Browsing by Author "Hamzadayi, Alper"

Now showing 1 - 16 of 16

Balancing of Mixed-Model Two-Sided Assembly Lines Using Teaching-Learning Based Optimization Algorithm
(Pamukkale Univ, 2018) Hamzadayi, Alper
The Teaching-Learning Based Optimization (TLBO) algorithm is a population-based optimization technique that has been shown to be competitive against other population-based algorithms. The main purpose of this paper is to solve the balancing problem of mixed-model two-sided assembly lines by using TLBO algorithm first time in the literature. Most recently, hybrid teaching-learning-based optimization (HTLBO) algorithm is proposed by [1] for solving the balancing of stochastic simple two-sided assembly line problem. The HTBLO algorithm is compared with the well-known 10 different meta-heuristic algorithms in the literature in [1]. The tests performed underlined that HTLBO algorithm presented more outstanding performance when compared to other algorithms. In this paper, HTLBO algorithm is also adapted for solving the problem of balancing mixed-model two-sided assembly line and its performance is analysed. The objective function of this study is to minimize the number of mated-stations and total number of stations for a predefined cycle time. A comprehensive computational study is conducted on a set of test problems that are taken from the literature and the performance of the algorithms are compared with existing approaches. Experimental results show that TLBO algorithm has a noticeable potential against to the best-known heuristic algorithms and HTLBO algorithm results show that it performs well as far as the best-known heuristic algorithms for the problem in the literature.
Benders Decomposition for the Mixed No-Idle Permutation Flowshop Scheduling Problem
(Springer, 2020) Bektas, Tolga; Hamzadayi, Alper; Ruiz, Ruben
The mixed no-idle flowshop scheduling problem arises in modern industries including integrated circuits, ceramic frit and steel production, among others, and where some machines are not allowed to remain idle between jobs. This paper describes an exact algorithm that uses Benders decomposition with a simple yet effective enhancement mechanism that entails the generation of additional cuts by using a referenced local search to help speed up convergence. Using only a single additional optimality cut at each iteration, and combined with combinatorial cuts, the algorithm can optimally solve instances with up to 500 jobs and 15 machines that are otherwise not within the reach of off-the-shelf optimization software, and can easily surpass ad-hoc existing metaheuristics. To the best of the authors' knowledge, the algorithm described here is the only exact method for solving the mixed no-idle permutation flowshop scheduling problem.
A Branch-And Approach for the Distributed No-Wait Flowshop Scheduling Problem
(Pergamon-elsevier Science Ltd, 2022) Avci, Mustafa; Avci, Mualla Gonca; Hamzadayi, Alper
The distributed no-wait flowshop scheduling problem (DNWFSP) is an extension of the permutation flowshop scheduling problem with multiple factories and no-wait constraints. The DNWFSP consists of two decisions, namely, assigning jobs to the factories and sequencing the set of jobs assigned to the same factory. The no -wait constraints require that jobs have to be processed without any interruption between operations. Since the introduction of the DNWFSP, a number of metaheuristic approaches were developed to solve it. However, there exists no exact solution approach for the DNWFSP to the best of our knowledge. In this regard, a branch -and-cut (BC) algorithm is proposed to solve the DNWFSP. The proposed BC is integrated with a heuristic algorithm to obtain good upper bounds. Moreover, a set of symmetry breaking constraints are employed in the models to strengthen the formulations. The performance of BC is evaluated on a set of benchmark problem instances available in the related literature. The proposed BC is numerically compared with mixed-integer programming formulations of the DNWFSP which are solved by a commercial solver. The results obtained from the computational experiments reveal the effectiveness of the proposed approach. The proposed BC is able to solve all small-size instances, as well as, 206 out of 660 large-size instances to optimality. Besides, it is worth to mention that the average percentage gap for the large-size instances with two factories is only 0.43%.
