Browsing by Author "Kandiller, Levent"

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Citation - WoS: 10
Citation - Scopus: 13
A Bus Crew Scheduling Problem with Eligibility Constraints and Time Limitations
(ELSEVIER SCIENCE BV, 2017) Hande Oztop; Ugur Eliiyi; Deniz Tursel Eliiyi; Levent Kandiller; Öztop, Hande; Kandiller, Levent; Eliiyi, Uǧur; Eliiyi, Deniz Türsel; HB Celikoglu; AH Lav; MA Silgu
In this study we consider a real life crew scheduling problem (CSP) of a public bus transportation authority where the objective is to determine the optimal number of different types of crew members with a minimum cost that cover a given set of tasks regarding working and spread time limitations. Each driver has a spread time limit from the start time to the end time of his/her shift including the idle times. Additionally a driver cannot exceed the maximum total working time limit. The processing times of the tasks assigned to each driver are included in his/her working time as well as the sequence-dependent setup times. As our study is inspired from a real life CSP the tasks can require different types of vehicles that require different crew capabilities. Therefore there are several crew classes based on the competencies required to use certain vehicle types inducing eligibility constraints in the problem. We formulate a Tactical Fixed Job Scheduling Problem based binary programming model for the problem. In the formulation we consider only processing times of tasks as working time. In order to avoid defining an additional sequence control variable that explodes the model size and in turn ruins solution performance we develop an iterative valid inequality generation scheme which eliminates task sequences exceeding the total working time when setup times are included. The performance of the developed model is investigated through a comprehensive experimentation and the numerical results are reported. The results show that our optimal seeking solution procedure is quite effective in terms of solution time for instances with up to 120 tasks. (C) 2017 The Authors. Published by Elsevier B.V.
Citation - WoS: 15
Citation - Scopus: 15
A constraint programming approach to a real-world workforce scheduling problem for multi-manned assembly lines with sequence-dependent setup times
(Taylor and Francis Ltd., 2024) Funda Güner; Abdül Kadir Görür; Benhür Satır; Levent Kandiller; John H. Drake; Satir, Benhur; Gorur, Abdul K.; Kandiller, Levent; Guner, Funda; Drake, John H.
For over five decades researchers have presented various assembly line problems. Recently assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study however concentrates on assigning tasks to workers already assigned to a specific workstation rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods one using mixed integer linear programming and the other using constraint programming to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 23
A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems
(Institute of Electrical and Electronics Engineers Inc., 2020) Damla Kizilay; M. Fatih Tasgetiren; Hande Oztop; Levent Kandiller; Ponnuthurai Nagaratnam Suganthan; Kizilay, Damla; Tasgetiren, M. Fatih; Suganthan, P. N.; Oztop, Hande; Kandiller, Levent
In this paper a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known the performance of a DE algorithm depends on the mutation strategy and its control parameters namely crossover and mutation rates. For this reason the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore a new mutation operator namely DE/Best to current/l is proposed in the DE-QL algorithm. In this paper 57 EDPs provided in 'Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results' are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 1
A Discrete-Time Resource Allocated Project Scheduling Model
(Springer Science and Business Media Deutschland GmbH, 2022) Berkay Çataltuğ; Helin Su Çorapcı; Levent Kandiller; Fatih Kağan Keremit; Giray Bingöl Kırbaş; Özge Ötleş; Atakan Özerkan; Hazal Tucu; Damla Yüksel; Yuksel, Damla; Cataltug, Berkay; Corapci, Helin Su; Keremit, Fatih Kagan; Otles, Ozge; Kirbas, Giray Bingol; Kandiller, Levent; N.M. Durakbasa , M.G. Gençyılmaz
Project Management involves the implementation of knowledge and skills to meet project requirements with beneficial tools and techniques. Project Management consists of tools that provide improvement in the pillars of time cost and quality. Discrete-Time Resource Allocated Project Scheduling Model (DTRAPS) is developed to minimize time to maximize quality and to minimize the cost of a project together in one tool. By means of this tool it is possible to allocate the resources over the project tasks in an optimized way with respect to each pillar of the triangle. Moreover sensitivity analysis on the model is done with the number of activities resource number and available time window parameters. The results indicate that the model is robust. Since the model is taking a longer CPU time in solving large problems heuristics such as Greedy Smallest Requirements First (SRF) Largest Requirements First (LRF) and Randomized are developed together with Swap improvement algorithm. Heuristics are compared and analyzed. Last of all a dynamic and user-friendly decision support system is developed on Excel-VBA for the model solution via CPLEX solver and heuristics. © 2022 Elsevier B.V. All rights reserved.
