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Publication Metadata only A data-driven optimization framework for routing mobile medical facilities(2020) Yücel, Eda; Bozkaya, Burçin; Gökalp, Cemre; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage.Publication Metadata only A matheuristic for the generalized order acceptance and scheduling problem(Elsevier, 2022) N/A; Department of Industrial Engineering; Tarhan, İstenç; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033In make-to-order production systems, manufacturer can have limited capacity and due to the order de-livery time requirements, it may not be possible to accept all orders. This leads to the order acceptance and scheduling problem with release times and sequence dependent setup times that determines which orders to accept and how to schedule them simultaneously to maximize the revenue (GOAS). The aim of this study is to develop an effective and efficient solution methodology for the GOAS problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model, and a matheuristic algorithm that consists of a time-bucket based mixed integer linear programming model, a variable neighborhood search algorithm and a tabu search algorithm. Computational results show that the proposed matheuristic outperforms both the proposed exact models and previous state-of-the-art al-gorithms developed for the GOAS problem. The boundary of optimally solved instance size is pushed further and near optimal solutions are obtained in reasonable time for instances falling beyond this boundary.Publication Open Access A model-based heuristic to the min max K-arc routing for connectivity problem(Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2014) Akbari, Vahid; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We consider the post-disaster road clearing problem with the goal of restoring network connectivity in shortest time. Given a set of blocked edges in the road network, teams positioned at depot nodes are dispatched to open a subset of them that reconnects the network. After a team finishes working on an edge, others can traverse it. The problem is to find coordinated routes for the teams. We generate a feasible solution using a constructive heuristic algorithm after solving a relaxed mixed integer program. In almost 70 percent of the instances generated both randomly and from Istanbul data, the relaxation solution turned out to be feasible, i.e. optimal for the original problem.Publication Open Access A multi-criteria decision analysis to include environmental, social, and cultural issues in the sustainable aggregate production plans(Elsevier, 2019) Department of Industrial Engineering; Türkay, Metin; Rasmi, Seyyed Amir Babak; Kazan, Cem; Faculty Member; PhD Student; Department of Industrial Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/AAggregate production planning (APP) that is an important concept of supply chain management (SCM), is one of the tools to determine production rates, inventory levels, and workforce requirements for fulfilling customer demands in a multi-period setting. Traditional APP models employ a single objective function to optimize monetary issues only. In this paper, we present a multi-objective APP model to analyze economic, social, environmental, and cultural pillars inclusively; moreover, each pillar includes several sub-pillars in the model. The resulting model includes an accurate representation of the problem with binary and continuous variables under sustainability considerations. We illustrate the effectiveness of the model in an appliance manufacturer and solve the problem using an exact solution method for multi-objective mixed-integer linear programs (MOMILP). We find a large number of the non-dominated (ND) points in the objective function space and analyze their trade-offs systematically. We show how this framework supports multiple criteria decision making process in the APP problems in the presence of sustainability considerations. Our approach provides a comprehensive analysis of the ND points of sustainable APP (SAPP) problems, and hence, the trade-offs of objective functions are insightful to the decision makers.Publication Metadata only A multicenter international study to evaluate different aspects of relationship between MS and pregnancy(Sage, 2019) Zakaria, M.; Alroughani, R.; Moghadasi, A. N.; Terzi, M.; Sen, S.; Koseoglu, M.; Efendi, H.; Soysal, A.; Gozubatik-Celik, G.; Ozturk, M.; Sahraian, M.; Akinci, Y.; Kaya, Z. E.; Saip, S.; Siva, A.; N/A; Department of Industrial Engineering; Altıntaş, Ayşe; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 11611; 237468N/APublication Open Access A multiperiod stochastic production planning and sourcing problem with service level constraints(Springer, 2005) Yıldırım, Işıl; Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600; 3579We study a stochastic multiperiod production planning and sourcing problem of a manufacturer with a number of plants and/or subcontractors. Each source, i.e. each plant and subcontractor, has a different production cost, capacity, and lead time. The manufacturer has to meet the demand for different products according to the service level requirements set by its customers. The demand for each product in each period is random. We present a methodology that a manufacturer can utilize to make its production and sourcing decisions, i.e., to decide how much to produce, when to produce, where to produce, how much inventory to carry, etc. This methodology is based on a mathematical programming approach. The randomness in demand and related probabilistic service level constraints are integrated in a deterministic mathematical program by adding a number of additional linear constraints. Using a rolling horizon approach that solves the deterministic equivalent problem based on the available data at each time period yields an approximate solution to the original dynamic problem. We show that this approach yields the same result as the base stock policy for a single plant with stationary demand. For a system with dual sources, we show that the results obtained from solving the deterministic equivalent model on a rolling horizon gives similar results to a threshold subcontracting policy.Publication Metadata only A novel integer programming formulation with logic cuts for the U-shaped assembly line balancing problem(Taylor and Francis, 2014) Elaoud, Semya; Azer, Erfan Sadeqi; N/A; Department of Industrial Engineering; Fattahi, Ali; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956U-shaped assembly lines are regarded as an efficient configuration in Just-In-Time manufacturing. Balancing the workload in these lines is an unsolved problem that attracted significant research within the past two decades. We present a novel integer programming formulation for U-shaped line balancing problems, where cycle time, the interval between two consecutive outputs, is known and the aim is to minimize the number of workstations. To enhance the efficiency of the LP relaxation of the new formulation, we present three types of logic cuts (assignable-station-cuts, task-assignment-cuts and knapsack-cuts) that exploit the inherent logic of the problem structure. The new formulation and logic cuts are tested on an extensive set of benchmark problems to provide a comparative analysis with the existing models in the literature. The results show that our novel formulation augmented by assignable-station-cuts is significantly better than the previous formulations.Publication Open Access A prospective prediction tool for understanding Crimean-Congo haemorrhagic fever dynamics in Turkey(Elsevier, 2020) N/A; N/A; Department of Industrial Engineering; Ak, Çiğdem; Ergönül, Önder; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; School of Medicine; College of Engineering; N/A; 110398; 237468Objectives: we aimed to develop a prospective prediction tool on Crimean-Congo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: we used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: we predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases.Publication Metadata only A tandem queueing model with coupled processors(Elsevier, 2003) Resing, Jacques; Department of Industrial Engineering; Örmeci, Lerzan; Faculty Member; Department of Industrial Engineering; College of Engineering; 32863We consider a tandem queue with coupled processors and analyze the two-dimensional Markov process representing the numbers of jobs in the two stations. A functional equation for the generating function of the stationary distribution of this two-dimensional process is derived and solved through the theory of Riemann-Hilbert boundary value problems.Publication Open Access Agricultural planning of annual plants under demand, maturation, harvest, and yield risk(Elsevier, 2012) Department of Industrial Engineering; Tan, Barış; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Administrative Sciences and Economics; N/A; 28600In this study we present a planning methodology for a firm whose objective is to match the random supply of annual premium fruits and vegetables from a number of contracted farms and the random demand from the retailers during the planning period. The supply uncertainty is due to the uncertainty of the maturation time, harvest time, and yield. The demand uncertainty is the uncertainty of weekly demand from the retailers. We provide a planning methodology to determine the farm areas and the seeding times for annual plants that survive for only one growing season in such a way that the expected total profit is maximized. Both the single period and the multi period cases are analyzed depending on the type of the plant. The performance of the solution methodology is evaluated by using numerical experiments. These experiments show that the proposed methodology matches random supply and random demand in a very effective way and improves the expected profit substantially compared to the planning approaches where the uncertainties are not taken into consideration. (c) 2012 Elsevier B.V. All rights reserved.