Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

This is the author’s version of a work that was accepted for publication in Robotics and Computer-Integrated Manufacturing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Computer-Integrated Manufacturing, [Volume 29, Issue 5, October 2013, Pages 418–429] DOI10.1016/j.rcim.2013.04.001[EN] The traditional production scheduling problem considers performance indicators such as processing time, cost and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption and environmental impacts into account completely. Therefore, this paper proposes an energy-efficient model for flexible flow-shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as the NPhard problem, an improved genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption for implementing a feasible scheduling. Finally, a case study of production scheduling problem for metalworking workshop in a plant is simulated. The experimental results show the relationship between the makespan and the energy consumption is conflicting apparently. Moreover, an energy saving decision is performed in a feasible scheduling. Using the decision method, there can be a significant potential to minimize energy consumption while complying with the conflicting relationshipThis research was carried out as a part of the CASES project which is supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Program under the Grant agreement no 294931. This research was also supported by National Science Foundation of China (No. 51175262), Jiangsu Province Science Foundation for Excellent Youths (No. BK201210111), Jiangsu Province Industry-Academy-Research Grant (No. BY201220116), the NUAA Fundamental Research Fund (No. NS2013053), the Project Funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the research project TIN2010-20976-C02-01 (Ministry of Science and Innovation, Spain).Dai, M.; Tang, D.; Giret Boggino, AS.; Salido Gregorio, MA.; Li, W. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing. 29(5):418-429. https://doi.org/10.1016/j.rcim.2013.04.001S41842929

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

INTEGRATED APPROACH OF SCHEDULING A FLEXIBLE JOB SHOP USING ENHANCED FIREFLY AND HYBRID FLOWER POLLINATION ALGORITHMS

Manufacturing industries are undergoing tremendous transformation due to Industry 4.0. Flexibility, consumer demands, product customization, high product quality, and reduced delivery times are mandatory for the survival of a manufacturing plant, for which scheduling plays a major role. A job shop problem modified with flexibility is called flexible job shop scheduling. It is an integral part of smart manufacturing. This study aims to optimize scheduling using an integrated approach, where assigning machines and their routing are concurrently performed. Two hybrid methods have been proposed: 1) The Hybrid Adaptive Firefly Algorithm (HAdFA) and 2) Hybrid Flower Pollination Algorithm (HFPA). To address the premature convergence problem inherent in the classic firefly algorithm, the proposed HAdFA employs two novel adaptive strategies: employing an adaptive randomization parameter (α), which dynamically modifies at each step, and Gray relational analysis updates firefly at each step, thereby maintaining a balance between diversification and intensification. HFPA is inspired by the pollination strategy of flowers. Additionally, both HAdFA and HFPA are incorporated with a local search technique of enhanced simulated annealing to accelerate the algorithm and prevent local optima entrapment. Tests on standard benchmark cases have been performed to demonstrate the proposed algorithm’s efficacy. The proposed HAdFA surpasses the performance of the HFPA and other metaheuristics found in the literature. A case study was conducted to further authenticate the efficiency of our algorithm. Our algorithm significantly improves convergence speed and enables the exploration of a large number of rich optimal solutions.

Extraction of operation characteristics in mechanical systems using genetic morphological filter

Operation characteristics such as rotating speed are of great importance in condition monitoring and fault diagnosis of rotating machineries. Since different components in the mechanical system are often correlated and interacted, the acquired signals are highly coupled and contaminated by lots of high-frequency noises. As a result, the frequency and phase of the observed signal cannot reflect actual condition of the mechanical component. In this paper, we propose a genetic morphological filter to purify the operation characteristics of the mechanical system in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to remove stochastic noises from the original signal. Then, according to the geometric characteristic of the noises, the structure elements are constructed with two parabolas and four parameters of the structure elements are synchronously optimized with genetic algorithm. The combination of Hurst exponent and Kurtosis is selected as the fitness function of the genetic algorithm and the optimal parameters of the structure elements correspond to the maximum of fitness function. The proposed method is evaluated by simulated signals with different frequencies, vibration signals measured on condensate pump and sound signals acquired from motor engine, respectively. Results show that with genetic morphological filter, the operation characteristics such as rotating speed and phase can be extracted in the time domain efficiently

