Browsing by Author "Ornek, M. Arslan"

Now showing 1 - 9 of 9

Citation - WoS: 6
Citation - Scopus: 8
A goal programming approach to lean production system implementation
(Taylor and Francis Ltd., 2023) Sadik Serhat Karakütük; Mustafa Arslan Ornek; Ornek, M. Arslan; Karakutuk, S. Serhat
Companies use different production policies to ensure customer demands are satisfied in time. To track the performance of production policies some important Key Performance Indicators related to production control and management are On-Time Delivery (OTD) machine or line productivity (OEE–Overall Equipment Efficiency) optimization of inventory levels between workstations (WIP) customer satisfaction i.e. prioritization of customer orders according to requirements of the customer and backlog minimization. In this study a real-life production management problem is described modelled and solved to improve customer delivery rate and to plan manufacturing orders using lean production tools. Currently the Make-to-Stock policy is used for semi-finished materials. The problems encountered are low customer service level high level of WIP between operations and efficiency losses. Therefore the goal of this study is to increase efficiency (OTD OEE and customer satisfaction) by minimizing setup time decreasing WIP by minimizing earliness backlog quantity level and improving service level by minimizing lateness of orders. Since these goals contradict each other we propose a multi-objective mathematical formulation with a setup carryover strategy. Then we formulate a Goal Programming (GP) model and solve it by using different GP variants and the normalization method. Finally we discuss numerical results and provide our final remarks and conclusions. © 2023 Elsevier B.V. All rights reserved.
A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion
(Springer Science and Business Media Deutschland GmbH, 2021) M. Fatih Tasgetiren; Hande Oztop; Quanke Pan; Mustafa Arslan Ornek; Talya Temizceri; Tasgetiren, M. Fatih; Temizceri, Talya; Oztop, Hande; Pan, Quan-Ke; Ornek, M. Arslan
Permutation flow shop scheduling problem is a well-known problem in the scheduling literature. Even though various multi-objective permutation flowshop scheduling problems have been studied in the literature energy consumption consideration in scheduling is still very seldom. In this paper we consider a bi-objective permutation flowshop scheduling problem with the objectives of minimizing the total energy consumption and the makespan. We present a bi-objective mixed integer programming model for the problem applying a speed-scaling approach. Then we employ the augmented ε -constraint method to generate the Pareto-optimal solution sets for small-sized instances. For larger instances we use the augmented ε -constraint method with a time limit on CPLEX solver to approximate the Pareto frontiers. We also propose a heuristic approach which employs a very recent variable block insertion heuristic algorithm. In order to evaluate performance of the proposed algorithm we have carried out detailed computational experiments using well-known benchmarks from the literature. First we present the performance of the proposed algorithm on small-sized problems, then we show that the proposed algorithm is very effective to solve larger problems as compared with the time-limited CPLEX. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 9
An application of a circular economy approach to design an energy-efficient heat recovery system
(ELSEVIER SCI LTD, 2021) S. Serhat Karakutuk; Sener Akpinar; M. Arslan Ornek; Akpinar, Sener; Ornek, M. Arslan; Karakutuk, S. Serhat
This paper aims to develop an optimal real-life energy-efficient design for a production plant within the concept of the circular economy. The problem is to install a Heat Recovery System (HRS) that utilizes the hot oil used by the compressors to heat the water for the central heating system. To achieve the desired level of energy efficiency this design problem must be formulated from both the optimization and sustainability points of view. Additionally this design problem must also consider the investment cost. In line with this purpose this paper formulates this design problem as an optimization problem employing a mathematical programming approach as a single objective and as a multi-objective optimization problem through a goal programming approach. Besides this paper uses the return on investment as a key performance indicator since it deals with a real-life design problem with an investment cost. The related design problem is solved with the single objective and multiobjective versions of the developed mathematical programming model via a commercial solver to identify different design alternatives and hence giving the decision-maker to make a selection option. Finally the capability of the developed mathematical programming model is tested on a set of randomly generated problems. The obtained results indicate that the developed mathematical programming model is a successful decision support system since its single and multi-objective versions are capable of identifying energy-efficient production designs within the context of the real-life problem on hand and the circular economy.