Capability-Based Machine Layout With a Matheuristic-Based Approach
(Pergamon-elsevier Science Ltd, 2022) Baykasoglu, Adil; Subulan, Kemal; Hamzadayi, Alper
Capability-based machine layout (CB-ML) problem is firstly introduced in this paper. In the conventional ma-chine layout problem, part flow matrix is generated from parts' machine routes to minimize total part flows. However, defining part flow matrix based on the machines' routes (instead of processing capability requirements of parts) restricts facility designers to utilize available flexibility in manufacturing systems. In this research, parts' processing requirements are defined in terms of Resource Elements (REs), which describe unique pro-cessing capabilities and the processing capability overlaps of machines. If part flow matrix is defined in terms of REs, it becomes possible to utilize available flexibility in a more effective manner. However, physical part flows cannot be identified directly from the RE-based flow matrices. Because, the processing requirements of manu-factured parts can be satisfied from alternative machines that contain the required REs. Therefore, RE-based part flow matrix must be mapped into the machine flow matrix, which requires defining the machine flow matrix as a decision variable. This makes the proposed CB-ML problem much more complicated than the conventional machine layout problem. We firstly developed an integer non-linear programming model for the proposed CB-ML problem. Because of its NP-completeness and nonlinear structure, a matheuristic-based solution approach is also developed. The extensive computational analysis have shown that the proposed approach is able to provide good quality solutions for the larger problem instances within reasonable computation times.
A Comparative Study of Modeling and Solution Approaches for the Multi-Mode Resource-Constrained Discrete Time-Cost Trade-Off Problem: Case Study of an Erp Implementation Project
(Pergamon-elsevier Science Ltd, 2022) Cakir, Gizem; Subulan, Kemal; Yildiz, Seyda Topaloglu; Hamzadayi, Alper; Asilkefeli, Ceren
Most knowledge-intensive industries, especially companies developing software engineering projects such as Enterprise Resource Planning (ERP) implementation projects, generally necessitate finding the optimal trade-off between the project duration and total usage cost of the renewable resource costs (e.g., human resource expertise costs). Therefore, the MRC-DTCTP, which integrates classical multi-mode resource-constrained project scheduling (MRCPSP) and discrete time-cost trade-off problems (DTCTP), can be seen as a more applicable problem since it better reflects the objectives and requirements of today's real-life software project applications. The MRC-DTCTP is a much more complex variant of the MRCPSP since it aims to minimize total direct/indirect costs of the resources simultaneously under a pre-specified project deadline. Based on this motivation, a new explicit integer-linear programming (ILP) model of the MRC-DTCTP was first developed based on the implicit non-linear programming model of Wuliang and Chengen (2009). Due to its NP-hard nature, we also proposed a constraint programming (CP) model that includes several search strategies to solve large-sized problem instances within reasonable computation time. In addition, a genetic algorithm (GA) approach in combination with a Modified Serial Schedule Generation scheme (SSGS) is implemented to make further comparisons on several benchmark instances, which are generated based on the existing MRCPSP data sets taken from the project scheduling problem library (PSPLIB) by considering additional problem characteristics. A comprehensive experimental study has shown that the proposed CP model and GA approach can provide superior results in shorter run times for large-sized benchmark instances. Finally, an international Enterprise Resource Planning (ERP) Software Company's real-life application is presented. The ERP projects generally necessitate finding the optimal trade-off between project makespan and human resource costs, making the MRC-DTCTP much more difficult than classical MRCPSPs & DTCTPs. For further analysis, time-cost trade-off curves under different human resource avail abilities and project deadlines are drawn to provide managerial insights to ERP project managers.
Distributed Assembly Permutation Flow Shop Problem; Single Seekers Society Algorithm
(Elsevier Sci Ltd, 2021) Hamzadayi, Alper; Arvas, Mehmet Ali; Elmi, Atabak
The distributed manufacturing and assembly systems have an important role at the point of overcoming the difficulties faced by today's mass-production industry. By using both of these systems together in the same production system, the advantages of this integration can make industries more flexible and stronger. Besides, optimizing these systems is more complicated since the multiple production systems can undoubtfully affect the production system's performance. In this paper, two new mixed-integer linear programming (MILP) models are proposed for the distributed assembly permutation flow shop problem (DAPFSP), inspiring by the multipletravelling salesman structure. Moreover, a single seekers society (SSS) algorithm is proposed for solving the DAPFSP to minimize the maximum completion time of all products. The performance of the proposed MILP models is evaluated using 900 small-sized benchmark instances. The proposed MILP models were effective and were able to find more optimal solutions or improve the best-found solutions for the small-sized DAPFSP benchmark instances. Similarly, the SSS algorithm is statistically compared with the best-known algorithms developed for solving the DAPFSP on 900 small and 810 large-sized benchmark instances. The proposed SSS algorithm shows superior performance compared to other algorithms in solving the small-sized DAPFSP instances in terms of finding better solutions. In addition, it is as effective as the best performing algorithms developed to solve the large-sized DAPFSP instances. Furthermore, the best-found solutions for 40 numbers of test problems reported to be improved in this paper.