A DSS for Competency-Based Workforce Scheduling for Multi-production Lines
(Springer Science and Business Media Deutschland GmbH, 2023) Ziya Arsan; Bilge Bayrak; Selin Kader; Bilge Özen; Mert Turan Sarıca; Batuhan Türkan; Ege Duran; Levent Kandiller; Bayrak, Bilge; Özen, Bilge; Sarıca, Mert Turan; Kader, Selin; Arsan, Ziya; Kandiller, Levent; Türkan, Batuhan; N.M. Durakbasa , M.G. Gençyılmaz
Today effective workforce scheduling is crucial for companies. This paper presents a workforce scheduling problem for multiple production lines. This study aims to develop an efficient and methodological shift scheduling algorithm for a heating device plant to reduce unnecessary overtime and improve quality and performance. The workers are assigned to the stations based on their qualification rates and a line balancing option is provided if required. After linearizing the non-linear objective function using an innovative approach the mathematical model is developed and solved by OPL CPLEX STUDIO IDE 12.10.0. With toy and original data. The mathematical model is verified and validated and the results provide the optimal assignment of the workers to maximize the quality of lines. Since the company cannot use CPLEX due to license restrictions the mathematical model is integrated into the Python program using the Pulp library to provide a solver option to the company. Moreover the Hungarian method is coded in Python to create an instant solution. Since the company prefers to assign the workers manually a greedy method is developed to improve the manual solution. Finally a user-friendly Decision Support System is designed to support the decision-making processes. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 6
A General Variable Neighborhood Search for the NoIdle Flowshop Scheduling Problem with Makespan Criterion
(Institute of Electrical and Electronics Engineers Inc., 2019) Liangshan Shen; M. Fatih Tasgetiren; Hande Oztop; Levent Kandiller; Liang Gao; Shen, Liangshan; Tasgetiren, Mehmet Fatih; Gao, Liang; Oztop, Hande; Kandiller, Levent
This paper proposes a novel general variable neighborhood search (GVNS) algorithm to solve the no-idle flowshop scheduling problem with the makespan criterion. The initial solution of the GVNS is generated using the FRB5 heuristic. In the outer loop insert and swap operations are employed to shake the permutation. In the inner loop of variable neighborhood descent procedure two effective algorithms namely Iterated Greedy (IG) and Variable Block Insertion Heuristic (VBIH) algorithms are used. Note that an effective referenced insertion scheme is employed in these IG and VBIH algorithms. The proposed GVNS algorithm is compared with the standard IG algorithm using the benchmark instances. The computational experiments show that the GVNS performs much better than the standard IG. Furthermore the results of the standard IG and GVNS algorithms are compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS algorithm improves some of the current best-known solutions in the literature. Consequently it can be said that the GVNS is very effective for the no-idle flowshop scheduling problem with the makespan criterion. © 2020 Elsevier B.V. All rights reserved.
