Organizational Unit: Department of Industrial Engineering
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Master ThesisPublication Metadata only An integrated post-disaster assessment routing problem for collecting damage information with dronesAdsanver, Birce; Göktürk, Elvin Çoban; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Albey, Erinç; Yanıkoğlu, İhsan; Yıldırım, U. M.; Department of Industrial Engineering; Adsanver, BirceIn this study, we focus on post-disaster damage assessment operations supported by a set of drones when the disaster-affected area is divided into grids, and grids are clustered based on their attributes. We propose a two-phase methodology to assess the damage status of the built environment in grids. Specifically, given a set of drones and a limited time for an assessment interval, the first phase addresses the problem of determining the grids to scan by each drone and the sequence of visits to the selected grids. We aim to maximize the total priority score collected from the scanned grids while satisfying the predefined targeted coverage ratio. In the second phase, we aim to predict the damage status of unscanned grids by using the cluster-based information obtained from the scanned grids at the end of the assessment interval. Nevertheless, the damage status of all grids may not be assessed (by scanning or prediction) after one interval; therefore, these two phases iterate until all grids are evaluated. For the problem solved in the first phase, we adapt two formulations from the literature developed for electric vehicle routing problems. We also develop a Variable Neighborhood Descent based heuristic which can find high-quality solutions rapidly. We evaluate the performance of the alternative formulations and the heuristic in a variety of instances. For the second phase, we devise a novel imputation method and different imputation policies to predict the damage status of the unscanned grids. We also define several performance metrics to measure the efficiency and effectiveness of the proposed imputation policies. Our analyses demonstrate that using the proposed imputation policies improve the system performance as they induce a rapid detection of the damaged areas.Master ThesisPublication Metadata only An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronic(2017-08) Sarıkaya, Merve Burcu; Özener, Okan Örsan; Özener, Okan Örsan; Ekici, Ali; Duran, S.; Department of Industrial Engineering; Sarıkaya, Merve BurcuVestel Electronics produces LCD/LED televisions and has a significant market share in consumer electronics sector in Europe. TV manufacturing is planned based on a make-to order strategy, and Vestel uses 15 assembly lines to produce 110 different product groups and 3817 different models. Once the orders are received, production scheduling is performed at the beginning of each month, and the goal is to satisfy the demand on time as much as possible. Each order/job is processed on one of the compatible assembly lines, and preemption is not allowed. In this thesis, we study the TV production scheduling operations at Vestel. The problem faced by Vestel is a variant of unrelated machine scheduling problem, and the objective is to minimize total tardiness. We propose a wide range of heuristics including a very simple sequential algorithm and a novel set partitioning-based approach. We test the heuristics on the real-life data and compare the solutions with the current practice. We observe up to 50% improvement in total tardiness. Keywords: parallel machine scheduling; unrelated machines; sequence-dependent setups; tardinessMaster ThesisPublication Metadata only Coordinated inventory planning for humanitarian relief agencies(2016-07) Karaca, Meserret; Özener, Okan Örsan; Koyuncu, Burcu Balçık; Özener, Okan Örsan; Koyuncu, Burcu Balçık; Özen, Ulaş; Ekici, Ali; Duran, S.; Department of Industrial Engineering; Karaca, MeserretPre-positioning relief supplies in strategic locations around the world is essential for effective disaster response, especially during the critical 72 hours immediately following the disaster. Most of the existing studies that use quantitative models to determine pre-positioning decisions focus on a single relief agency and assume that the agency makes stock pre-positioning decisions independently of other agencies; that is, the possibility of sharing inventory among different agencies is not considered. In this study, we aim to investigate the potential benefits of making stock pre-positioning decisions collaboratively among multiple agencies. In particular, we consider two agencies that stock relief supplies in a joint depot owned and operated by a separate coordinator (such as the United Nations Humanitarian Response Depot). We assume that these agencies have several operating regions throughout the world. Each operating region may be served by a single agency or by several different agencies depending on its location. Once a disaster occurs in an agency's operating region, the agency aims to satisfy demand as much as possible. Also, other agencies may share their excess inventory with the responding agency. The amount of supplies that can be sent to the disaster region is affected by the uncertain post-disaster funding level of the responding agency. We consider a finite set of scenarios to characterize the uncertainties in disaster locations, impacts and post-disaster funding levels and develop a two-stage stochastic programming model to determine the amount of inventory to be pre-positioned at the joint depot by each agency. We perform a numerical analysis to establish when collaborative action would be beneficial for different types of agencies in different settings.Master ThesisPublication Metadata only Creating an evacuation plan during an emergency by coordinating vehicles(2018-10) Bacaksız, Hazal; Ekici, Ali; Ekici, Ali; Günneç, Dilek; Moğulkoç, H. T.; Department of Industrial