Ceyda Oguz
Koç University, Industrial Engineering, Faculty Member
- Health Care Scheduling, Scheduling, Flow Shop Scheduling, Scheduling Algorithms, Production Scheduling, Production Planning and Scheduling, and 14 moreMine Production Scheduling, Crop Water Requirements and Irrigation Scheduling, Vehicle Routing and Scheduling, Job shop scheduling, Scheduliş, Heuristics, Meta-Heuristics (Tabu Search, Genetic Algorithms, Simulated Annealing, Ant-Colony, Particle Swarm Optimization), Bioinformatics, Computational and Systems Biology, Bioinformatics, Operations research and Optimization, Logistics, Container Terminal, Container Terminal Planning, and Optimization (Mathematical Programming)edit
This data set includes the test instances and computational results in the paper entitled "A Matheuristic for the Generalized Order Acceptance and Scheduling Problem" (Tarhan and Oğuz).
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In this paper, a single machine, n-job scheduling problem, in which each job has a distinct due date-and equal earliness and tardiness coefficients, is studied. The objective is to determine an optimal schedule to minimize the sum of... more
In this paper, a single machine, n-job scheduling problem, in which each job has a distinct due date-and equal earliness and tardiness coefficients, is studied. The objective is to determine an optimal schedule to minimize the sum of earliness-tardiness penalties. Based on a new theorem for the idle time insertion in the optimal schedule, the paper presents a deterministic algorithm to obtain the upper bound of an optimal schedule. By combining it with an existing lower bound estimation scheme, a fast branch and bound approach for optimal schedule is proposed. Finally, a numerical example is given to illustrate the effectiveness of the new method.
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This data set includes the test instances and computational results in the paper entitled "A Matheuristic for the Generalized Order Acceptance and Scheduling Problem" (Tarhan and Oğuz).
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Abstract In make-to-order production systems, manufacturer can have limited capacity and due to the order delivery time requirements, it may not be possible to accept all orders. This leads to the order acceptance and scheduling problem... more
Abstract In make-to-order production systems, manufacturer can have limited capacity and due to the order delivery 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 algorithms 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.
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Evolutionary methods of protein engineering such as phage display have revolutionized drug design and the means of studying molecular binding. In order to obtain the highest experimental efficiency, the distributions of constructed... more
Evolutionary methods of protein engineering such as phage display have revolutionized drug design and the means of studying molecular binding. In order to obtain the highest experimental efficiency, the distributions of constructed combinatorial libraries should be carefully adjusted. The presented approach takes into account diversity–completeness trade–off and tries to maximize the number of new amino acid sequences generated in each cycle of the experiment. In the paper, the mathematical model is introduced and the parallel genetic algorithm for the defined optimization problem is described. Its implementation on the SunFire 6800 computer proves a high efficiency of the proposed approach.
Research Interests: Mathematics, Computer Science, Distributed Computing, Combinatorial Optimization, Mathematical Programming, and 15 moreModeling, Algorithm, Genetic Algorithm, Protein Engineering, Optimization Problem, parallel Genetic algorithm, Drug Design, Phage display, Display, Evolutionary Algorithm, Mathematical Model, Parallel Algorithm, Amino Acid Profile, Amino Acid Sequence, and High Efficiency
Research Interests: Computer Science, Distributed Computing, Parallel Computing, Computer Vision, Image Processing, and 13 moreComputer Software, Multiprocessor Scheduling, Job Scheduling, CEE, Real Time Application, Heuristic, Lower Bound, Parallel Computer, Task Scheduling, Electrical And Electronic Engineering, Multiprocessing, Heuristic algorithm, and Computers and Electrical Engineering
ABSTRACT This paper studies a non-preemptive two-stage flowshop scheduling problem to minimize the earliness and tardiness under the environment of a common due window. The window size and the window location are considered to be given... more
ABSTRACT This paper studies a non-preemptive two-stage flowshop scheduling problem to minimize the earliness and tardiness under the environment of a common due window. The window size and the window location are considered to be given parameters. The just-in-time problem exists naturally and has many practical applications. The problem is shown to be NP-complete in the strong sense. We develop a branch and bound algorithm and a heuristic to solve the problem. We conduct the computational experiments to test the performances of the algorithms. A strong lower bound is derived for the branch and bound algorithm that can efficiently solve 15 jobs problem for about . The heuristic is shown to be efficient and effective, which can solve the problem of 150 jobs for about and provide near-optimal solution. We justify that the heuristic is an excellent solution approach for large problem instances. We also show that four special cases are either polynomial solvable or NP-complete in the ordinary sense.