An Effective Benders Decomposition Algorithm for Solving the Distributed Permutation Flowshop Scheduling Problem
(Pergamon-elsevier Science Ltd, 2020) Hamzadayi, Alper
In today's centralized globalized economy, large manufacturing firms operate more than one production center. Therefore, the multifactory production scheduling environment, so-called the distributed scheduling problem, is gaining more and more attention from the authors. In this context, which factory will manufacture which product is an important decision making process. The distributed permutation flowshop scheduling problem (DPFSP) provided with real life applications has attracted attention of the researchers for nearly one decade as one of the special cases of the distributed scheduling problem. In the current literature, approximation methods have been intensely used for solving the DPFSP and only one paper containing the exact solution methods has been published to solve this problem. In this paper, the best mathematical formulations available in the current literature has been further improved and traditional and hybrid Benders decomposition algorithms are presented through the proposed new mathematical model. The developed new model is a position based model intended for restricting the domains of decision variables and assigning jobs to sequential positions in the related decision variables. The proposed hybrid Benders decomposition algorithm consists of the hybridization of NEH2_en local search algorithm, a mathematical model to find the upper bound for the number of positions used in the related decision variables, the LS3 algorithm, with the Benders decomposition algorithms. The new and best exact methods available in the literature are compared with each other by using the benchmark data sets and the experimental results showed that the new exact methods developed in this paper are superior to the existing exact methods in all aspects. In this paper, 18 new best solutions are founded for the DPFSP. (C) 2020 Elsevier Ltd. All rights reserved.
Energy-Aware Production Lot-Sizing and Parallel Machine Scheduling With the Product-Specific Machining Tools and Power Requirements
(Pergamon-elsevier Science Ltd, 2024) Sel, Cagri; Gurkan, M. Edib; Hamzadayi, Alper
This study addresses a multi-product lot-sizing and scheduling problem with sequence-dependent setup times, considering that the machining operations cause energy consumption. The production facility comprises identical parallel machines under which the production of each product requires a certain set of tools. The energy requirement of production depends on the product-specific machining tools. The problem deals with determining the minimum cost lot-sizing and scheduling plan considering the energy capacity of the production facility. We formulate the problem as a mixed integer linear programming model by introducing energy consumption-related costs and constraints. We perform a case study on CNC milling and turning workshops. Further, we propose an heuristic approach combining a decomposition-based Simulated Annealing heuristic and Fix&Optimise algorithms to handle larger-sized problem instances. The computational performance of the proposed heuristic approach is evaluated against the proposed mixed integer linear programming model on a numerical study. Our numerical experiments reveal that the proposed heuristic approach is capable of providing cost-efficient solutions without compromising time efficiency.
Event Driven Strategy Based Complete Rescheduling Approaches for Dynamic M Identical Parallel Machines Scheduling Problem With a Common Server
(Pergamon-elsevier Science Ltd, 2016) Hamzadayi, Alper; Yildiz, Gokalp
This paper addresses the dynamic m identical parallel machine scheduling problem in which the sequence dependent setup operations between the jobs are performed by a single server. An event driven rescheduling strategy based simulation optimization model is proposed by inspiration from limited order release procedure (Bergamaschi, Cigolini, Perona, & Portioli, 1997) for being able to tackle the changing environment of the system. The proposed event driven rescheduling strategy is based on the logic of controlling the level of the physical work-in-process on the shop floor. A simulated annealing and dispatching rules based complete rescheduling approaches as the simulation based optimization tools are proposed and adapted to the developed simulation model for generating new schedules depending on the proposed event driven rescheduling strategy. The objective of this study is to minimize the length of schedule (makespan). The performances of the approaches are compared on a hypothetical simulation case. The results of the extensive simulation study indicate that simulated annealing based complete rescheduling approach produces better scheduling performance. (C) 2015 Elsevier Ltd. All rights reserved.
