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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Linear tests for decreasing absolute risk aversion stochastic dominance(The Institute for Operations Research and the Management Sciences (INFORMS), 2015) Fang, Yi; Kopa, Milos; N/A; Post, Gerrit Tjeerd; Other; Graduate School of Business; N/AWe develop and implement linear formulations of convex stochastic dominance relations based on decreasing absolute risk aversion (DARA) for discrete and polyhedral choice sets. Our approach is based on a piecewise-exponential representation of utility and a local linear approximation to the exponentiation of log marginal utility. An empirical application to historical stock market data suggests that a passive stock market portfolio is DARA stochastic dominance inefficient relative to concentrated portfolios of small-cap stocks. The mean-variance rule and Nth-order stochastic dominance rules substantially underestimate the degree of market portfolio inefficiency because they do not penalize the unfavorable skewness of diversified portfolios, in violation of DARA.Publication Metadata only An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations(Wiley, 2017) Bozkaya, Burçin; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Telciler, Kaan; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 178838; N/AWe consider the route optimization problem of transporting valuables in cash-in-transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high-risk nature of this operation (e.g., robberies) we consider a bi-objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socioeconomic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi-objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin-destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with costrisk trade-off to support decision making.Publication Metadata only A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems(Elsevier Science Bv, 2014) N/A; N/A; Department of Business Administration; Kirlik, Gökhan; Sayın, Serpil; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 6755Most real-life decision-making activities require more than one objective to be considered. Therefore, several studies have been presented in the literature that use multiple objectives in decision models. In a mathematical programming context, the majority of these studies deal with two objective functions known as bicriteria optimization, while few of them consider more than two objective functions. In this study, a new algorithm is proposed to generate all nondominated solutions for multiobjective discrete optimization problems with any number of objective functions. In this algorithm, the search is managed over (p - 1)-dimensional rectangles where p represents the number of objectives in the problem and for each rectangle two-stage optimization problems are solved. The algorithm is motivated by the well-known epsilon-constraint scalarization and its contribution lies in the way rectangles are defined and tracked. The algorithm is compared with former studies on multiobjective knapsack and multiobjective assignment problem instances. The method is highly competitive in terms of solution time and the number of optimization models solved.Publication Metadata only Minimum-variance hedging for managing risks in inventory models with price fluctuations(Now Publishers, 2017) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Canyakmaz, Caner; Karaesmen, Fikri; Özekici, Süleyman; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 3579; 32631We consider the financial hedging of a random operational cash flow that arises in inventory operations with price and demand uncertainty. We use a variance minimization approach to find a financial portfolio that would minimize the total variance of operational and financial returns. For inventory models that involve continuous price fluctuations and price-dependent demand that arrives in continuous time, we characterize the minimum-variance hedging policies and numerically illustrate their effectiveness.Publication Metadata only Multi-item dynamic lot-sizing with delayed transportation policy(Elsevier, 2011) N/A; Department of Industrial Engineering; Sancak, Emre; Salman, Fatma Sibel; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 178838We optimize ordering and inbound shipment decisions for a manufacturer that sources multiple items from a single supplier. the objective is to satisfy the requirements in the production plan with minimum transportation and inventory holding costs over a multi-period planning horizon. Transportation costs are charged to the manufacturer on a per truck shipment basis. We investigate the option of delaying a less-than-full truckload shipment to the next period, by utilizing the safety stocks as needed. We analyze the impact of delaying shipments on both cost and service levels in stochastic environments through experiments with data from a bus manufacturer. the results indicate that the proposed policy reduces both holding and transportation costs without creating much stock-out risk.Publication Metadata only Mean-variance newsvendor model with random supply and financial hedging(Taylor and Francis Inc, 2015) N/A; Department of Industrial Engineering; Tekin, Müge; Özekici, Süleyman; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 32631In this paper, we follow a mean-variance (MV) approach to the newsvendor model. Unlike the risk-neutral newsvendor that is mostly adopted in the literature, the MV newsvendor considers the risks in demand as well as supply. We further consider the case