Browsing by Author "Karaca, Tolga Kudret"

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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: 1
Solution approaches for the bi-objective Skiving Stock Problem
(Pergamon-Elsevier Science Ltd, 2023) Karaca, Tolga Kudret; Samanlıoğlu, Funda; Altay, Ayca
The Skiving Stock Problem (SSP) aims to determine an optimal plan for producing as many large objects as possible by combining small items. The skiving process may need different considerations depending on the production environment and the product characteristics. In this study, we address bi-objective 1D-SSP with two conflicting objectives. One common objective is to minimize the trim loss remaining after skiving, as removing the excess width is an extra procedure. When welding is an element of the skiving process, increasing the number of items for each product indicates compromised quality. Therefore, minimizing the number of small items for each product becomes a primary objective in such cases. To solve this bi-objective version of the NP-hard problem, we implement a Lexicographic Method (LM) in which the importance of the objectives imposes their preference orders. We propose two methodologies within the LM framework. The first methodology integrates Column Generation (CG) and Branch & Bound (B&B) to search for an exact solution. Given the excessive computational time an exact solver may require for tight or large-sized problems, we propose a heuristic method integrating the Dragonfly Algorithm (DA) and a Constructive Heuristic (CH). Real-world application results validate the exact solver and demonstrate comparable results for the heuristic solver in terms of solution quality and computational time. The efficiency of the solution methodologies for a preemptive multi-objective SSP aims to support decision-makers with make-or-buy decisions.
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.