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In the paper, the flow-shop scheduling problem with parallel machines at each stage (machine center) is studied. For each job its release and due date as well as a processing time for its each operation are given. The scheduling criterion... more
In the paper, the flow-shop scheduling problem with parallel machines at each stage (machine center) is studied. For each job its release and due date as well as a processing time for its each operation are given. The scheduling criterion consists of three parts: the total weighted ...
Research Interests: Computer Science, Operations Research, Tabu Search, Simulated Annealing, Scheduling, and 12 moreFlow Shop Scheduling, Multidisciplinary, Production economics, Metaheuristic, Mathematical Optimization, Job shop scheduling, Tardiness, Flow shop, Parallel Machines, Approximate Algorithm, Waiting Time, and LINEAR PROGRAM
2000-2001 > Academic research: refereed > Refereed conference pape
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ABSTRACT The berth allocation problem is to allocate space along the quayside to incoming ships at a container terminal in order to minimize some objective function. We consider minimization of total costs for waiting and handling as well... more
ABSTRACT The berth allocation problem is to allocate space along the quayside to incoming ships at a container terminal in order to minimize some objective function. We consider minimization of total costs for waiting and handling as well as earliness or tardiness of completion, for all ships. We assume ships can arrive at any given time, i.e., before or after the berths become available. The resulting problem, which subsumes several previous ones, is expressed as a linear mixed 0–1 program. As it turns out to be too time-consuming for exact solution of instances of realistic size, a Variable Neighborhood Search (VNS) heuristic is proposed, and compared with Multi-Start (MS), a Genetic Search algorithm (GA) and a Memetic Search algorithm (MA). VNS provides optimal solutions for all instances solved to optimality in a previous paper of the first two authors and outperforms MS, MA and GA on large instances.
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2003-2004 > Academic research: refereed > Refereed conference pape
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2003-2004 > Academic research: refereed > Refereed conference pape
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Research Interests: Applied Mathematics, Computer Science, Computational Complexity Theory, Parallel Computing, Computational Complexity, and 8 moreOperations Research, Pure Mathematics, Multiprocessor Scheduling, Scheduling Theory, Discrete Optimization, Numerical Analysis and Computational Mathematics, Multiprocessing, and Polynomial Time
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Multi-layer multiprocessor systems are generally employed in real-time applications such as robotics and computer vision. The authors developed three efficient heuristic scheduling algorithms for such systems. In their model, they... more
Multi-layer multiprocessor systems are generally employed in real-time applications such as robotics and computer vision. The authors developed three efficient heuristic scheduling algorithms for such systems. In their model, they considered scheduling multiprocessor tasks with arbitrary processing times and arbitrary processor requirements in a two-stage hybrid flow-shop to minimize makespan. They also derived an effective lower bound for the problem.
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Page 351. Job Scheduling in a Multi-layer Vision System M. Fikret Ercan1, Ceyda Oguz2, and Yu-Fai Fung1 1 Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR {eefikret, eeyffung} Qee. polyu. edu. ...
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Research Interests: Computer Science, Parallel Computing, Flow Shop Scheduling, Genetic Algorithm, Optimal mine design and scheduling, and 10 moreparallel Genetic algorithm, Multiprocessor Scheduling, Job shop scheduling, Schedule, Parallel Computer, Multiprocessing, Parallel Architecture, Machine Scheduling, Optimal scheduling, and Springer Ebooks
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Abstract This 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... more
Abstract This 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.
