Derya Dinler | Middle East Technical University (original) (raw)
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Papers by Derya Dinler
We consider a continuous multi-facility location-allocation problem that aims to minimize the sum... more We consider a continuous multi-facility location-allocation problem that aims to minimize the sum of weighted farthest Euclidean distances between (closed convex) polygonal and/or circular demand regions, and facilities they are assigned to. We show that the single facility version of the problem has a straightforward second-order cone programming formulation and can therefore be efficiently solved to optimality. To solve large size instances, we adapt a multi-dimensional direct search descent algorithm to our problem which is not guaranteed to find the optimal solution. In a special case with circular and rectangular demand regions, this algorithm, if converges, finds the optimal solution. We also apply a simple subgradient method to the problem. Furthermore, we review the algorithms proposed for the problem in the literature and compare all these algorithms in terms of both solution quality and time. Finally, we consider the multi-facility version of the problem and model it as a mixed integer second-order cone programming problem. As this formulation is weak, we use the alternate location-allocation heuristic to solve large size instances.
Computers & Operations Research, 2014
ABSTRACT We consider a continuous multi-facility location allocation problem where the demanding ... more ABSTRACT We consider a continuous multi-facility location allocation problem where the demanding entities are regions in the plane instead of points. The problem can be stated as follows: given m (closed, convex) polygonal demand regions in the plane, find the locations of q facilities and allocate each region to exactly one facility so as to minimize a weighted sum of squares of the maximum Euclidean distances between the demand regions and the facilities they are assigned to.
Computers & Operations Research, 2014
ABSTRACT Given a set of n interacting points in a network, the hub location problem determines lo... more ABSTRACT Given a set of n interacting points in a network, the hub location problem determines location of the hubs (transfer points) and assigns spokes (origin and destination points) to hubs so as to minimize the total transportation cost. In this study, we deal with the uncapacitated single allocation planar hub location problem (PHLP). In this problem, all flow between pairs of spokes goes through hubs, capacities of hubs are infinite, they can be located anywhere on the plane and are fully connected, and each spoke must be assigned to only one hub. We propose a mathematical formulation and a genetic algorithm (PHLGA) to solve PHLP in reasonable time. We test PHLGA on simulated and real life data sets. We compare our results with optimal solution and analyze results for special cases of PHLP for which the solution behavior can be predicted. Moreover, PHLGA results for the AP and CAB data set are compared with other heuristics.
Book Chapters by Derya Dinler
Traditional data mining methods for clustering only use unlabeled data objects as input. The aim ... more Traditional data mining methods for clustering only use unlabeled data objects as input. The aim of such methods is to find a partition of these unlabeled data objects in order to discover the underlying structure of the data. In some cases, there may be some prior knowledge about the data in the form of (a few number of) labels or constraints. Performing traditional clustering methods by ignoring the prior knowledge may result in extracting irrelevant information for the user. Constrained clustering, i.e., clustering with side information or semi-supervised clustering, addresses this problem by incorporating prior knowledge into the clustering process to discover relevant information from the data. In this chapter, a survey of advances in the area of constrained clustering will be presented. Different types of prior knowledge considered in the literature, and clustering approaches that make use of this prior knowledge will be reviewed.
Thesis by Derya Dinler
We consider a continuous multi-facility location-allocation problem that aims to minimize the sum... more We consider a continuous multi-facility location-allocation problem that aims to minimize the sum of weighted farthest Euclidean distances between (closed convex) polygonal and/or circular demand regions, and facilities they are assigned to. We show that the single facility version of the problem has a straightforward second-order cone programming formulation and can therefore be efficiently solved to optimality. To solve large size instances, we adapt a multi-dimensional direct search descent algorithm to our problem which is not guaranteed to find the optimal solution. In a special case with circular and rectangular demand regions, this algorithm, if converges, finds the optimal solution. We also apply a simple subgradient method to the problem. Furthermore, we review the algorithms proposed for the problem in the literature and compare all these algorithms in terms of both solution quality and time. Finally, we consider the multi-facility version of the problem and model it as a mixed integer second-order cone programming problem. As this formulation is weak, we use the alternate location-allocation heuristic to solve large size instances.
Computers & Operations Research, 2014
ABSTRACT We consider a continuous multi-facility location allocation problem where the demanding ... more ABSTRACT We consider a continuous multi-facility location allocation problem where the demanding entities are regions in the plane instead of points. The problem can be stated as follows: given m (closed, convex) polygonal demand regions in the plane, find the locations of q facilities and allocate each region to exactly one facility so as to minimize a weighted sum of squares of the maximum Euclidean distances between the demand regions and the facilities they are assigned to.
Computers & Operations Research, 2014
ABSTRACT Given a set of n interacting points in a network, the hub location problem determines lo... more ABSTRACT Given a set of n interacting points in a network, the hub location problem determines location of the hubs (transfer points) and assigns spokes (origin and destination points) to hubs so as to minimize the total transportation cost. In this study, we deal with the uncapacitated single allocation planar hub location problem (PHLP). In this problem, all flow between pairs of spokes goes through hubs, capacities of hubs are infinite, they can be located anywhere on the plane and are fully connected, and each spoke must be assigned to only one hub. We propose a mathematical formulation and a genetic algorithm (PHLGA) to solve PHLP in reasonable time. We test PHLGA on simulated and real life data sets. We compare our results with optimal solution and analyze results for special cases of PHLP for which the solution behavior can be predicted. Moreover, PHLGA results for the AP and CAB data set are compared with other heuristics.
Traditional data mining methods for clustering only use unlabeled data objects as input. The aim ... more Traditional data mining methods for clustering only use unlabeled data objects as input. The aim of such methods is to find a partition of these unlabeled data objects in order to discover the underlying structure of the data. In some cases, there may be some prior knowledge about the data in the form of (a few number of) labels or constraints. Performing traditional clustering methods by ignoring the prior knowledge may result in extracting irrelevant information for the user. Constrained clustering, i.e., clustering with side information or semi-supervised clustering, addresses this problem by incorporating prior knowledge into the clustering process to discover relevant information from the data. In this chapter, a survey of advances in the area of constrained clustering will be presented. Different types of prior knowledge considered in the literature, and clustering approaches that make use of this prior knowledge will be reviewed.