Using micro genetic algorithm for solving scheduling problems

Job Shop Scheduling Problem (JSSP) and Timetable scheduling are known to be computationally NP–hard problems. There have been many attempts by many researchers to develop reliable scheduling software, however, many of these software have only been tested or applied on an experimental basis or on a small population with minimal constraints. However in actual model JSSP, the constraints involved are more complicated compared to classical JSSP and feasible schedule must be suggested within a short period of time. In this thesis, an enhanced micro GA, namely micro GA with local search is proposed to solve an actual model JSSP. The scheduler is able to generate an output of a set of feasible production plan not only at a faster rate but which can generate a plan which can reduce the makespan as compare to those using manual. Also, in this thesis, the micro GA is applied to the timetabling problem of Faculty of Electrical Engineering Universiti Teknologi Malaysia which has more than 3,000 students. Apart from having more students, the faculty also offers various different type s of specialized courses. Various constraints such as elective subjects, classrooms capacity, multiple sections students, lecturer, etc have to be taken into consideration when designing the solution for this problem. In this thesis , an enhanced micro GA is proposed for timetable scheduling in the Faculty to overcome the problems. The enhanced micro GA algorithm is referred to as distributed micro GA which has local search to speed up the scheduling process. Comparisons are made with simple GA methods such that a more optimal solution can be achieved. The proposed algorithm is successfully implemented at the Faculty meeting a variety of constraints not achievable using manual methods

Optimizing Time Utilization of FMS

The aim of the research is to solve the problem of simultaneous production on the flexible manufacturing system with different combination of product types and quantities that will give maximal utilization of production system. The presumption for good utilization of FMS (Flexible Manufacturing System) is in forming of working order with such product type structure that will make possible of production processing with minimal time load of complete production system. Working order structure from the point of product types and quantities is dictated by market demands that are known earlier. Because the structure of particular working order is not harmonized with the exploitation characteristics of FMS, we are faced with problem how to realize working order in such conditions as well as how to achieve main goal: shorter machining cycle with less time occupation of production system. The method based on two phases for solving problem of control working order realization is presented in the work. In the first phase the selection of optimal combination of process plans which gives minimal time load of production system through simultaneous production of different products and their quantities is given. In the second phase the order of part production and the order of particular operations processing is optimized. The optimization problem in both phases of control is solved by application of genetic algorithm approach. The software for computing and optimizing of processing order on FMS is developed

Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus

Genetic Algorithm Based Charge Optimization of Lithium-Ion Batteries in Small Satellites

Small spacecraft that are powered by solar energy have limitations because of the size of their solar panels. With the limitations on the solar panel size, it is generally hard to comply with the demands from all the satellite subsystems, payloads and batteries at the same time. To overcome these problems we have developed and adopted a power management optimization scheme that runs in real time in the satellite. The proposed power management scheme primarily involves scheduling of loads (various subsystem operations, payload experimentation, battery charging, etc.) so that power utilization and thereby the charge of the batteries is at its optimum. We have developed a genetic algorithm based schedule optimizer and propose an FPGA based fitness evaluation function for it

Continuous Process Improvement Implementation Framework Using Multi-Objective Genetic Algorithms and Discrete Event Simulation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems. Design/methodology/approach This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources. Findings Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework. Originality/value Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other

A particle swarm optimization for the job shop scheduling problems

Popülasyon temelli sezgisel yöntemlerden biri olan Parçacık Sürü Optimizasyonu (PSO), kuş ve balık sürülerinin sosyal davranışlarından etkilenerek geliştirilen yeni bir eniyileme yöntemidir. Bu makalede, zor çizelgeleme problemleri arasında yer alan Atölye Tipi Çizelgeleme problemlerinin çözümü için, bir PSO modeli, Değişken Komşuluk Arama yöntemi ile birlikte geliştirilmiştir. Oluşturulan bu model, tamamlanma zamanı performans ölçütüne göre literatürde yer alan bazı zor test problemleri üzerindeki sonuçları incelenmiş ve iyi sonuçlar veren diğer sezgisel yöntemlerin sonuçlarıyla karşılaştırılmıştır. Sonuçta genel olarak önerilen modelin diğer yöntemlere göre daha iyi veya eşdeğer seviyede olduğu görülmüştür.  Anahtar Kelimeler: Atölye tipi çizelgeleme, parçacık sürü optimizasyonu, sezgiseller.Particle Swarm Optimization (PSO) is one of the population based optimization technique inspired by social behavior of bird flocking and fish schooling. PSO inventers were inspired of such natural process based scenarios to solve the optimization problems. In PSO, each single solution, called a particle, is considered as a bird, the group becomes a swarm (population) and the search space is the area to explore. Each particle has a fitness value calculated by a fitness function, and a velocity of flying towards the optimum, food. All particles fly across the problem space following the particle nearest to the optimum. PSO starts with initial population of solutions, which is updated iteration-by-iteration. Therefore, PSO can be counted as an evolutionary algorithm besides being a metaheuristics method, which allows exploiting the searching experience of a single particle as well as the best of the whole swarm. In this paper, A PSO model for the job shop scheduling problem is proposed. In addition, a simple but efficient local search method called Variable Neighborhood Search (VNS) is embedded to the PSO model and applied to several hardest benchmark suites. The results for the PSO algorithm with VNS are also presented and compared with many efficient meta-heuristic algorithms in literature. As a final result, PSO with VNS results are generally found to be better than other results. Keywords: Job shop scheduling, particle swarm optimization, Meta-Heuristics

An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

[EN] Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJB460018), the Innovation Foundation for Science and Technology of Yangzhou University (No. 2016CXJ020 and No. 2017CXJ018), Science and Technology Project of Yangzhou under (No. YZ2017278), Research Topics of Teaching Reform of Yangzhou University under (No. YZUJX2018-28B), and the Spanish Government (No. TIN2016-80856-R and No. TIN2015-65515-C4-1-R).Dai, M.; Zhang, Z.; Giret Boggino, AS.; Salido, MA. (2019). An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints. Sustainability. 11(11):1-23. https://doi.org/10.3390/su11113085S1231111Wu, X., & Sun, Y. (2018). 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