A Hybrid Flow Shop Scheduling Problem
(Springer Science and Business Media Deutschland GmbH, 2020) Ayşegül Eda Özen; Gülce Çini; Merve Çamlıca; Nilay Çınar; Hasan Bahtiyar Soydan; Levent Kandiller; Hande Oztop; Çini, Gülce; Özen, Ayşegül Eda; Çamlıca, Merve; Kandiller, Levent; Öztop, Hande; Çınar, Nilay; Soydan, Hasan Bahtiyar; N.M. Durakbasa , M.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , M.G. Gençyilmaz
Hybrid flow shop environment generally refers to the flow shop with multiple parallel machines per stage. Hybrid flow shop scheduling problem (HFSP) is a complex combinatorial optimization problem that came across in many real-life problems. In this study a real-life HFSP of a lubricant company is considered where the aim is to minimize total weighted completion time of the jobs. Apart from classical HFSPs the studied problem has additional constraints such as machine eligibility sequence-dependent setup times and machine capacities. Due to the additional constraints in the system a novel mixed integer linear programming model is proposed for the studied HFSP with three stages. As the problem is NP-hard two constructive heuristic algorithms and an improvement heuristic algorithm are also developed. The performance of the proposed heuristic algorithms is evaluated by comparisons with the optimal results obtained from the mathematical model. The extensive computational results show that proposed heuristic algorithms find near optimal results in reasonable computational times. Sensitivity analysis is also performed for the weight parameter of the problem which indicates that the proposed heuristic algorithms also perform very well for different weight parameter values. Finally the proposed heuristic algorithms are integrated into a user-friendly decision support system using Microsoft Excel VBA interface to provide an efficient scheduling tool for the company. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 9
A Multi-compartment Vehicle Routing Problem for Livestock Feed Distribution
(SPRINGER INTERNATIONAL PUBLISHING AG, 2017) Levent Kandiller; Deniz Tursel Eliiyi; Bahar Tasar; Kandiller, Levent; Tasar, Bahar; Eliiyi, Deniz Tursel; KF Doerner; I Ljubic; G Pflug; G Tragler
In the well-known Vehicle Routing Problem (VRP) customer demands from one or more depots are to be distributed via a fleet of vehicles. Various objectives of the problem are considered in literature including minimization of the total distance/time traversed by the fleet during distribution the total cost of vehicle usage or minimizing the maximum tour length/time. In this study we consider a multi-compartment VRP with incompatible products for the daily solution of a livestock feed distribution network where each livestock farm requests one type of feed from a single depot and the vehicles have several compartments. The objective is to minimize the total cost of distribution. Although VRP is a well-studied problem in literature multi-compartment VRP is considered only by few authors and our problem differs from the existing ones due to special operational constraints imposed by the situation on hand. We formulate a basic mathematical model for the problem and present possible extensions. We design a computational experiment for testing the effects of uncontrollable parameters over model performance on a commercial solver and report the results. The proposed model can easily be adapted to other distribution networks such as food and fuel/chemicals.
A Multi-sided and Multi-model Assembly Line Balancing Problem
(Springer Science and Business Media Deutschland GmbH, 2021) Seda Gemici; Emine Otuzbir; İrem Almila Koçyiğit; Sinem Pekelli; Fethi Tüzmen; Hande Oztop; Levent Kandiller; Pekelli, Sinem; Otuzbir, Emine; Gemici, Seda; Koçyiğit, İrem Almila; Tüzmen, Fethi; Öztop, Hande; Kandiller, Levent; N.M. Durakbasa , M.G. Gençyılmaz
In this paper we study a real-life assembly line balancing problem (ALBP) of a cooler manufacturer brand in Manisa Turkey. The aim of this study is to create an effective assembly line balancing tool for the company which minimizes the number of stations and balances the total workloads of the stations while keeping the number of products produced the same. The studied ALBP is Type-1 ALBP that minimizes the number of workstations given a cycle time which is determined by a bottleneck operation. However different from the standard Type-1 ALBP some of the stations are two-sided stations in the studied assembly line. There are special constraints in the studied ALBP such as concurrent tasks preemptive tasks zone-restricted tasks and parallel tasks. Due to these additional characteristics of the system a novel mixed-integer linear programming (MILP) model is proposed for the studied ALBP to minimize the number of workstations. A secondary objective which balances the workload of the stations is also considered in the proposed MILP model using a lexicographic optimization. The computational experiments show that the proposed MILP model can obtain the optimal solution in reasonable computational time. When the model results are compared with the current system there is a 44% improvement in the number of stations on average. Furthermore a sensitivity analysis is performed to analyze the trade-off between the number of stations and cycle time criteria employing an ε-constraint method. Finally a user-friendly DSS is developed by embedding the proposed MILP model. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 17
Citation - Scopus: 23
A Novel General Variable Neighborhood Search through Q-Learning for No-Idle Flowshop Scheduling
(IEEE, 2020) Hande Oztop; Mehmet Fatih Tasgetiren; Levent Kandiller; Quan-Ke Pan; Tasgetiren, Mehmet Fatih; Oztop, Hande; Kandiller, Levent; Pan, Quan-Ke
In this study a novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective. In the outer loop of the GVNS-QL insertion and exchange operators are used to shaking the permutation. On the other hand in the inner loop of variable neighborhood descent procedure variable iterated greedy and variable block insertion heuristic algorithms are employed with two effective insertion local search procedures. The proposed GVNS-QL defines the parameters of the algorithm using a Q-learning mechanism. The developed GVNS-QL algorithm is compared with the traditional iterated greedy (IG) algorithm using the well-known benchmark set. The comprehensive computational experiments show that the GVNS-QL outperforms the traditional IG algorithm. The results of the IG and GVNS-QL algorithms are also compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS-QL algorithm improves the current best-known solutions for 104 out of 250 instances.