Engineering; Bacaksız, HazalAs disaster relief operations required quick and e ective service, especially evacuating victims from disaster area will be more problematic. In this paper, the problem is about evacuating the people who need service after a disaster by using best routes. The problem based on the Pickup and Delivery Problem with consideration of critical operational constraints. The problem is NP Hard and exact proposal for the solution of real life problem is not achievable. The best routes which only contain generalized Pickup and Delivery Problem is based on the established Traveling Salesman Problem(TSP) methodology. For our research, we have two types of patients and instead of create an evacuation plan with one vehicle, we developed our approach for two vehicle with two end points. The vehicles can carry all types of patient moreover, the patient locations can contain both type of patients so, some points visited twice. For minimize the total transportation time, we regulated evacuation plan as the vehicles can help each other and we proposed a change point for swap the patients to carry their own end points. So, our solution methodology is provide new routes for determine a switch point. Especially, when we consider these critical operational constraints mentioned above the TSP can be insu cient. Thus, to make this problem more practicable, we created initial routes by using Christo des' Algorithm then presented a mathematical model which applied our critical elimination process on the Hamiltonian paths that we acquired in the rst phase. This implementation improved the solution of TSP. We also described two e ective and fast heuristic algorithms. As a result of these heuristics, we improved the quality and e ciency of TSP solution and the best routes that contained the critical constraints.Master ThesisPublication Metadata only Open-end bin packing problem with conflictsBalık, Ece Nur; Ekici, Ali; Ekici, Ali; Özener, Okan Örsan; Tekin, S.; Department of Industrial Engineering; Balık, Ece NurIn this thesis study, we focus on a new variant of the famous Bin Packing Problem (BPP) called the Open-End Bin Packing Problem with Conflicts (OEBPPC) which combines the Open-End Bin Packing Problem (OEBPP) and the Bin Packing Problem with Conflicts (BPPC). In OEBPPC, the aim is to pack a set of items into the least number of bins. However, the bin capacity is allowed to be exceeded only by the last item packed into the bin, and there exist conflicts between some item pairs; they cannot be packed into the same bin. We introduce a mathematical formulation and propose lower bounding procedures for our problem. We propose a metaheuristic algorithm, namely Variable Neighborhood Search (VNS), to approach the optimal solution through systematic changes and improvements in the solution. We generate different sets of instances by adapting some instances from the literature to our problem. We compare the performance of our metaheuristic algorithm both against the best lower bound and other algorithms we adapted from the literature as benchmark algorithms. We observe that our proposed metaheuristic outperforms the best benchmark algorithm in 74% of the instances with varying features.Master ThesisPublication Metadata only Thermal request optimization of a smart district heating systemKarasu, Mehmet Berk; Yanıkoğlu, İhsan; Yanıkoğlu, İhsan; Önal, Mehmet; Yavuz, T.; Department of Industrial EngineeringThis thesis proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden to decrease the carbon footprint of the residents. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of a decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we adapt three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses how the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission decrease and cost savings compared with the method that the experts of the utility company propose.Master ThesisPublication Metadata only A decomposition-based metaheuristic approach for solving the rapid needs assessment routing problem(2021-01-18) Mıhçıoğlu, Yurtsev; Albey, Erinç; Albey, Erinç; Yanıkoğlu, İhsan; Güler, M. G.; Department of Industrial EngineeringThis study proposes a decomposition-based tabu search algorithm for a multi-cover routing problem (MCRP), which aims to classify and evaluate the impacts of the disaster in different sites and the needs of different community groups affected by a disaster when remote communication is not possible, and highlights the solution time and quality performances of the introduced algorithm. The algorithm focuses on decomposing the problem to three phases and to apply different methods while solving them. Performance of the proposed tabu search algorithm is evaluated with respect to different benchmark solutions, findings are put to statistical tests, and the results indicate that the proposed algorithm can achieve high-quality solutions expeditiously, providing better results on average compared with the best-known solutions existing in the literature.PhD DissertationPublication Metadata only Applications of robust optimization in logistics and production planningAvishan, Farzad; Yanıkoğlu, İhsan; Yanıkoğlu, İhsan; Özener, Okan Örsan; Özener, Başak Altan; Yakıcı, E.; Yavuz, T.; Department of Industrial EngineeringWe analyze three applications of the robust optimization approach in this thesis. The initial part is dedicated to planning a dairy production and distribution problem considering uncertain demand. The second part presents an adjustable robust optimization approach for relief distribution in a post-disaster scenario where travel times are uncertain. Lastly, in the third part, we tackle the electric bus scheduling problem that incorporates uncertainty in both travel times and energy consumption. The first part of this thesis investigates a robust dairy production and distribution planning problem that considers the complexities of dairy production, including