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Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.

The studies about Electric Vehicles (EV) have gained importance and increased in the last years depending on the environmental concerns of the classic transportation systems. One of the problems to consider at this point is locating the proper points of electric charging stations for EVs. The primary objective of this research is to locate the electric charging stations in Istanbul considering the flow of the paths. The locations of electric charging stations are determined by using a mathematical model based on the flow-refuelling location model with the aim of maximizing the captured flow. The mathematical model is run for various values of “p” and the optimum locations are obtained.

Klasik ulaştırma sistemlerinin çevresel kaygıları göz önünde bulundurulduğunda, elektrikli araçlarla (EA) ilgili çalışmalar önem kazanmış ve son yıllarda sayıca artmıştır. Bu noktada ele alınması gereken sorunlardan biri EA’lar için şarj istasyonlarının uygun yerlerinin belirlenmesidir. Bu çalışmanın temel amacı, İstanbul'daki elektrik şarj istasyonlarına ilişkin en uygun konumları yolların akışını değerlendirmeye alarak bulmaktır. Şarj istasyonlarının konumları, yolların kapsanan akışını maksimize etmeyi amaçlayan akış-yakıt ikmal lokasyon modeline dayanan bir model kullanılarak belirlenmiştir. Matematiksel model farklı “p” değerli için çözülmüş ve en iyi lokasyonlar belirlenmiştir.

Efficient planning and management of the workforce resources is one of the most essential requirements for the companies operating in the service sector. For banks, a large number of transactions coming to Central Operations Department from the branches or directly from the customers and their aim is to provide the best operational service with the highest efficiency with the limited workforce resources in the departments. In this study, a real assignment problem was discussed and the problem was considered as Generalized Assignment Problem. For the solution of the problem, related algorithms were listed and examined in the literature survey section. Then, a two-step method is proposed. First step prioritizes the task coming to the system by considering the customer types, service level agreement (SLA) times, cut-off times, task type. In the second step, a multi-objective mathematical model was developed to assign task to employee groups. A preference based optimization method called Linear Physical Programming (LPP) is used to solve the model. Afterward, proposed model was tested on real banking data. For all the tests, GAMS was used as a solver. Results show that proposed model gave better results compared with current situation. With the proposed solution method, the workloads of the profile groups working above their capacity were transferred to other profile groups with idle capacity. Thus, the capacity utilization rates of the profile groups were more balanced and the minimum capacity utilization rate was calculated as 41%.

Purpose: The purpose of this study is to design a responsive network for after-sale services of high-tech products. Design/methodology/approach: Analytic Hierarchy Process (AHP) and weighted max-min approach are integrated to solve a fuzzy goal programming model. Findings: Uncertainty is an important characteristic of reverse logistics networks, and the level of uncertainty increases with the decrease of the products’ life-cycle. Research limitations/implications: Some of the objective functions of our model are simplified to deal with non-linearities. Practical implications: Designing after-sale services networks for high-tech products is an overwhelming task, especially when the external environment is characterized by high levels of uncertainty and dynamism. This study presents a comprehensive modeling approach to simplify this task. Originality/value: Consideration of multiple objectives is rare in reverse logistics network design literature. Although the number of multi-objective reverse logistics network design studies has been increasing in recent years, the last two objective of our model is unique to this research area.

Logistics Centers (LCs) are among the most important facilities for rapid, cost effective, environmental friendly and secure logistics services to the urban logistics problems. Finding proper locations to LCs has an important effect on their success. In this study, LCs’ location selection problem for Istanbul Metropolitan area is investigated. Istanbul is a leading city of Turkey in many categories including various industries, import/export amounts, transportation infrastructure and population density. However, industrialization and urbanization have introduced numerous problems to Istanbul. New projects are continuously being announced for the problems of metropolis, however, they may also bring new problems if they are not punctiliously planned and applied. Hence, in this study, it is aimed to propose a systematic Multi-Criteria Decision-Making (MCDM) approach for the LCs’ location selection problem of Istanbul. In this approach, an integrated DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) and Intuitionistic Fuzzy (IF) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is utilized. DEMATEL is used to analyse the decision criteria and IF-TOPSIS is used to evaluate candidate locations for LCs. Considering the vagueness inherent to this decision-making problem, IF sets are used for the evaluation process. Results are compared with the results of IF-VIKOR (VIše Kriterijumska Optimizacija kompromisno Rešenje – multi-criteria optimization and compromise solution) technique. First publihed online 19 March 2020