A Warehouse Management Decision Support System for a Spare Parts Company
(Springer Science and Business Media Deutschland GmbH, 2024) Burak Kurt; Güner Şirin; Melis Karakurt; Sevde Zümrüt İspay; Simge Güçlükol Ergin; Levent Kandiller; Karakurt, Melis; Kurt, Burak; İspay, Sevde Zümrüt; Şirin, Güner; Kandiller, Levent; Ergin, Simge Güçlükol; N.M. Durakbasa , M.G. Gençyılmaz
Warehouse management is considered one of the essential components of a supply chain. Inadequate storage space and inefficient available storage are common problems in designing warehouses. This study underlines the need for an effective warehouse management policy because of the limited space for finished goods. The complementary solution is to ensure the highest-selling inventory is easily accessible by placing it at the most accessible point. The position of the finished goods and deciding the sequence of routes to perform the fastest loading and unloading work are critical factors in reaching maximum efficiency. This project aims to provide easy access to stored goods and minimize the travel time between the picking and the placing positions to avoid inefficient routes and disruptions by strategically planning the warehouse layout design and running each operation in the best sequential manner. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
An alternative MILP model for makespan minimization on assembly lines
(SPRINGER HEIDELBERG, 2017) Sel Ozcan; Deniz Tursel Eliiyi; Levent Kandiller; Kandiller, Levent; Ozcan, Sel; Eliiyi, Deniz Türsel
The Simple Assembly Line Balancing Problem-2 (SABLP-2) is defined as partitioning the tasks among stations in order to minimize the cycle time given the number of stations. SALBP-2 reduces to the identical parallel machine scheduling problem with makespan minimization (P-m parallel to C-max) when precedence relations are ignored providing a lower bound. In a certain layout setting tasks revisiting the same station over consecutive tours might be preferable when the sole objective is to minimize the makespan of producing the order quantity. In this study the tradeoff between the makespans obtained from SALBP-2 and (P-m parallel to C-max) as a function of order quantity is analyzed. A piecewise linear concave makespan function is observed. We developed an alternative model formulation and an iterative solution scheme for makespan minimization for all possible order quantities. The results of our computational experiment indicate that SALBP-2 outperforms for small order quantities whereas (P-m parallel to C-max) yields the best results for larger order quantities. However there is a certain range of order quantity for which the proposed model dominates the other two. Our results are validated in benchmark instances.
Citation - WoS: 43
Citation - Scopus: 53
An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption
(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Damla Yuksel; M. Fatih Tasgetiren; Levent Kandiller; Liang Gao; Yüksel, Damla; Taşgetiren, M. Fatih; Gao, Liang; Kandiller, Levent
In manufacturing scheduling sustainability concerns that raise from the service-oriented performance criteria have seldom been studied in the literature. This study aims to fill this gap in the literature by integrating the different energy consumption levels at the operational level. Since energy-efficient scheduling ideas have recently been increasing its popularity in industry due to the need for sustainable production this study will be a good resource for future energy-efficient scheduling problems. Energy consumption in high volume manufacturing is a significant cost item in most industries. Potential energy saving mechanisms are needed to be integrated into manufacturing facilities for cost minimization at the operational level. A leading energy-saving mechanism in manufacturing is to be able to adapt/change the machine speed levels which exactly determines the energy consumption of the machines. Hence in this study the afore-mentioned framework is applied to the no-wait permutation flowshop scheduling problem (NWPFSP) which is a variant of classical permutation flowshop scheduling problems. However it has various critical applications in industries such as chemical pharmaceutical food-processing etc. This study proposes both mixed-integer linear programming (MILP) and constraint programming (CP) model formulations for the energy-efficient bi-objective no-wait permutation flowshop scheduling problems (NWPFSPs) considering the total tardiness and the total energy consumption minimization simultaneously. This problem treats total energy consumption as a second objective. Thus the trade-off between the total tardiness - a service level measurement indicator - and the total energy consumption - a sustainability level indicator - is analyzed in this study. Furthermore due to the NP-hardness nature of the first objective of the problem a novel multi-objective discrete artificial bee colony algorithm (MO-DABC) a traditional multi-objective genetic algorithm (MO-GA) and a variant of multi-objective genetic algorithm with a local search (MO-GALS) are proposed for the bi-objective no-wait permutation flowshop scheduling problem. Besides the proposed algorithms are compared with the multi-objective energy-efficient algorithms from the literature. Consequently a comprehensive comparative metaheuristic analysis is carried out. The computational results indicate that the proposed MO-DABC algorithm outperforms MILP CP MO-GA MO-GALS and algorithms from the literature in terms of both cardinality and quality of the solutions. The powerful results of this study show that the proposed models and algorithms can be adapted to other energy-efficient scheduling problems such as no-idle flowshop blocking flowshop and job-shop scheduling problems or to other higher-level integrated manufacturing problems.