Citation - WoS: 2
Citation - Scopus: 4
Combinatorial optimization methods for yarn dyeing planning
(Springer, 2025) Ege Duran; Cemalettin Öztürk; Mustafa Arslan Ornek; Duran, Ege; Ozturk, Cemalettin; Ornek, M. Arslan
Managing yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack bin packing and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However several practical factors such as physical attributes of customer orders dyeing machine eligibility conditions like flotte color type chemical recipe and volume capacity of dye make this problem significantly unique. Furthermore alongside its economic aspects minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective which adds another complexity for planners. Hence in this paper we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research including a mixed-integer programming model for a novel industrial problem providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly and addressing the inefficiencies of manual scheduling in textile companies, reducing a time-consuming planning task from hours to minutes. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Heuristic methods for integrated incremental schedule design and fleet assignment problem for hub and spoke network
(Inderscience Publishers, 2024) Melis Tan Tacoglu; Mustafa Arslan Ornek; Yigit Kazancoglu; Ornek, M. Arslan; Tacoglu, Melis Tan; Kazancoglu, Yigit
Managing airline inbound and outbound schedules among passenger demand and aircraft utility complexity are addressed through three proposed heuristic methods for integrated schedule design and fleet assignment problem (ISDFAP) in single-hub two-flight leg hub-and-spoke networks. The second heuristic considering waiting time and available seat capacity contrasts with the first focusing only on waiting time for passenger-flight assignments. Meanwhile the third heuristic considers aircraft buffer time and stay time restrictions at the destination. Comparing the first two heuristics reveals that considering seat availability does not reduce waiting time. Flight departure time adjustments and overall timetable changes depend significantly on buffer time and wait time criteria. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 15
Integer and constraint programming model formulations for flight-gate assignment problem
(SPRINGER HEIDELBERG, 2022) M. Arslan Ornek; Cemalettin Ozturk; Ipek Sugut; Sugut, Ipek; Ornek, M. Arslan; Ozturk, Cemalettin
Flight-gate assignment problems are complex real world problems involving different constraints. Some of these constraints include plane-gate eligibility assigning planes of the same airline and planes getting service from the same ground handling companies to adjacent gates buffers for changes in flight schedules night stand flights priority of some gates over others and so on. In literature there are numerous models to solve this highly complicated problem and tackle its complexity. In this study first we propose two different integer programming models namely timetabling and assignment based models and then a scheduling based constraint programming model to solve the problem to optimality. These models prove to be highly efficient in that the computational times are quite short. We also present the results for one day operation of an airport using real data. Finally we present our conclusions based on our study along with the possible further research.
Citation - WoS: 5
Citation - Scopus: 7
Model-based heuristic for counter assignment problem with operational constrains: A case study
(ELSEVIER SCI LTD, 2019) M. Arslan Ornek; Cemalettin Ozturk; Ipek Sugut; Sugut, Ipek; Ornek, M. Arslan; Ozturk, Cemalettin
Check-in counters have a great impact on the quality of service for airports. It is airport management's responsibility to provide check-in counters to airlines. Each check-in group (i.e. flights sharing the same resources) has a counter demand and this gives rise to a counter assignment problem. This is due to a number of objectives and constraints under which check-in groups are allocated to check-in counters. In this paper we develop an Integer Programming model to optimally assign incoming flights to check-in counters and propose a decomposition algorithm to solve the allocation problem in a reasonable time. Computational results from a medium sized airport indicate a better utilisation of check-in counters enabling airport management to reduce/postpone investment in additional check-in counters.
Multiple Size Cutting Stock Problem in Steel Industry
(Springer Science and Business Media Deutschland GmbH, 2023) Damla Artan; Pelin Tezcan; Aysu Karlı; Egemen Sertpoyraz; Deniz Mermerci; Ege Efekan; Ege Duran; Mustafa Arslan Ornek; Tezcan, Pelin; Ornek, M. Arslan; Karlı, Aysu; Sertpoyraz, Egemen; Efekan, Ege; Mermerci, Deniz; Artan, Damla; N.M. Durakbasa , M.G. Gençyılmaz
This study solves a one-dimensional cutting stock problem with multiple stock lengths. It is applied in a manufacturing setting where rolls of steel rods of different lengths are cut according to customer requirements. The one-dimensional cutting stock problem (CSP) is an NP-hard problem including discrete demands and capacitated planning objectives. It is solved using column generation techniques. This study aims to develop a production plan that minimizes the waste of cutting steel rods of different lengths and diameters in required lengths. The approach to solving the problem has two steps. The first step is a heuristic algorithm that produces a cutting pattern at every iteration which is then fed into a novel mathematical model to determine an optimal solution. An initial solution is obtained using randomly generated cutting patterns for the mathematical model. The algorithm terminates after a given number of iterations. The paper also proposes a Decision Support System addresses application issues and concludes with further studies. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 4
Optimisation and heuristic approaches for assigning inbound containers to outbound carriers
(Routledge info@tandf.co.uk, 2017) Cemalettin Öztürk; F. Zeynep Sargut; Mustafa Arslan Ornek; D. T. Eliiyi; Ozturk, Cemalettin; Sargut, F. Zeynep; Ornek, M. Arslan; Türsel Eliiyi, Deniz; Eliiyi, Deniz Tursel
Due to economical and/or geographical constraints most of the time overseas containers cannot be directly shipped to their destinations. These containers visit transhipment ports where they are first unloaded and temporarily stored and then loaded onto smaller vessels (feeders) to be transported to their final destinations. The assignment of these containers to outbound vessels necessitates several factors to be taken into account simultaneously. In this paper we develop a mathematical model to reflect multiple objectives with priorities and to assign these containers to different vessels at the transit container port terminal. Although we solve a single-objective (with the weighted sum of objectives) mathematical model to optimality we also propose two heuristic approaches to solve this complex problem for a transit agency. The first heuristic is shipment based and has four variants differing in how the opportunity costs of the assignments are calculated. The second greedy heuristic is trip based where the goal is to maximise the capacity utilisation of the vessels. The heuristics return very promising solutions in ignorable computational times. We also provide real-life cases and present our conclusions. © 2017 Elsevier B.V. All rights reserved.