Organizational Unit: Industrial Engineering
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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 methods for the robotic cell problem(Springer Science+Business Media, 2011-06) Kharbeche, M.; Carlier, J.; Haouari, Mohamed; Moukrim, A.; Industrial Engineering; HAOUARI, MohamedThis paper investigates an exact method for the Robotic Cell Problem. We present a branch-and-bound algorithm which is the first exact procedure specifically designed with regard to this complex flow shop scheduling variant. Also, we propose a new mathematical programming model as well as new lower bounds. Furthermore, we describe an effective genetic algorithm that includes, as a mutation operator, a local search procedure. We report the results of a computational study that provides evidence that medium-sized instances, with up to 176 operations, can be optimally solved. Also, we found that the new proposed lower bounds outperform lower bounds from the literature. Finally, we show, that the genetic algorithm delivers good solutions while requiring short CPU times.ArticlePublication Metadata only VRP12 (vehicle routing problem with distances one and two) with side constraints(Elsevier, 2013-08) Ceranoglu, A. N.; Duman, Ekrem; Industrial Engineering; DUMAN, EkremThe problem undertaken in this study is inspired from a real life application. Consider a vehicle routing problem where the distances between the customer locations are either one or two. We name this problem as VRP12 in an analogy for the name TSP12 used for the traveling salesman problem in the literature. Additionally, assume that, the time to visit each customer is not constant and the visiting time together with the travel time constitutes the capacity of the vehicle. Furthermore, each customer has two characteristics and any two customers having a common characteristic should not be visited at the same time. If visited, a penalty fee incurs. In this study, we give the formulation of this problem and suggest some simple but effective algorithms that can be used to solve it. The algorithms are built with the relaxation of the side constraints but their performances are evaluated with their success in satisfying them. Information on our case study is also provided.ArticlePublication Metadata only Cyclic delivery schedules for an inventory routing problem(Informs, 2015-11) Ekici, Ali; Özener, Okan Örsan; Kuyzu, G.; Industrial Engineering; EKİCİ, Ali; ÖZENER, Okan ÖrsanWe consider an inventory routing problem where a common vendor is responsible for replenishing the inventories of several customers over a perpetual time horizon. The objective of the vendor is to minimize the total cost of transportation of a single product from a single depot to a set of customers with deterministic and stationary consumption rates over a planning horizon while avoiding stock-outs at the customer locations. We focus on constructing a repeatable (cyclic) delivery schedule for the product delivery. We propose a novel algorithm, called the Iterative Clustering-Based Constructive Heuristic Algorithm, to solve the problem in two stages: (i) clustering, and (ii) delivery schedule generation. To test the performance of the proposed algorithm in terms of solution quality and computational efficiency, we perform a computational study on both randomly generated instances and real-life instances provided by an industrial gases manufacturer. We also compare the performance of the proposed algorithm against an algorithm developed for general routing problems.Conference ObjectPublication Metadata only Summary of an effective formulation of the multi-criteria test suite minimization problem(IEEE, 2022) Özener, Okan Örsan; Sözer, Hasan; Industrial Engineering; Computer Science; ÖZENER, Okan Örsan; SÖZER, HasanThis is an extended abstract of the article: Okan Orsan Ozener and Hasan Sozer, 'An Effective Formulation of the Multi-Criteria Test Suite Minimization Problem', published in the Journal of Systems and Software, Vol. 168, pp. 110632, 2020. https://doi.org/10.1016/j.jss.2020.110632.ArticlePublication Metadata only Optimal pricing and inventory strategies for fashion products under time-dependent interest rate and demand(Elsevier, 2021-04) Akan, M.; Albey, Erinç; Güler, M. G.; Industrial Engineering; ALBEY, ErinçIn this work we consider the dynamic pricing problem of a retailer operating in a market with a single fashion item and under time-dependent interest rate. The demand is assumed to be deterministic and dependent on the price and decay with time, i.e., the market shrinks throughout the horizon. Using an optimal-control-theoretic approach, we analytically derive the optimal pricing and inventory strategy for the retailer over a finite horizon setting. We further analyze the ramifications of the optimal pricing decision for different initial inventory levels dictated by the relationship between the supplier and the retailer; and for varying market interest rates. Optimal dynamic pricing policy is a continuous function, which is almost impossible to use in practice. This is handled using approximate piece-wise constant pricing policies. The trade-off between dynamic pricing policy and approximate policies is also investigated.ArticlePublication Metadata only Multi-vehicle sequential resource allocation for a nonprofit distribution system(Informa