Citation - WoS: 50
Citation - Scopus: 56
An energy-efficient permutation flowshop scheduling problem
(Elsevier Ltd, 2020) Hande Oztop; M. Fatih Tasgetiren; D. T. Eliiyi; Quanke Pan; Levent Kandiller; Tasgetiren, M. Fatih; Öztop, Hande; Pan, Quan-Ke; Kandiller, Levent; Eliiyi, Deniz Türsel
The permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies multiple objectives related to production efficiency have been considered simultaneously. However studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work we studied two contradictory objectives namely total flowtime and total energy consumption (TEC) in a green permutation flowshop environment in which the machines can be operated at varying speed levels corresponding to different energy consumption values. A bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework. To address the conflicting objectives of minimizing TEC and total flowtime the augmented epsilon-constraint approach was employed to obtain Pareto-optimal solutions. We obtained near approximations for the Pareto-optimal frontiers of small-scale problems using a very small epsilon level. Furthermore the mathematical model was run with a time limit to find sets of non-dominated solutions for large instances. As the problem was NP-hard two effective multi-objective iterated greedy algorithms and a multi-objective variable block insertion heuristic were also proposed for the problem as well as a novel construction heuristic for initial solution generation. The performance of the developed heuristic algorithms was assessed on well-known benchmark problems in terms of various quality measures. Initially the performance of the algorithms was evaluated on small-scale instances using Pareto-optimal solutions. Then it was shown that the developed algorithms are tremendously effective for solving large instances in comparison to time-limited model. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 31
Citation - Scopus: 33
An evolution strategy approach for the distributed blocking flowshop scheduling problem
(Elsevier Ltd, 2022) Korhan Karabulut; Damla Kizilay; M. Fatih Tasgetiren; Liang Gao; Levent Kandiller; Kizilay, Damla; Tasgetiren, M. Fatih; Gao, Liang; Karabulut, Korhan; Kandiller, Levent
Scheduling in distributed production environments has become common in recent years since the advantages of multi factory manufacturing have been growing. This paper examines the distributed blocking flowshop scheduling problem (DBFSP) to minimize the makespan. Two different mathematical models namely a mixed-integer programming model and a constraint programming model were proposed to solve the considered problem to optimality. Due to the NP-Hard nature of the problem large-size instances cannot be solved by the mathematical models and an evolutionary algorithm was proposed. Three different NEH-based heuristics were used and the first three solutions are included in the initial population whereas the rest is constructed randomly. The offspring population is generated by the self-adaptive destruction and construction (DC) procedure of the iterated greedy algorithm. Self-adaptive DC procedure is achieved by the evolution strategy approach. In the local search part of the algorithm a variable local search with three neighborhood structures was applied to the solution obtained by the DC procedure. The developed mathematical models initially verified the performance of the metaheuristic algorithm by using small instances. Then the proposed algorithm was tested on the benchmark suite from the literature. The computational results indicate that the proposed algorithm outperforms the other metaheuristic algorithms from the literature. Finally the solutions of the 156 best so far were obtained by the proposed algorithm which is more effective than the existing state-of-the-art methods. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 42
Citation - Scopus: 45
An evolution strategy approach for the distributed permutation flowshop scheduling problem with sequence-dependent setup times
(Elsevier Ltd, 2022) Korhan Karabulut; Hande Oztop; Damla Kizilay; M. Fatih Tasgetiren; Levent Kandiller; Kizilay, Damla; Tasgetiren, M. Fatih; Karabulut, Korhan; Oztop, Hande; Kandiller, Levent
This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled large companies to have distributed production centers to become widespread and effective production scheduling between these centers plays a vital role in the competitiveness of companies. To provide effective scheduling for the DPFSP-SDST we propose a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model which is presented for the first time in literature to the best of our knowledge. As the CP has become a solid competitor to the MILP in the literature this study aims to exploit the effectiveness of CP to solve such a complex DPFSP-SDST. Since the problem is NP-hard we also offer an evolution strategy (ES_en) algorithm that employs a self-adaptive scheme to obtain high-quality solutions in a short time. A ruin-and-recreate procedure is also embedded into the developed ES_en. We calibrate the parameters of the proposed ES_en using a design of experiment approach. We also compare the proposed ES_en algorithm's performance with three state-of-the-art metaheuristic algorithms from the literature i.e. the IG2S (a