Greedy Randomized Adaptive Search and Benders Decomposition Algorithms To Solve the Distributed No-Idle Permutation Flowshop Scheduling Problem
(Elsevier, 2025) Hamzadayi, Alper; Van, Muenevver Gunay
In today's competitive manufacturing landscape, large enterprises manage multiple production sites, leading to complex scheduling challenges. This study investigates the Distributed No-Idle Permutation Flowshop Scheduling Problem (DNIPFSP), where the objective is to minimize makespan across multiple identical factories while ensuring continuous machine utilization without idle time. To address this problem, we propose both approximation and exact methods. For the approximation method, we introduce a novel Greedy Randomized Adaptive Search Procedure (GRASP). On the exact optimization side, we develop three mathematical formulations: a sequence-based model, an improved position-based model, and a restricted version of the improved position-based model, where the upper bounds of decision variables are determined through a two-stage process. First, an initial GRASP solution is obtained, and based on this solution, an additional model is solved to compute the upper bounds of decision variables. The Benders decomposition algorithm is then applied to efficiently solve problem instances. To further improve computational efficiency, we introduce a hybrid Benders decomposition algorithm, incorporating heuristic-derived cuts alongside standard Benders cuts. Additionally, symmetry-breaking constraints are integrated to strengthen the formulations. Extensive benchmark experiments demonstrate the superiority of the proposed methods over existing approaches. The hybrid Benders decomposition algorithm with symmetry-breaking constraints significantly outperforms the best-known models in the literature, optimally solving 419 out of 420 small-sized instances with an average optimality gap of 0.011%. Additionally, the GRASP achieves the lowest average relative percentage deviation (RPD) for large-sized instances, demonstrating its effectiveness in large-scale scheduling optimization.
Greedy Randomized Adaptive Search for Dynamic Flexible Job-Shop Scheduling
(Elsevier Sci Ltd, 2020) Baykasoglu, Adil; Madenoglu, Fatma S.; Hamzadayi, Alper
Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.
Hybrid Strategy Based Complete Rescheduling Approaches for Dynamic M Identical Parallel Machines Scheduling Problem With a Common Server
(Elsevier, 2016) Hamzadayi, Alper; Yildiz, Gokalp
In this paper, a simulated annealing and seven dispatching rule based complete rescheduling approaches are proposed for the dynamic m identical parallel machines scheduling problem with a common server to generate new schedules depending on the hybrid rescheduling policy. A priority based performance measure is proposed to minimize the number of tardy jobs as primary goal and the square root of the mean-squared deviation for due dates as secondary goal. The proposed complete rescheduling approaches are executed in a hypothetical simulation case to minimize the proposed performance measures under different scheduling frequencies and due date tightness factors. The rankings of the proposed approaches are compared by using simple additive weighting method under different weighting scenarios. The utility results indicate that simulated annealing based complete rescheduling method produces better scheduling performance when compared to dispatching rule based complete rescheduling methods in general. (C) 2016 Elsevier B.V. All rights reserved.
Modeling a Closed-Loop Inventory Routing Problem for Returnable Transport Items Under Horizontal Logistics Collaborations and Dynamic Demand Structure
(Taylor & Francis Ltd, 2025) Yavrucu, Erencan; Soysal, Mehmet; Sel, Cagri; Cimen, Mustafa; Hamzadayi, Alper
This paper addresses a closed-loop inventory routing problem with multiple suppliers, products, and periods under horizontal collaboration assumptions. Our problem encompasses various decision aspects, including routing, inventory management, product delivery, returnable transport item collection and cleaning. We analyze various logistics collaboration scenarios. The effects of demand dynamicity are also assessed. The problem has been mathematically defined as a Mixed Integer Linear Programming model. A rolling horizon approach and a hybrid heuristic algorithm are proposed for instances that exceed the computational requirements of solving the exact MILP model. The applicability and potential benefits of the MILP model and the proposed solution methodologies are demonstrated through a base case and additional numerical analyses on larger-sized instances and networks. The results show that supplier collaboration significantly reduces routing costs, while customer collaboration reduces inventory costs. Numerical comparisons reveal that the proposed algorithms outperform the MILP model for large-scale problem instances.