Group, 2014) Koyuncu, Burcu Balçık; Iravanib, S.; Smilowitz, K.; Industrial Engineering; KOYUNCU, Burcu BalçıkThis article introduces a multi-vehicle sequential allocation problem that considers two critical objectives for nonprofit operations: providing equitable service and minimizing unused donations. This problem is motivated by an application in food redistribution from donors such as restaurants and grocery stores to agencies such as soup kitchens and homeless shelters. A set partitioning model is formulated that can be used to design vehicle routes; it primarily focuses on equity maximization and implicitly considers waste. The behavior of the model in clustering agencies and donors on routes is studied, and the impacts of demand variability and supply availability on route composition and solution performance are analyzed. A comprehensive numerical study is performed in order to develop insights on optimal solutions. Based on this study, an efficient decomposition-based heuristic for the problem that can handle an additional constraint on route length is developed and it is shown that the heuristic obtains high-quality solutions in terms of equity and waste.ArticlePublication Metadata only Tight compact models and comparative analysis for the prize collecting Steiner tree problem(Elsevier, 2013-03) Haouari, Mohamed; Layeb, S. B.; Sherali, H. D.; Industrial Engineering; HAOUARI, MohamedWe investigate a generalized version of the prize collecting Steiner tree problem (PCSTP), where each node of a given weighted graph is associated with a prize as well as a penalty cost. The problem is to find a tree spanning a subset of nodes that collects a total prize not less than a given quota Q, such that the sum of the weights of the edges in the tree plus the sum of the penalties of those nodes that are not covered by the tree is minimized. We formulate several compact mixed-integer programming models for the PCSTP and enhance them by appending valid inequalities, lifting constraints, or reformulating the model using the Reformulation–Linearization Technique (RLT). We also conduct a theoretical comparison of the relative strengths of the associated LP relaxations. Extensive results are presented using a large set of benchmark instances to compare the different formulations. In particular, a proposed hybrid compact formulation approach is shown to provide optimal or very near-optimal solutions for instances having up to 2500 nodes and 3125 edges.ArticlePublication Metadata only Healthcare intelligence: Turning data into knowledge(IEEE, 2014) Yang, H.; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer ErhunExceptional opportunities exist for researchers and practitioners to invest in conducting innovative and transformative research in data mining and health informatics. This IEEE Intelligent Systems "Trends and Controversies" (T&C) department hopes to raise awareness and highlight recent research to move toward such goals. The introduction, "Healthcare Intelligence: Turning Data into Knowledge," is written by Hui Yang and Erhun Kundakcioglu. Next, "Empowering Excellence of Care by Radiology Informatics" is written by Jing Li, Teresa Wu, J. Ross Mitchell, Amy K. Hara, William Pavlicek, Leland S. Hu, Alvin C. Silva, and Christine M. Zwart. Third, "Opportunities for Operations Research in Medical Decision Making" is written by Sait Tunc, Oguzhan Alagoz, and Elizabeth Burnside. Fourth, "Diagnostic Network Modeling of Neural Connectivity Using Functional Magnetic Resonance Imaging" is written by W. Art Chaovalitwongse, Georgiy Presnyakov, Yulian Cao, Sirirat Sujitnapitsatham, Daehan Won, Tara Madhyastha, Kurt E. Weaver, Paul R. Borghesani, and Thomas J. Grabowski. The final article, "Spatial Clustering in Public Health: Advances and Challenges," is written by Lianjie Shu, Man Ho Ling, Shui-Yee Wong, and Kwok-Leung Tsui.ArticlePublication Metadata only Algorithmic expedients for the prize collecting Steiner tree problem(Elsevier, 2010) Haouari, Mohamed; Layeb, S. B.; Sherali, H. D.; Industrial Engineering; HAOUARI, MohamedThis paper investigates the Prize Collecting Steiner Tree Problem (PCSTP) on a graph, which is a generalization of the well-known Steiner tree problem. Given a root node, edge costs, node prizes and penalties, as well as a preset quota, the PCSTP seeks to find a subtree that includes the root node and collects a total prize not smaller than the specified quota, while minimizing the sum of the total edge costs of the tree plus the penalties associated with the nodes that are not included in the subtree. For this challenging network design problem that arises in telecommunication settings, we present two valid 0-1 programming formulations and use them to develop preprocessing procedures for reducing the graph size. Also, we design an optimization-based heuristic that requires solving a PCSTP on a specific tree-subgraph. Although, this latter special case is shown to be NP-hard, it is effectively solvable in pseudo-polynomial time. The worst-case performance of the proposed heuristic is also investigated. In addition, we describe new valid inequalities for the PCSTP and embed all the aforementioned constructs in an exact row-generation approach. Our computational study reveals that the proposed approach can solve relatively large-scale PCSTP instances having up to 1000 nodes to optimality.