Supply Chain Management: A Case Study from The Automotive Industry in Turkey
(IEEE, 2021) Hekimoğlu, Mustafa; Yücekaya, Ahmet Deniz; Bozkurt, Hayreddin; Yucekaya, Ahmet; Hekimoglu, Mustafa
Blockchain Technology, underlined as the most revolutionizing innovation after the internet, is still in the growth phase and waits for the practitioners to enlighten its productivity promises. In the current environment, volatile profits require a more digitalized work experience and competitive advantages to get ahead in such a highly competitive automotive industry and innovative applications that lead to more simplified operation management. Accordingly, this paper aims to present a case study via use cases in which Blockchain has been used and smart contract as the sought-out innovation and its application for the digitized spare parts disposal legal process. Blockchain Technology in the automotive sector is discussed by focusing on the supply management process of an automotive company's processes in Turkey. Blockchain technology is expected to develop and simplify spare parts-related transactions in the automotive industry, which deals with more than 500K stock keeping units per company. Paper presents the current, future, and ideal states of spare parts transactions with Blockchain adoption. The implemented application enables the development of an enterprise-level blockchain platform with hyper-ledger fabric as an open-source. The distributed ledger technology provides a smart contract system between actors of the existed supply chain process. The study aims to show the potential of Blockchain Technology in delivering a high degree of competitive advantage especially for automotive service providers with regards to its features related to providing security, transparency, traceability, cost reduction, more efficient data storage in dense supply based industries.
Citation - WoS: 20
Citation - Scopus: 20
Maintenance Optimization for a Single Wind Turbine Component Under Time-Varying Costs
(Elsevier, 2022) Schouten, Thijs Nicolaas; Hekimoğlu, Mustafa; Dekker, Rommert; Hekimoglu, Mustafa; Eruguz, Ayse Sena
In this paper, we introduce a new, single-component model for maintenance optimization under timevarying costs, specifically oriented at offshore wind turbine maintenance. We extend the standard age replacement policy (ARP), block replacement policy (BRP) and modified block replacement policy (MBRP) to address time-varying costs. We prove that an optimal maintenance policy under time-varying costs is a time-dependent ARP policy. Via a discretization of time, the optimal time-dependent ARP can be found using a linear programming formulation. We also present mixed integer linear programming models for parameter optimization of BRP and MBRP. We present a business case and apply our policies for maintenance planning of a wind turbine gearbox and show that we can achieve savings up-to 23%.(c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Citation - Scopus: 2
Markdown Optimization in Apparel Retail Sector
(Springer Science and Business Media B.V., 2020) Yıldız, S.C.; Hekimoğlu, Mustafa; Hekimoğlu, M.
Price discounts, known as markdowns, are important for fast fashion retailers to utilize inventory in a distribution channel using demand management. Estimating future demand for a given discount level requires the evaluation of historical sales data. In this evaluation recent observations might be more important than the older ones as majority of price discounts take place at the end of a selling season and that time period provides more accurate estimations. In this study, we consider a weighted least squares method for the parameter estimation of an empirical demand model used in a markdown optimization system. We suggest a heuristic procedure for the implementation of weighted least squares in a markdown optimization utilizing a generic weight function from the literature. We tested the suggested system using empirical data from a Turkish apparel retailer. Our results indicate that the weighted least squares method is more proper than the ordinary least squares for the fast fashion sales data as it captures price sensitivity of demand at the end of a selling season more accurately. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Citation - WoS: 1
Citation - Scopus: 1
Markdown Optimization with Generalized Weighted Least Squares Estimation
(Springernature, 2022) Hekimoğlu, Mustafa
Retailers increasingly apply price markdowns for their seasonal products. Efficiency of these markdown applications is driven by the accuracy of empirical models, especially toward the end of a selling season. In the literature, recent sales are recognized to be more important than older sales data for estimating the current period's demand for a given markdown level. The importance difference between the weeks of a selling season is addressed by weighted least squares (WLS) method with continuous weight functions of time. This study suggests a generalization of the weight functions and a method for optimizing their shape and discretization parameters to stimulate the predictive accuracy of models. We find that addressing the importance difference of recent sales observations using our generalized weight functions improves the forecast accuracy by up to 20%, and most of the improvement stems from our weight discretization method.