where the randomness in demand and supply is correlated with the financial markets. The MV newsvendor hedges demand and supply risks by investing in a portfolio composed of various financial instruments. The problem therefore includes both the determination of the optimal ordering policy and the selection of the optimal portfolio. Our aim is to maximize the hedged MV objective function. We provide explicit characterizations on the structure of the optimal policy. We also present numerical examples to illustrate the effects of risk-aversion on the optimal order quantity and the effects of financial hedging on risk reduction.Publication Metadata only Generalized order acceptance and scheduling problem with batch delivery: models and metaheuristics(Pergamon-Elsevier Science Ltd, 2021) 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; 6033This paper addresses an extended version of the generalized order acceptance and scheduling problem by including the logistics aspects into the production scheduling decisions. While order acceptance and scheduling feature of the problem includes the joint decision of which orders to accept and how to schedule them due to the limited capacity in production environment and due to the order delivery time requirements for the customers, logistics aspect of the problem entails the decision of how to batch the accepted orders for the delivery in conjunction with the production scheduling. The objective is to maximize the net revenue in line with the literature of order acceptance and scheduling problem. We first present a mixed integer linear programming and a constraint programming model for this problem. To tackle large size problem instances in which these models fail, we propose an iterated local search algorithm using a new local search scheme. To evaluate the performance of the proposed local search scheme, a variant of this algorithm is developed which replaces the relevant scheme with tabu search. Computational results show that the proposed models achieve small optimality gaps for the small size problems, but their performances deteriorate significantly as the problem size enlarges. For the large size problem instances, the iterated local search algorithm using the proposed local search scheme achieves smaller optimality gaps compared to the one with the tabu search algorithm.Publication Metadata only An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem(Elsevier, 2014) Department of Business Administration; Department of Industrial Engineering; Department of Industrial Engineering; N/A; Aksen, Deniz; Kaya, Onur; Salman, Fatma Sibel; Tüncel, Özge; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Sciences; College of Engineering; Graduate School of Sciences and Engineering; 40308; 28405; 178838; N/AWe study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.Publication Metadata only Solving the multi-depot location-routing problem with lagrangian relaxation(Springer, 2007) N/A; N/A; Department of Business Administration; Özyurt, Zeynep; Aksen, Deniz; Master Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 40308Multi-depot Location-Routing Problem (MDLRP) is about finding the optimal number and locations of depots while allocating customers to depots and determining vehicle routes to visit all customers. In this study we propose a nested Lagrangian relaxation-based method for the discrete uncapacitated MDLRP. An outer Lagrangian relaxation embedded in subgradient optimization decomposes the parent problem into two subproblems. The first subproblem is a facility location-like problem. It is solved to optimality with Cplex 9.0. The second one resembles a capacitated and degree constrained minimum spanning forest problem, which is tackled with an augmented Lagrangian relaxation. The solution of the first subproblem reveals a depot location plan. As soon as a new distinct location plan is found in the course of the subgradient iterations, a tabu search algorithm is triggered to solve the multi-depot vehicle routing problem associated with that plan, and a feasible solution to the parent problem is obtained. Its objective value is checked against the current upper bound on the parent problem's true optimal objective value. The performance of the proposed method has been observed on a number of test problems, and the results have been tabulated.Publication Metadata only Prediction of folding type of proteins using mixed-integer linear programming(Elsevier Science Bv, 2005) Department of Industrial Engineering; Department of Industrial Engineering; N/A; Türkay, Metin; Yüksektepe, Fadime Üney; Yılmaz, Özlem; Faculty Member; Researcher; Master Student; Department of Industrial Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/AProteins are classified into four main structural classes by considering their amino acid compositions. Traditional approaches that use hyperplanes to partition data sets into two groups perform poorly due to the existence of four classes. Therefore, a novel method that uses mixed-integer programming is developed to overcome difficulties and inconsistencies of these traditional approaches. Mixed-integer programming (MIP) allows the use of hyper-boxes in order to define the boundaries of the sets that include all or some of the points in that class. For this reason, the efficiency and accuracy of data classification with MIP approach can be improved dramatically compared to the traditional methods. The efficiency of the proposed approach is illustrated on a training set of 120 proteins (30 from each type). The prediction results and their validation are also examined.