variant of an iterated greedy algorithm with NEH2_en initialization) IGR (another variant of an iterated greedy algorithm with a restart scheme) and discrete artificial bee colony (DABC) algorithm. A detailed computational experiment is carried out to evaluate the performance of the mathematical models (MILP and CP) and the heuristic algorithms (ES_en IG2S IGR and DABC). A comprehensive benchmark set is generated for the DPFSP-SDST from the well-known PFSP instances from the literature considering various combinations of jobs machines factories and SDST settings resulting in 2880 benchmark instances. For 216 out of 240 small-size instances optimal results are reported by solving the proposed MILP and CP models whereas time-limited model results are reported for the rest. The computational results show that the CP model outperforms the MILP model in terms of the solution time for small-size instances. Initially the performance of the heuristic algorithms is verified concerning the optimal results on small-size instances. Then the performance of the heuristic algorithms is evaluated for large instances. ES_en algorithm significantly outperforms the IG2S IGR and DABC algorithms for solving large instances. The computational results show that the proposed ES_en algorithm is robust and provides good-quality solutions for the DPFSP-SDST in a short computational time. © 2022 Elsevier B.V. All rights reserved.
Analysis of a Turkey Meat Production Agri-Chain: A Simulation Study
(Springer Science and Business Media Deutschland GmbH, 2024) Nazlı Karatas Aygün; Levent Kandiller; Önder Bulut; Aygün, Nazli Karataş; Kandiller, Levent; Bulut, Önder; N.M. Durakbasa , M.G. Gençyılmaz
Poultry meat will take the greatest share of global meat consumption within ten years because of the ongoing increase. It is anticipated that this upward trend in consumption will result in a rise in the amount of poultry meat produced. Poultry meat agri-chains are composed of various interdependent components including a hatchery many farms and coops and a slaughterhouse generally. The current study tackles the analysis of a real-life agri-chain reaction to changes in hatchery slaughterhouse and farm/coop capacities under different stochastic demand parameters. A discrete-event simulation model is used. The simulation model introduced here is a generic model such that model parameters can be played to reveal different real-life cases. After verifying the model using a toy problem the model is used to observe the impacts of several model parameters on the system-wide performance measures. © 2024 Elsevier B.V. All rights reserved.
Beklemesiz permütasyon akış tipi çizelgeleme problemleri için yeni çözüm teknikleri
(2024) Yüksel, Damla; Kandiller, Levent
No-Wait Permutation Flowshop Scheduling Problem (NWPFSP) is a scheduling problem variant where jobs must proceed through machines in a fixed order without waiting times between operations. This thesis explores innovative solution techniques for the NWPFSPs. The primary contributions of this thesis are twofold: single-objective optimization and bi-criteria optimization. For single-objective optimization, this thesis examines five mathematical model formulations — three using Mixed-Integer Linear Programming (MILP) and two using Constraint Programming (CP) — focused on separately minimizing makespan, total flow time, and total tardiness. One MILP model is enhanced with valid inequalities to address these objectives. A new Lower Bound (LB) mechanism based on the Shortest Path (SP) algorithm is developed to optimize makespan, total flow time, total tardiness, and the number of tardy jobs separately. Following that, two mathematical models, one belonging to the MILP class and the other to the CP class, have been studied for the number of tardy job minimization in NWPFSPs. A novel upper bound, the Sacrifice and Rearrange Heuristic (SRH), is introduced to minimize the number of tardy jobs. Optimizing the number of tardy jobs in NWPFSPs requires high-quality due dates, as they are crucial for improving performance metrics related to lateness. A new mechanism for generating high-quality due dates has been developed to address this. Incorporating the Sacrifice and Rearrange Heuristic (SRH), this mechanism ensures practical and effective due dates. For bi-criteria optimization, the NWPFSP is approached as a combinatorial optimization problem with two objectives, aiming to minimize total flow time and makespan simultaneously: Bi-Criteria No-Wait Permutation Flowshop Scheduling Problems (BI-CRI NWPFSPs). Initially, an MILP model formulation is explored to address BI-CRI NWPFSPs. Following this, Q-learning-guided algorithms are developed for Bi-CRI NWPFSPs. Q-learning, a well-known reinforcement learning technique, is employed to direct action selection, thereby reducing the need for random exploration during the iterative metaheuristic process. The developed Q-learning guided metaheuristics are Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BC-IGQL) and Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIHQL). The performance of these algorithms is compared with other state-of-the-art approaches. Thus, this thesis advances the literature on the NWPFSPs by developing new solution techniques for both single-objective and bi-criteria scenarios.
