KESİKLİ HARMONİ ARAMA ALGORİTMASI İLE OPTİMİZASYON PROBLEMLERİNİN ÇÖZÜMÜ: LİTERATÜR ARAŞTIRMASI

It is usually assumed the variables which are used in the optimization problems are continuous variables. However, the variables have discrete or integer values in many real life practices. Considering discrete integer variables in the optimization problems makes the problems more complex. There are few methods to solve these type of problems. The Harmony Search Algorithm inspired by improvisation of musical harmony and a recent variant of it, The Discrete Harmony Search Algorithm were investigated. It is thought that The usage of the Discrete Harmony Search Algorithm is going to provide a good alternative to solve the optimization problems.Genellikle optimizasyon problemlerinde kullanılan değişkenlerin sürekli değişkenler olduğu kabul edilmektedir. Ancak gerçek hayatta çoğu problemin değişkenleri kesikli veya tam sayı değişkenler şeklindedir. Optimizasyon problemlerinde kesikli tam sayı değişkenlerin dikkate alınmasıyla karmaşıklık daha fazla artmaktadır. Bu tür karmaşık problemlerin çözümünde az da olsa çeşitli yöntemler mevcuttur. Bu çalışmada bir müzik eserinde oluşan harmoniden esinlenilerek geliştirilen Harmoni Arama Algoritması ve henüz yeni bir uygulaması olan Kesikli Harmoni Arama Algoritması ile ilgili yapılan araştırmalar incelenmiştir. Kesikli Harmoni Arama Algoritması kullanılarak optimizasyon problemlerinin çözümü bu konuda bir alternatif sağlayacaktır

Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization

This paper proposes two constructive heuristics, i.e. HPF1 and HPF2, for the blocking flow shop problem in order to minimize the total flow time. They differ mainly in the criterion used to select the first job in the sequence since, as it is shown, its contribution to the total flow time is not negligible. Both procedures were combined with the insertion phase of NEH to improve the sequence. However, as the insertion procedure does not always improve the solution, in the resulting heuristics, named NHPF1 and NHPF2, the sequence was evaluated before and after the insertion to keep the best of both solutions. The structure of these heuristics was used in Greedy Randomized Adaptive Search Procedures (GRASP) with variable neighborhood search in the improvement phase to generate greedy randomized solutions. The performance of the constructive heuristics and of the proposed GRASPs was evaluated against other heuristics from the literature. Our computational analysis showed that the presented heuristics are very competitive and able to improve 68 out of 120 best known solutions of Taillard’s instances for the blocking flow shop scheduling problem with the total flow time criterionPeer ReviewedPostprint (author’s final draft

Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance

An efficient discrete artificial bee colony algorithm for the blocking flow shop problem with total flowtime minimization

This paper presents a high performing Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with flow time criterion. To develop the proposed algorithm, we considered four strategies for the food source phase and two strategies for each of the three remaining phases (employed bees, onlookers and scouts). One of the strategies tested in the food source phase and one implemented in the employed bees phase are new. Both have been proved to be very effective for the problem at hand. The initialization scheme named HPF2(¿, µ) in particular, which is used to construct the initial food sources, is shown in the computational evaluation to be one of the main procedures that allow the DABC_RCT to obtain good solutions for this problem. To find the best configuration of the algorithm, we used design of experiments (DOE). This technique has been used extensively in the literature to calibrate the parameters of the algorithms but not to select its configuration. Comparing it with other algorithms proposed for this problem in the literature demonstrates the effectiveness and superiority of the DABC_RCTPeer ReviewedPostprint (author’s final draft

Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms

This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique, which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters, mainly, the destruction size and temperature scale factor, adaptively during the search process. Besides IGQ algorithms, two different mathematical modeling techniques. One of these techniques is the constraint programming (CP) model, which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model, which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models, extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms

하이브리드 플로우 샵 (HFS) 스케줄링 문제의 시뮬레이션 기반 의사 결정 지원 프레임 워크에 관한 연구

학위논문 (석사)-- 서울대학교 대학원 공과대학 산업공학과, 2017. 8. Park, Jin Woo.Manufacturing environments has become very complicated nowadays. They consist of hundreds of job varieties, diverse types of machines with complex architectural layouts. Hybrid flow shop (HFS) is one of them. Although there is no exact definition of HFS but flow shops with multiple parallel machines at each stage are referred as HFS in general. However, the characteristics of a hybrid flow shop might differ according to the particular production environment. HFS production scheduling is one of the most complex combinatorial problems encountered in many real world industries. Given HFSs complexity and importance, most of the literatures on HFS scheduling seem to focus on mono-criteria objectives which is sometimes quite unrealistic. Real world HFS scheduling problem involves several performance measures as objective functions, which eventually can often conflict and compete for decision making. Industries have been using simulation extensively to model and analyze the impact of such variabilities on production system behavior and to explore several ways of coping under any changes or uncertainties. Simulation flexibility may help to find better or optimal solutions to a number of complex problems of HFS. The HFS scheduling problem requires all activities to be considered. Even though simulation is a good tool, there is one more aspect to be considered on using simulation. Almost each and every level of employees needs to be skilled enough with simulation software to deal with HFS scheduling problems. But not all of them are fully capable to utilize the simulation system. Inadequate capability of personnel to utilize simulation effectively can only be overcome if we can design custom interfaces and integrate flexible simulation framework with supportive programs. In this study, a flexible Simulation modeling framework is proposed to mimic HFS systems. This research analyzes the impact of different combinations of commonly used job sequencing and dispatching policies for multiple performance measures. A heuristic is also proposed to reduce the number of comparisons thus to reduce the number of simulation runs. By implementing the proposed heuristics, better combinations of dispatching policies are found each of the performance measure considered. In the end, an analysis is shown regarding the impact of varying batch size on certain HFSs performance measures.Chapter 1. Introduction ................................ .......................... 1 1.1 Background ................................ ................................ ............... 1 1.2 Motivation ................................ ................................ ................. 3 1.3 Outline of the thesis ................................ ................................ .. 4 Chapter 2. Literature Review ................................ ................. 5 2.1 Problems in HFS ................................ ................................ ....... 5 2.2 HFS scheduling problem (HFSP) ................................ .............. 6 2.2.1 HFS classifications .................................................................. 6 2.2.2 Performance criteria .............................................................. 10 2.3 Solution methods for HFSP ................................ .................... 13 2.3.1 Dispatching rules ................................................................... 15 2.3.2 Simulation ............................................................................. 15 Chapter 3. Problem Description ................................ ........... 17 3.1 Notations of parameters and variables ................................ .... 18 3.2 Objectives ................................ ................................ ................ 20 3.3 Constraints ................................ ................................ ............... 21 Chapter 4. Methodology ................................ ....................... 22 4.1 Simulation framework ................................ ............................. 22 4.2 Proposed dispatching policies ................................ ................. 24 4.2.1 Mathematical measures of dispatching policies .................... 25 4.3 Proposed heuristic ................................ ................................ ... 26 4.3.1 IWMF heuristic ..................................................................... 27 Chapter 5. Case Study ................................ .......................... 30 5.1 Introduction ................................ ................................ ............. 30 5.2 Optical lens processing system ................................ ............... 31 5.3 Design of Experiment ................................ ........................... 32 5.3.1 Analyzed job types ................................................................ 32 5.3.2 Layout of the analyzed HFS .................................................. 33 5.3.3 Simulation model setup ......................................................... 36 5.3.4 Assumptions for experiment ................................................. 37 5.3.5 Attributes for simulation experiment .................................... 38 5.4 Model development ................................ ................................ . 42 5.4.1 Job orders creation and routing ............................................. 42 5.4.2 Workstation design ................................................................ 42 5.4.3 Queue modules ...................................................................... 42 5.4.4 Interface control objects ........................................................ 43 5.4.5 Integration of dispatching rules ............................................. 43 Chapter 6. Results and Analysis ................................ ........... 44 6.1 Experiment criteria ................................ ................................ .. 44 6.2 Experiment procedure ................................ ............................. 44 6.2.1 Observations .......................................................................... 53 6.3 Varying batch size effects ................................ ........................ 57 6.3.1 Observations .......................................................................... 58 Chapter 7. Conclusion ................................ .......................... 60 7.1 Contribution ................................ ................................ ............ 60 7.2 Limitations ................................ ................................ .............. 61 7.3 Future work ................................ ................................ ............. 62 Bibliography ................................ ................................ ......... 63 APPENDIX A ................................ ................................ ....... 69 APPENDIX B ................................ ................................ ....... 72 APPENDIX C ................................ ................................ ....... 76Maste

Analysis, design and optimization of offshore power system network

Ph.DDOCTOR OF PHILOSOPH