Extraction of operation characteristics in mechanical systems using genetic morphological filter

Operation characteristics such as rotating speed are of great importance in condition monitoring and fault diagnosis of rotating machineries. Since different components in the mechanical system are often correlated and interacted, the acquired signals are highly coupled and contaminated by lots of high-frequency noises. As a result, the frequency and phase of the observed signal cannot reflect actual condition of the mechanical component. In this paper, we propose a genetic morphological filter to purify the operation characteristics of the mechanical system in the time domain. Firstly, an average weighted combination of open-closing and close-opening morphological operator, which eliminates statistical deflection of amplitude, is utilized to remove stochastic noises from the original signal. Then, according to the geometric characteristic of the noises, the structure elements are constructed with two parabolas and four parameters of the structure elements are synchronously optimized with genetic algorithm. The combination of Hurst exponent and Kurtosis is selected as the fitness function of the genetic algorithm and the optimal parameters of the structure elements correspond to the maximum of fitness function. The proposed method is evaluated by simulated signals with different frequencies, vibration signals measured on condensate pump and sound signals acquired from motor engine, respectively. Results show that with genetic morphological filter, the operation characteristics such as rotating speed and phase can be extracted in the time domain efficiently

Cost Optimization of Multi-Level Multi-Product Distribution Using An Adaptive Genetic Algorithm

Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces the lowest and surprising result compared to the existing algorith

Optimal Expenditure and Benefit Cost Based Location, Size and Type of DGs in Microgrids Systems Using Adaptive Real Coded Genetic Algorithm

The economic issue is an essential element to determine whether DG should be installed or not. This work presents the economical approach for multi-type DGs placement in microgrid systems with more comprehensive overview from DG’s owner perspective. Adaptive Real Coded GA (ARC-GA) with replacement process is developed to determine the location, type, and rating of DGs so as the maximum profit is achieved. The objectives of this paper are maximizing benefit cost and minimizing expenditure cost. All objectives are optimized while maintaining the bus voltage at the acceptable range and the DGs penetration levels are below of the DGs capacities.The proposed method is applied on the 33 bus microgrids systems using conventional and renewable DG technology, namely Photovoltaic (PV), Wind Turbine (WT), Micro Turbine (MT) and Gas Turbine (GT). The simulation results show the effectiveness of the proposed approach

MITIGASI PREMATURE CONVERGENCE PADA GENETIC ALGORITHM MENGGUNAKAN METODA DYNAMICS GROWTH POPULATION DALAM KASUS UNIVERSITY COURSE SCHEDULING

Permasalahan penjadwalan kegiatan perkuliahan atau yang biasa disebut sebagai University Course Scheduling (UCS), hingga saat ini masih menjadi dilema antara kepentingan dosen, mahasiswa dan fasilitas yang tersedia. salah satu solusi terhadap permasalahan permasalahan tersebut ini adalah dengan menggunakan Genetic Algorithm (GA) untuk menguraikan permutasi acara perkuliahan dengan pertimbangan constraint yang diinginkan. Penelitian ini mengusulkan penggunaan Dynamics Population pada pertumbuhan jumlah populasi setiap generasinya untuk mencegah terjadi premature convergence akibat terbatasnya search space. Data penelitian diperoleh berdasarkan proses penjadwalan pada jurusan Teknik Elektro UIN SUSKA –Riau semester Gasal 2019-2020 dan hasil interview dari sejumlah civitas akademika. Beberapa skenario yang diamati dalam penelitian ini adalah berdasarkan variasi inisialisasi populasi 50-100 individu, dengan probabilitas 0,1 hingga 0,5 dan probabilitas mutasi 0,01 hingga 0,05. Hasil penelitian menunjukkan bahwa inisialisasi populasi 60 dengan probabilitas crossover 0,2 hingga 0,4 dapat mengatasi permasalahan premature convergence untuk mendapatkan solusi terhadap UCS. Selain itu penambahan probabilitas mutasi lebih dari 0,01 akan mengakibatkan beban komputasi yang semakin tinggi. Kata Kunci : Dynamics Population, Elithsm, Genetic Algorithm, PMX, Reciprocal Exchange Mutatio