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This article considers minimizing the makespan in a two-stage flowshop scheduling problem with a common second-stage machine. After introducing the problem, we show that it is NP-hard and give two special cases which are polynomially... more
This article considers minimizing the makespan in a two-stage flowshop scheduling problem with a common second-stage machine. After introducing the problem, we show that it is NP-hard and give two special cases which are polynomially solvable. Next, we propose a heuristic algorithm and analyze its worst-case error bound. We then develop some lower bounds. Finally, we perform some computational experiments
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ABSTRACT We study several single-machine non-preemptive scheduling problems to minimize the sum of weighted earliness–tardiness, weighted number of early and tardy jobs, common due window location, and flowtime penalties. We allow the due... more
ABSTRACT We study several single-machine non-preemptive scheduling problems to minimize the sum of weighted earliness–tardiness, weighted number of early and tardy jobs, common due window location, and flowtime penalties. We allow the due window location to be either a decision variable or a given parameter. We assume that the due window location has a tolerance and the window size is a given parameter. We further make the assumption that the ratios of the job processing times to the earliness–tardiness weights are agreeable for the first problem. We propose pseudo-polynomial dynamic programming algorithms to optimally solve the problems. We also provide polynomial time algorithms for several special cases.Scope and purpose The widespread use of Just-In-Time philosophy in manufacturing to eliminate inventories leads to a new class of scheduling problems in which the earliness and/or number of early jobs are penalized as well as the tardiness and/or tardy jobs. In this type of environments, the jobs are sometimes associated with a period of time within which they incur no penalty since the customers will generally allow a time interval for the delivery of the products. This time period is called a due window. There are a variety of applications with due windows in factory automation, production maintenance, and so on. In this paper, we consider the common due window problems to minimize the weighted earliness–tardiness, weighted number of early–tardy jobs and weighted flowtime on a single machine. The main contributions of this paper are identifying the computational complexity of the problems, developing dynamic programming algorithms to optimally solve them, and providing efficient and exact polynomial algorithms for the special cases.
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Research Interests: Psychology, Social Psychology, Job Satisfaction, China, Gender Discrimination, and 12 moreSexual Harassment, Cross-Cultural Comparison, Vocational, Business and Management, Legislation, United States, Cross cultural studies, Sex Discrimination, Mainland China, Harassment, Affective Commitment, and Labor Turnover
The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the... more
The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details of a genetic algorithm, which is used in the memetic algorithm. We then propose a constraint programming based branch-and-bound algorithm to be employed as
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Predictive performance of machine learning algorithms on related problems can be improved using multitask learning approaches. Rather than performing survival analysis on each data set to predict survival times of cancer patients, we... more
Predictive performance of machine learning algorithms on related problems can be improved using multitask learning approaches. Rather than performing survival analysis on each data set to predict survival times of cancer patients, we developed a novel multitask approach based on multiple kernel learning (MKL). Our multitask MKL algorithm both works on multiple cancer data sets and integrates cancer-related pathways/gene sets into survival analysis. We tested our algorithm, which is named as Path2MSurv, on the Cancer Genome Atlas data sets analyzing gene expression profiles of 7,655 patients from 20 cancer types together with cancer-specific pathway/gene set collections. Path2MSurv obtained better or comparable predictive performance when benchmarked against random survival forest, survival support vector machine, and single-task variant of our algorithm. Path2MSurv has the ability to identify key pathways/gene sets in predicting survival times of patients from different cancer types.
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Abstract This 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... more
Abstract This 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.
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A constraint programming based branch-and-bound algorithm is embedded into a memetic algorithm to solve multiprocessor task scheduling problem in hybrid flow-shop environments. Both meth- ods are able to solve the problem by themselves... more
A constraint programming based branch-and-bound algorithm is embedded into a memetic algorithm to solve multiprocessor task scheduling problem in hybrid flow-shop environments. Both meth- ods are able to solve the problem by themselves but the combination of the two allows to solve larger problem in a shorter amount of time. Computational experiments are conducted on a large set of in- stances and the resulting memetic algorithm gives the best results so far.
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This data set includes the test instances and computational results in the paper entitled as "Generalized Order Acceptance and Scheduling Problem with Batch Delivery: Models and Metaheuristics" (Tarhan and Oğuz) and to be... more
This data set includes the test instances and computational results in the paper entitled as "Generalized Order Acceptance and Scheduling Problem with Batch Delivery: Models and Metaheuristics" (Tarhan and Oğuz) and to be published in Computers and Operations Research with the following doi: https://doi.org/10.1016/j.cor.2021.105414.
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Port terminals processing large cargo vessels play an important role in bulk material supply chains. This paper addresses the question of how to allocate vessels to a location on a berth and the sequence in which the vessels should be... more
Port terminals processing large cargo vessels play an important role in bulk material supply chains. This paper addresses the question of how to allocate vessels to a location on a berth and the sequence in which the vessels should be processed in order to minimize delays. An important consideration in the berth allocation is the presence of tidal constraints that limit the departure of fully loaded vessels from the terminal. We show how the berth allocation problem can be modeled as an integer program and discuss a number of ways to tighten the formulation in order to make it computationally tractable. In addition, a two-phase method is developed for solving these problems. Empirical computational results demonstrate an order of magnitude improvement in performance. The two new approaches can solve significantly larger instances, producing faster solutions for small instances and much tighter bounds for large instances.