perishability, sequence dependence, and demand uncertainty. To tackle this uncertainty, we introduce an adjustable robust optimization approach that generates a robust and Pareto-efficient production and distribution management plan. This approach provides decision-making flexibility by allowing for adjustments based on the actual demand observed over a multi-period planning horizon. The effectiveness of the proposed method is evaluated through extensive Monte Carlo simulation experiments. Additionally, we conduct a case study to demonstrate how the adjustable approach outperforms the static robust approach in terms of the objective function value and solution performance. In the second part, we present an adjustable robust optimization approach for relief supply distribution in the aftermath of a disaster. The approach generates routes for relief logistics teams and determines the service times for visited sites to distribute supplies, taking into account the uncertainty of travel times. The model allows for adjustments to service decisions based on real-time information, resulting in robust solutions for the worst-case scenario of travel times but also more flexible and less conservative than those of static robust optimization. Due to the computational complexity of solving resulting models, we propose heuristic algorithms as an alternative solution approach. Using 2011 earthquake data from the Van province of Turkey, we have also demonstrated the effectiveness of our approach. In the last part, we investigate a scheduling problem for electric fleets faced with uncertain travel times and energy consumption. We propose a mixed-integer linear programming model to optimize electric fleet purchasing and charging operation costs, utilizing robust optimization to address uncertainty. A new uncertainty set is introduced to control the robustness of the solution. The model determines the required number of buses to cover all trips, schedules the trips, and designs charging plans for the buses. We evaluate the effectiveness of the model through extensive Monte Carlo simulations. Additionally, we present a case study on the off-campus transport network at Binghamton University to demonstrate the real-world applicability of the model and solution approach.Master ThesisPublication Metadata only A syncronized routing problem for restoring inter-dependent infrastructure networks(2018-05) Sevindik, Büşra; Günneç, Dilek; Balçık, Burcu; Günneç, Dilek; Balçık, Burcu; Ekici, Ali; Yanıkoğlu, İhsan; Çavdaroğlu, B.; Department of Industrial Engineering; Sevindik, BüşraDisasters may cause significant damages in lifeline infrastructure systems (such as gas, power, water) and lead to long-lasting failures. It is important to repair the damaged components and restore the affected infrastructures quickly. Since different lifeline infrastructure systems depend on each other, considering the inter-dependencies among different networks during repair planning can speed up the recovery process. In this thesis, we focus on developing practical methods to support planning repair operations for two inter-dependent infrastructure networks by considering the inter-dependencies within and between these networks. Specifically, we assume that repairing a damaged component may not be sufficient for making the component functional due to network dependencies. We consider multiple repair teams, each of which can repair the damaged components of one type of infrastructure, and formulate a coordinated repair scheduling problem, which determines a repair schedule for each repair team to minimize the total time for making all nodes functional. To solve this problem, we propose two alternative constructive heuristics, which employ different strategies to prioritize the visit of the damaged nodes based on their dependency status. We also apply local search procedures to improve the solutions attained by the constructive heuristics. We present computational results to evaluate the performance of the proposed heuristics. The results show that our heuristics lead to high quality solutions and can be used to make repair plans quickly in the post-disaster environment.Master ThesisPublication Metadata only An adaptive large neighborhood search for the multi-compartment inventory routing problem(2021-06-10) Gültekin, Ceren; Özener, Okan Örsan; Özener, Okan Örsan; Yanıkoğlu, İhsan; Ekici, Ali; Önal, Mehmet; Yakıcı, E.; Department of Industrial Engineering; Gültekin, CerenIn this thesis study, we concentrate on an inventory routing problem with a fleet of multi-compartment vehicles which enables the distribution of different products to customers on a delivery route. Using separate compartments on a vehicle increases profitability and customer satisfaction when customer demands vary over product and period basis. We assume that the compartment that each product can be loaded is known and the capacities of the compartments are fixed. Customers have preset storage capacities and distribution plans should be made in a way that no customers would face stock-outs for any product on any day. We observe the practices of this variant in the distribution of foods with different temperature needs to groceries, feed distribution to livestock farms, and collection of different types of recyclable wastes. We examine this problem separately for three assumptions considering different cases of allowing/disallowing split delivery to customers. We propose a matheuristic ap proach to solve the addressed problem where we systematically integrate an Adaptive Large Neighborhood Search algorithm with mathematical programming models. We generate a set of instances and test the performance of our algorithm by comparing it with the results obtained by a flow formulation adapted from the literature. We observe that the best results we find for each instance are only %11.7 worse than the solutions found by the flow formulation on average.