Cost-Optimal Egg Procurement Planning via MILP: A Smart System for Demand Satisfaction Under Fuzzy Constraints
(Springer Science and Business Media Deutschland GmbH, 2025) Canan Akgün; Nazlı Karatas Aygün; Yağmur Başgöl; Melisa Deligöz; Levent Kandiller; Ataberk Köseoğlu; Duru Özcanlı; Özcanlı, Duru; Deligöz, Melisa; Aygün, Nazlı Karataş; Akgün, Canan; Kandiller, Levent; Başgöl, Yağmur; Köseoğlu, Ataberk; C. Kahraman , S. Cebi , B. Oztaysi , S. Cevik Onar , C. Tolga , I. Ucal Sari , I. Otay
This study optimizes turkey egg supply planning for a meat production company. The primary objective is to determine optimal weekly procurement quantities that minimize costs while effectively meeting production demands and hatchery schedules. Both domestic and international suppliers with varying capacities transportation and costs are handled. The originality of this study lies in integrating detailed real-world constraints—supplier variability international logistics complexities regulatory requirements breed-specific considerations and hatchery limitations—through an inclusive Mixed-Integer Linear Programming (MILP) model. For balanced resource allocation suppliers breed types and penalty costs applied to excess supply and unmet demand are assigned. Procurement decisions all together optimize transportation customs clearance ordering costs and supplier selection. Computational experiments using real-life data demonstrate improved procurement efficiency reduced waste due to surplus and perishability and enhanced operational stability. This approach prevents bottlenecks ensuring cost-effective resource utilization. Results validate the model’s effectiveness in streamlining procurement strategies for perishable goods supply chains. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 33
Citation - Scopus: 37
Ensemble of metaheuristics for energy-efficient hybrid flowshops: Makespan versus total energy consumption
(Elsevier B.V., 2020) Hande Oztop; M. Fatih Tasgetiren; Levent Kandiller; D. T. Eliiyi; Liang Gao; Tasgetiren, M. Fatih; Gao, Liang; Öztop, Hande; Kandiller, Levent; Eliiyi, Deniz Türsel
Due to its practical relevance the hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature with the objectives related to production efficiency. However studies regarding energy consumption and environmental effects have rather been limited. This paper addresses the trade-off between makespan and total energy consumption in hybrid flowshops where machines can operate at varying speed levels. A bi-objective mixed-integer linear programming (MILP) model and a bi-objective constraint programming (CP) model are proposed for the problem employing speed scaling. Since the objectives of minimizing makespan and total energy consumption are conflicting with each other the augmented epsilon (ε)-constraint approach is used for obtaining the Pareto-optimal solutions. While close approximations for the Pareto-optimal frontier are obtained for small-sized instances sets of non-dominated solutions are obtained for large instances by solving the MILP and CP models under a time limit. As the problem is NP-hard two variants of the iterated greedy algorithm a variable block insertion heuristic and four variants of ensemble of metaheuristic algorithms are also proposed as well as a novel constructive heuristic. The performances of the proposed seven bi-objective metaheuristics are compared with each other as well as the MILP and CP solutions on a set of well-known HFSP benchmarks in terms of cardinality closeness and diversity of the solutions. Initially the performances of the algorithms are tested on small-sized instances with respect to the Pareto-optimal solutions. Then it is shown that the proposed algorithms are very effective for solving large instances in terms of both solution quality and CPU time. © 2020 Elsevier B.V. All rights reserved.