Citation - WoS: 8
Citation - Scopus: 13
Markov-Modulated Analysis of a Spare Parts System With Random Lead Times and Disruption Risks
(Elsevier Science Bv, 2018) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; van der Laan, Ervin; Dekker, Rommert
Spare parts supply chains are highly dependent on the dynamics of their installed bases. A decreasing number of capital products in use increases the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk appears to present itself through varying lead times coupled with supply disruptions. To model the nonstationary supply-side risk we consider an exogenous Markov chain that modulates random lead times and disruption probabilities. Assuming that order crossovers do not occur we prove the optimality of a state-dependent base stock policy. Later we conduct an impact study to understand the value of considering stochastic lead times and supply disruption risk in spare parts inventory control. Our results indicate that the coupled effect of random lead times and disruptions can be larger than the summation of individual effects even for moderate lead time variances. Also the effect of nonstationarity on total cost can be as large as the summation of all risk factors combined. In addition to this managerial insight we present a procedure for supply risk mitigation based on an empirical model and our mathematical model. Experiments on a real business case indicate that the procedure is capable of reducing costs while making the inventory system more prepared for disruptions. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
Modeling Repair Demand in Existence of a Nonstationary Installed Base
(Elsevier, 2023) Hekimoglu, Mustafa; Hekimoğlu, Mustafa; Karli, Deniz
Life cycles of products consist of 3 phases, namely growth, maturity, and decline phases. Modeling repair demand is particularly difficult in the growth and decline stages due to nonstationarity. In this study, we suggest respective stochastic models that capture the dynamics of repair demand in these two phases. We apply our theory to two different operations management problems. First, using the moments of spare parts demand, we suggest an algorithm that selects a parametric distribution from the hypergeometric family (Ord, 1967) for each period in time. We utilize the algorithm in a single echelon inventory control problem. Second, we focus on investment decisions of Original Equipment Manufacturers (OEMs) to extend economic lifetimes of products with technology upgrades. Our results indicate that the second moment is sufficient for growing customer bases, whereas using the third moment doubles the approximation quality of theoretical distributions for a declining customer base. From a cost minimization perspective, using higher moments of demand leads to savings up to 13.6% compared to the single-moment approach. Also, we characterize the optimal investment policy for lifetime extension decisions from risk-neutral and risk-averse perspectives. We find that there exists a critical level of investment cost and installed base size for profitability of lifetime extension for OEMs. From a managerial point of view, we find that a risk-neutral decision maker finds the lifetime extension problem profitable. In contrast, even a slight risk aversion can make the lifetime extension decision economically undesirable.
Multi-Criteria Decision-Making Analysis for the Selection of Desalination Technologies
(2022) Hekimoğlu, Mustafa; Hekimoğlu, Mustafa; Savun-hekimoğlu, Başak; Erbay, Barbaros; Gazioğlu, Cem
Accessible fresh water resources for drinking and usage are very limited in our world. Furthermore, these limited fresh water resources are gradually decreasing due to climate change, industrialization, and population growth. Despite the ever-increasing need for water, the inadequacies in our resources have made it critical to develop alternative drinking and utility water production methods. Desalination, one of the most important alternatives for fresh water supply, is on the rise on a global scale. Desalination facilities use various thermal and membrane techniques to separate water and salt. Concentrated brine, which contains desalination chemicals and significant amounts of salt, and is formed in high volumes from desalination processes, is also a concern. This article compares various desalination techniques using a multi-criteria decision-making method. The findings show that the Reverse Osmosis & Membrane Crystallization process is the most preferred technology due to its cost advantages as well as operational efficiency. Similarly, Multistage flash &Electrodialysis, the least preferred alternative, has been criticized for its low cost-effectiveness. These results suggest that cost and operational efficiency will continue to be the main drivers in the evaluation of desalination technologies in the near future.
Citation - WoS: 0
Citation - Scopus: 0
On spare parts demand and the installed base concept: A theoretical approach
(Elsevier, 2023) Hekimoğlu, Mustafa; Frenk, J. B. G.; Hekimoglu, Mustafa
Original Equipment Manufacturers (OEMs) aim to design their service supply chain before the introduction of their products to maximize their aftersales business revenues, reduce waste and achieve sustainability. In this study, we develop a stochastic model that unifies the installed base, i.e., the number of products in use, spare parts demand, and the number of discarded products within a single modeling framework based on three product characteristics: sales rate, usage time, and failure rate. Our model describes the installed base and spare part demand evolution over the entire life cycle of a parent product using stochastic point processes. At the same time we propose under very general assumptions on the cdf of the usage time and the mean arrival functions of the sales and failure processes an easy bisection procedure to compute the time at which the expected installed base and rate of the expected demand for spare parts is maximal. Our numerical experiments show that the volume of aftersales services increases in the expected usage time if the products face an increasing failure rate. The same experiments also reveal a 20 percent shift of the time at which the expected installed base is maximal in case the expected usage time is increased threefold. At the same time, we observe a boosting effect of the intensity of the sales process on this point in time.