Modeling and Solving Static M Identical Parallel Machines Scheduling Problem With a Common Server and Sequence Dependent Setup Times
(Pergamon-elsevier Science Ltd, 2017) Hamzadayi, Alper; Yildiz, Gokalp
This paper addresses the static m identical parallel machines scheduling problem with a common server and sequence dependent setup times. Initially, a mixed integer linear programming (MILP) model is presented for the problem to minimize the makespan. Due to the complexity of the problem, simulated annealing (SA) and genetic algorithm (GA) based solution approaches are developed. Subsequently, the performance of the proposed MILP model, SA and GA based solution approaches are compared with the performance of basic dispatching roles such as, shortest processing time first (SPT) and longest processing time first (LPT) over a set of randomly generated problem instances. The results of the computational experiments indicate that the proposed GA is generally more effective and efficient in solving this problem when it is compared to the proposed HIP model, SA. SPT and LPT. (C) 2017 Elsevier Ltd. All rights reserved.
A Simulated Annealing Approach Based Simulation -Optimisation To the Dynamic Job-Shop Scheduling Problem
(Pamukkale Univ, 2018) Sel, Cagri; Hamzadayi, Alper
In this study, we address a production scheduling problem. The scheduling problem is encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce a simulation model (SM) to identify several situations such as machine failures, changing due dates in which scheduling rules (SRs) should be selected independently. Three SRs, i.e. the earliest due date rule (EDD), the shortest processing time first rule (SPT) and the first in first out rule (FIFO), are incorporated in a SM. A simulated annealing heuristic (SA) based simulation-optimisation approach is proposed to identify the unknown schedules in the dynamical system. In the numerical analysis, the performance of SRs and SA are compared using the simulation experiments. The objective functions minimising the mean flowtime and the mean tardiness are examined with varying levels of shop utilization and due date tightness. As an overall result, we observe that the proposed SA heuristic outperforms EDD and FIFO, the well-known SPT rule provides the best results. However, SA heuristic achieves very close results to the SPT and offers a reasonable computational burden in time-critical applications.
Single Seekers Society (Sss): Bringing Together Heuristic Optimization Algorithms for Solving Complex Problems
(Elsevier, 2019) Baykasoglu, Adil; Hamzadayi, Alper; Akpinar, Sener
This paper introduces a new metaheuristic, Single Seekers Society (SSS) algorithm, for solving unconstrained and constrained continuous optimization problems. The proposed algorithm aims to simulate the behaviour of a group of people living together, both individually and holistically. The SSS algorithm brings together several single-solution based search algorithms, single seekers, while realizing an information sharing mechanism based on the superposition principle and the reproduction procedure. Each single seeker tries to improve one single solution by using randomly generated parameter set until the stopping condition is reached. Then, the SSS algorithm exchanges partial information between the best solutions identified by the single seekers via the reproduction process. This characteristic generates new solutions to set as the starting point of the single seekers for their next run and provides a satisfactory level of diversification for the SSS algorithm. Additionally, the SSS algorithm determines a target point via the superposition principle at each iteration to make the single seekers to direct their discovery towards this target point. Thus, the SSS algorithm has the feature providing to share the information produced by the single seekers through the reproduction and the superposition principle. The performance of the proposed SSS algorithm is tested on the well-known unconstrained and constrained continuous optimization problems through a set of computational studies. This paper compares SSS algorithm against 27 and 17 different search algorithms on unconstrained and constrained problems, respectively. The experimental results indicate the stability and the effectiveness of the SSS algorithm in terms of quality of produced results, achieved level of convergence and the capability of coping with trapping in local optima and stagnation problems. (C) 2018 Elsevier B.V. All rights reserved.