Emir Žunić

Bio: Emir Žunić is an academic researcher from University of Sarajevo. The author has contributed to research in topics: Vehicle routing problem & Modular design. The author has an hindex of 3, co-authored 6 publications receiving 22 citations.

Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data

Abstract: Transportation management, as a part of the supply chain management, is a complex process that consists of planning and delivering goods to customers. The paper presents a complete multi-phase inte...

Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

Abstract: This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.

A Rule Based Events Correlation Algorithm for Process Mining

Abstract: Process mining is a technique for extracting process models from event logs. Process mining can be used to discover, monitor and to improve real business processes by extracting knowledge from event logs available in process-aware information systems. This paper is concerned with the problem of grouping events in instances and the preparation of data for the process mining analysis. Often information systems do not store a unique identifier of the case instance, or errors happen in the system during the recording of events in the log files. To be able to analyze the process, it is necessary that events are grouped into case instances. The aim of the presented rule based algorithm is to find events belonging to the same case instance. Performances of the algorithm, for different sizes of log file events and different levels of errors within log files in the real process, have been analyzed.

Innovative Modular Approach based on Vehicle Routing Problem and Ant Colony Optimization for Order Splitting in Real Warehouses

Abstract: A crucial part to any warehouse workflow is the process of order picking. Orders can significantly vary in the number of items, mass, volume and the total path needed to collect all the items. Some orders can be picked by just one worker, while others are required to be split up and shrunk down, so that they can be assigned to multiple workers. This paper describes the complete process of optimal order splitting. The process consists of evaluating if a given order requires to be split, determining the number of orders it needs to be split into, assigning items for every worker and optimizing the order picking routes. The complete order splitting process can be used both with and without the logistic data (mass and volume), but having logistic data improves the accuracy. Final step of the algorithm is reduction to Vehicle Routing Problem where the total number of vehicles is known beforehand. The process described in this paper is implemented in some of the largest warehouses in Bosnia and Herzegovina.

Two-Phase Approach for Solving the Rich Vehicle Routing Problem Based on Firefly Algorithm Clustering.

Abstract: The Vehicle Routing Problem (VRP) is an important optimization problem, the solution of which brings great savings to the company. Finding the optimal solution is significantly hampered by the introduction of realistic constraints such as time windows, capacity, customer-vehicle restrictions, and more. The paper presents a two-phase approach to solving the problem of vehicle routing with the fulfillment of several realistic conditions. The approach consists of customer clustering based on the firefly algorithm and process to solve rich VRP based on the created clusters. The algorithm was implemented in the real world and tested in some of the largest distribution companies in Bosnia and Herzegovina. The algorithm showed quality results in relation to the previously used methods, and in relation to the manual division of customers by the distribution manager.

Transporting COVID-19 testing specimens by routing unmanned aerial vehicles with range and payload constraints: the case of Istanbul

Abstract: This paper aims to emphasize the impacts of the COVID-19 pandemic at the healthcare logistics and supply chain networks and to transport polymerase chain reaction testing samples between hospitals ...

Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis

Abstract: With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.

The Prediction of Brent Crude Oil Trend Using LSTM and Facebook Prophet

Abstract: Ham petrol ve petrol urunleri, endustriyel uretimin onemli girdileri arasinda oldugu kadar lojistik ve tasimacilikta da kritik bir rol oynamaktadir. Dolayisiyla, petrol fiyatlarindaki ani artislar ve dususler kuresel ekonomilerde ve dahasi ekonomiler uzerinde dogrudan veya dolayli bir etkisi vardir. Ayrica, gelismekte olan ekonomilerdeki krizler, buyuk ekonomiler arasindaki ticaret anlasmazliklari ve petrol fiyatinin dinamik dogasi, petrol arz ve talebi uzerinde etkisi olmaktadir ve petrol fiyatinda zaman zaman oynaklik cok sert olmaktadir. Petrol fiyatlarindaki bu belirsizlikler hem tuketicilere hem de ureticilere agir potansiyel kayiplar yaratabilmektedir. Bu hizli degiskenlik ve dalgalanma nedeniyle petrol fiyatlarinin tahmin edilmesi kuresel oneme sahiptir. Bu calismada, Brent Petrol fiyatlarinin gelecekteki trendini tahmin edilebilmek icin gecmis degerleri girdi alan Uzun Kisa Sureli Bellek (LSTM) ve Facebook Prophet (FBPr) yontemleri kullanilmistir. Iki modelin petrol fiyatlari icin Haziran 1988 ile Haziran 2020 arasinda haftalik 32 yillik veri seti kullanilarak karsilastirilmis ve en uygun model belirlenmistir. Veri seti egitim ve test setleri olmak uzere iki gruba ayrilmistir; egitim seti icin ilk yirmi bes yil secilirken ve son yedi yil ise tahmin dogrulugunu onaylamak icin kullanilmistir. LSTM ve FBPr modelleri icin katsayi tayini (R2) egitim asamasinda 0.92, 0.89 ve test asamasinda 0.89, 0.62 bulunmustur. Elde edilen sonuclar incelendiginde, LSTM modelinin petrol fiyatlarindaki trendi tahmin etmek icin daha iyi sonuc verdigi gorulmustur.

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Abstract: Since December 2019, the novel coronavirus(COVID-19) has caused over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world, is now under community trans-mission of the COVID-19 outbreak. This has created huge health, social, and economic burdens. Till the 10th of February 2020, Bangladesh has reported over 500,000 infected cases and 8000 deaths. To prevent further detriment in our scenario, predicting future consequences are very important. Studies have shown that machine learning(ML) models work extremely well in providing precise information regarding COVID-19 to the authorities thus enabling them to make decisions accordingly. However, to the best of our knowledge, no ML models have been applied that can help in determining the pandemic circumstance for Bangladesh demographics. In this study, we explore different machine learning algorithms that can provide more accurate estimations for predicting future cases which includes infections and deaths due to COVID-19 for Bangladesh. Based on this the government and policymakers can make a decision about the lockdown, resource mobilization, etc. Our study shows that in predicting the pandemic situations, amidst many predicting models the Facebook Prophet Model provided the best accuracy. We believe that using this information the authorities can take decisions that will lead to the saving of countless lives of the people. Additionally, this will also help to reduce the immeasurable economic burden our country is facing due to the present status quo. Furthermore, this study will help analysts to construct predicting models for future explorations.