Browsing by Author "Samanlioglu, Funda"

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Citation Count: 0
A Bicriteria Model to Determine Pareto Optimal Pulse Vaccination Strategies
(Wiley, 2024) Samanlioglu, Funda; Tabassum, Nauman; Karaca, Tolga Kudret; Bilge, Ayse Humeyra
The aim of this paper is to determine approximate Pareto optimal (efficient) pulse vaccination strategies for epidemics modeled by the susceptible-infected-removed (SIR) without population dynamics, characterized by a single epidemic wave. Pulse vaccination is the application of the vaccination campaign over a limited time interval, by vaccinating susceptible individuals at a constant vaccination rate. A pulse vaccination strategy includes the determination of the beginning date and duration of the campaign and the vaccination rate. SIR with vaccination (SIRV) epidemic model is applied during pulse vaccination campaign, resulting in final proportions of removed (Rf) and vaccinated (Vf) individuals at the end of the epidemic. The burden of the epidemic is estimated in terms of Rf and Vf; two criteria are simultaneously minimized: vaccination cost and treatment cost of infected individuals and other economic losses due to sickness that are assumed to be proportional to Vf and Rf, respectively. To find approximate efficient solutions to this bicriteria problem, ODE and genetic algorithm toolboxes of MATLAB are integrated (GA-ODE). In GA-ODE, an augmented weighted Tchebycheff program is used as the evaluation function, calculated by solving the SIRV model and obtaining Rf and Vf values. Sample approximate efficient vaccination strategies are determined for diseases with a basic reproduction number (R0) 1.2 to 2.0. Consequently, obtained strategies are characterized as short-period campaigns that start as early as possible, i.e., as soon as vaccines are available and the vaccination rate increases with the severity of the disease (R0) and the importance weight given to minimization of Rf.
Citation Count: 0
Evaluation of Business Intelligence Tools for the Logistics Sector With Hesitant Fuzzy Hybrid Mcdm Methods
(Springer international Publishing Ag, 2024) Kup, Eyup Tolunay; Demir, Burcu; Gun, Alper; Samanlioglu, Funda; Gencay, Sevval Ece; Kocak, Gokcenur
Business intelligence (BI) tools have become essential for logistics companies due to their ability to facilitate improved decision-making and operational efficiency, as they provide comprehensive data analysis capabilities that contribute to more informed and timely decisions. This study utilizes the critical task of selecting the most suitable BI tool for an e-commerce logistics company, focusing on assessing various alternatives, including open-sourced and proprietary software. The research employs innovative integrated multi-criteria decision-making (MCDM) methods. The integrated approach primarily uses HFAHP to determine related criteria weights. Then, utilizing these determined weights, Hesitant Fuzzy Preference Ranking Organization Method for Enrichment Evaluation II (HF-PROMETHEE II), Hesitant Fuzzy Evaluation Based on Distance from Average Solution (HF-EDAS), and Hesitant Fuzzy Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form (HF-MULTIMOORA) methods are implemented to compare the alternatives. Five different Business intelligence tools were evaluated concerning eleven comprehensive criteria. This exhaustive evaluation, conducted by five business intelligence experts, aims to guide organizations in selecting an optimal BI tool that enhances data analysis, decision-making processes, and overall business efficiency.
Citation Count: 0
Predicting and Optimizing the Fair Allocation of Donations in Hunger Relief Supply Chains
(Elsevier, 2025) Sharmile, Nowshin; Nuamah, Isaac A.; Davis, Lauren; Samanlioglu, Funda; Jiang, Steven; Crain, Carter
Non-profit hunger relief organizations primarily depend on donors' benevolence to help alleviate hunger in their communities. However, the quantity and frequency of donations they receive may vary over time, thus making fair distribution of donated supplies challenging. This paper presents a hierarchical forecasting methodology to determine the quantity of food donations received per month in a multi-warehouse food aid network. We further link the forecasts to an optimization model to identify the fair allocation of donations, considering the network distribution capacity in terms of supply chain coordination and flexibility. The results indicate which locations within the network are under-served and how donated supplies can be allocated to minimize the deviation between overserved and underserved counties. (c) 2024 International Institute of Forecasters. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Citation Count: 0
Ranking Willingness To Reuse Water in Cotton Irrigation With Hybrid Mcdm Methods: Soke Plain Case Study
(Elsevier, 2024) Burak, Selmin; Samanlioglu, Funda; Ulker, Duygu; Kup, Eyup Tolunay
Soke Plain, located within the B & uuml;y & uuml;k Menderes River Basin is one of the highest producers of cotton in T & uuml;rkiye. The overall irrigation water supply is based on scarce conventional water resources that are being depleted at an increasing pace due to climate change impacts in B. Menderes. The inclusive objective of this research is to pave the way for a "water efficiency action plan" incorporating non-conventional (alternative) water resources for irrigation in Soke Plain to address adaptive management. Integrated Water Resources Management (IWRM) principles help decision makers (DMs) to identify and apply the most adequate alternatives among other possible ones in resource planning processes. Therefore, the preference ranking of DMs among possible water resource alternatives for irrigation is vital for implementation. This paper marks the first instance of using a multi-criteria decision-making (MCDM) method to evaluate both conventional and non-conventional water resource alternatives for cotton irrigation. The evaluation and ranking of water resource alternatives is processed using the hybrid MCDM method, integration of "Hesitant Fuzzy-Analytic Hierarchy Process" (HF-AHP) and "Hesitant Fuzzy Evaluation based on Distance from Average Solution" (HF-EDAS), namely HF-AHP-EDAS. This procedure implies several possibly contradictory qualitative and quantitative criteria, incorporates ambiguity, vagueness, and hesitancy in decision-makers' decisions, and achieves a consistent, dependable ranking of alternatives. Eight different water resources for irrigation are evaluated by 5 experts, for 15 assessment criteria, in Soke Plain. Conventional water resources blended with drainage water is concluded to be the best irrigation water resource alternative, with HF-AHP-EDAS and also with HF-AHP-PROMETHEE II (Preference Ranking Organization Method for Enriching Evaluations II), that is used for comparison analysis. This choice aligns well with the outlined arguments, culminating in an overall result deemed compliant with the field survey.
Citation Count: 0
A Two-Phase Pattern Generation and Production Planning Procedure for the Stochastic Skiving Process
(Hindawi Ltd, 2023) Karaca, Tolga Kudret; Samanlioglu, Funda; Altay, Ayca
The stochastic skiving stock problem (SSP), a relatively new combinatorial optimization problem, is considered in this paper. The conventional SSP seeks to determine the optimum structure that skives small pieces of different sizes side by side to form as many large items (products) as possible that meet a desired width. This study studies a multiproduct case for the SSP under uncertain demand and waste rate, including products of different widths. This stochastic version of the SSP considers a random demand for each product and a random waste rate during production. A two-stage stochastic programming approach with a recourse action is implemented to study this stochastic NP-hard problem on a large scale. Furthermore, the problem is solved in two phases. In the first phase, the dragonfly algorithm constructs minimal patterns that serve as an input for the next phase. The second phase performs sample-average approximation, solving the stochastic production problem. Results indicate that the two-phase heuristic approach is highly efficient regarding computational run time and provides robust solutions with an optimality gap of 0.3% for the worst-case scenario. In addition, we also compare the performance of the dragonfly algorithm (DA) to the particle swarm optimization (PSO) for pattern generation. Benchmarks indicate that the DA produces more robust minimal pattern sets as the tightness of the problem increases.