Organizational Unit: Industrial Engineering
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ArticlePublication Metadata only Delegation vs. control of component procurement under asymmetric cost information and simple contracts(Informs, 2013) Kayış, Enis; Erhun, F.; Plambeck, E. L.; Industrial Engineering; KAYIŞ, EnisA manufacturer must choose whether to delegate component procurement to her tier 1 supplier or control it directly. Because of information asymmetry about suppliers’ production costs and the use of simple quantity discount or price-only contracts, either delegation or control can yield substantially higher expected profit for the manufacturer. Delegation tends to outperform control when (1) the manufacturer is uncertain about the tier 1 supplier’s cost and believes that it is likely to be high; (2) the manufacturer and the tier 1 supplier know the tier 2 supplier’s cost or at least that it will be high; (3) the manufacturer has an alternative to engaging the tier 1 and tier 2 suppliers, such as in-house production; and (4) the firms use price-only contracts as opposed to quantity discount contracts. These results shed light on practices observed in the electronics industry.ArticlePublication Metadata only Robust scheduling and robustness measures for the discrete time/cost trade-off problem(Elsevier, 2010-12-01) Hazır, Ö.; Haouari, Mohamed; Erel, E.; Industrial Engineering; HAOUARI, MohamedProjects are often subject to various sources of uncertainties that have a negative impact on activity durations and costs. Therefore, it is crucial to develop effective approaches to generate robust project schedules that are less vulnerable to disruptions caused by uncontrollable factors. In this paper, we investigate the robust discrete time/cost trade-off problem, which is a multi-mode project scheduling problem with important practical relevance. We introduce surrogate measures that aim at providing an accurate estimate of the schedule robustness. The pertinence of each proposed measure is assessed through computational experiments. Using the insights revealed by the computational study, we propose a two-stage robust scheduling algorithm. Finally, we provide evidence that the proposed approach can be extended to solve a complex robust problem with tardiness penalties and earliness revenues.ArticlePublication Metadata only On discounted stochastic games with incomplete information on payoffs and a security application(Elsevier, 2014-01) Kardeş, Erim; Industrial Engineering; KARDEŞ, ErimThis paper presents a robust optimization model for n-person finite state/action stochastic games with incomplete information on payoffs. For polytopic uncertainty sets, we propose an explicit mathematical programming formulation for an equilibrium calculation. It turns out that a global optimal of this mathematical program yields an equilibrium point and epsilon-equilibria can be calculated based on this result. We briefly describe an incomplete information version of a security application that can benefit from robust game theory.ArticlePublication Open Access Scheduling ambulance crews for maximum coverage(Palgrave MacMillan, 2010-04) Erdoğan, Güneş; Erkut, Erhan; Ingolfsson, A.; Laporte, G.; Business Administration; Industrial Engineering; ERDOĞAN, Güneş; ERKUT, ErhanThis paper addresses the problem of scheduling ambulance crews in order to maximize the coverage throughout a planning horizon. The problem includes the subproblem of locating ambulances to maximize expected coverage with probabilistic response times, for which a tabu search algorithm is developed. The proposed tabu search algorithm is empirically shown to outperform previous approaches for this subproblem. Two integer programming models that use the output of the tabu search algorithm are constructed for the main problem. Computational experiments with real data are conducted. A comparison of the results of the models is presented.ArticlePublication Open Access A machine learning approach to deal with ambiguity in the humanitarian decision-making(Wiley, 2023-09) Grass, E.; Ortmann, J.; Koyuncu, Burcu Balçık; Rei, W.; Industrial Engineering; KOYUNCU, Burcu BalçıkOne of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision-making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision-making. To the best of our knowledge, the integration of ambiguous information into decision-making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision-maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.ArticlePublication Metadata only Exact approaches for integrated aircraft fleeting and routing at TunisAir(Science+Business Media, 2011-06) Haouari, Mohamed; Sherali, H. D.; Mansour, F. Z.; Aissaoui, N.; Industrial Engineering; HAOUARI, MohamedWe describe models and exact solutions approaches for an integrated aircraft fleeting and routing problem arising at TunisAir. Given a schedule of flights to be flown, the problem consists of determining a minimum cost route assignment for each aircraft so as to cover each flight by exactly one aircraft while satisfying maintenanceactivity constraints. We investigate two tailored approaches for this problem: Benders decomposition and branch-and-price. Computational experiments conducted on real-data provide evidence that the branch-and-price approach outperforms the Benders decomposition approach and delivers optimal solutions within moderate CPUtimes. On the other hand, the Benders algorithm yields very quickly high quality near-optimal solutions.ArticlePublication Metadata only A branch-and-cut algorithm for solving the non-preemptive capacitated swapping problem(Elsevier, 2010-08-06) Erdoğan, Güneş; Cordeau, J.-F.; Laporte, G.; Industrial Engineering; ERDOĞAN, GüneşThis paper models and solves a capacitated version of the Non-Preemptive Swapping Problem. This problem is defined on a complete digraph , at every vertex of which there may be one unit of supply of an item, one unit of demand, or both. The objective is to determine a minimum cost capacitated vehicle route for transporting the items in such a way that all demands are satisfied. The vehicle can carry more than one item at a time. Three mathematical programming formulations of the problem are provided. Several classes of valid inequalities are derived and incorporated within abranch-and-cut algorithm, and extensive computational experiments are performed on instances adapted from TSPLIB.ArticlePublication Metadata only An EOQ model with deteriorating items and self-selection constraints(Springer Nature, 2020-09) Önal, Mehmet; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; ÖNAL, MehmetIn this paper, we consider a store that sells two vertically differentiated items that might substitute each other. These items do not only differ in quality and price, but they also target two different customer segments. Items deteriorate over time and might require price adjustments to avoidcannibalization. We provide closed-form solutions for pricing and ordering of these items that lead to key managerial insights.ArticlePublication Metadata only Influence maximization in social networks under Deterministic Linear Threshold Model(Elsevier, 2018-12) Gürsoy, F.; Danış, Dilek Günneç; Industrial Engineering; DANIŞ, Dilek GünneçWe define the new Targeted and Budgeted Influence Maximization under Deterministic Linear Threshold Model problem and develop the novel and scalable TArgeted and BUdgeted Potential Greedy (TABU-PG) algorithm which allows for optional methods to solve this problem. It is an iterative and greedy algorithm that relies on investing in potential future gains when choosing seed nodes. We suggest new real-world mimicking techniques for generating influence weights, thresholds, profits, and costs. Extensive computational experiments on four real network (Epinions, Academia, Pokec and Inploid) show that our proposed heuristics perform significantly better than benchmarks. We equip TABU-PG with novel scalability methods which reduce runtime by limiting the seed node candidate pool, or by selecting more nodes at once, trading-off with spread performance.ArticlePublication Metadata only An application of unrelated parallel machine scheduling with sequence-dependent setups at Vestel Electronics(Elsevier, 2019-11) Ekici, Ali; Elyasi, Milad; Özener, Okan Örsan; Sarıkaya, M. B.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan Örsan; Elyasi, MiladIn this paper, we analyze a variant of the unrelated parallel machine scheduling problem with the objective of minimizing the total tardiness and earliness in the presence of sequence-dependent setups, unequal release times, machine-job compatibility restrictions and workload balance requirements. This study is motivated by the production scheduling operations at a television manufacturer, Vestel Electronics. Vestel produces LCD/LED TVs and has a significant market share in the 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. The decision maker has to consider several factors including job-assembly line compatibility, the release and due dates of the jobs and a workload balance among different assembly lines when forming the production schedule. To address this problem, we propose a wide range of heuristics including (i) a sequential algorithm, (ii) a tabu search algorithm, (iii) a random set partitioning approach, and (iv) a novel matheuristic approach utilizing the local intensification and global diversification powers of a tabu search algorithm. Through a computational study, we observe that all the proposed approaches not only significantly outperform the current practice but also provide solutions with around 5% less optimality gap compared to a benchmark algorithm in the literature.