Marriage in Honeybee Optimization to Scheduling Problems (original) (raw)
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Towards Scheduling Optimization through Artificial Bee Colony Approach
In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.
Application and Evaluation of Bee-Based Algorithms in Scheduling
Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, 2018
Scheduling is a vital element of manufacturing processes and requires optimal solutions under undetermined conditions. Highly dynamic and, complex scheduling problems can be classified as np-hard problems. Finding the optimal solution for multi-variable scheduling problems with polynomial computation times is extremely hard. Scheduling problems of this nature can be solved up to some degree using traditional methodologies. However, intelligent optimization tools, like BBAs, are inspired by the food foraging behavior of honey bees and capable of locating good solutions efficiently. The experiments on some benchmark problems show that BBA outperforms other methods which are used to solve scheduling problems in terms of the speed of optimization and accuracy of the results. This chapter first highlights the use of BBA and its variants for scheduling and provides a classification of scheduling problems with BBA applications. Following this, a step by step example is provided for multi-m...
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2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, 2010
this domain of scheduling with meta-heuristic. Being open-minded and helpful, they have provided me tonnes of suggestions, ideas and criticism. They also have given me enough freedom in my study. Their effectiveness in providing a comfortable and conducive research environment and the facilities in the Parallel Distributed Computing Center (PDCC) have eased my pursuit of knowledge. Besides, I am grateful for their encouragement to me in submitting papers about this research to various conferences and journals. Special thanks goes to the Universiti Sains Malaysia and the Ministry of Higher Education of Malaysia for the awarded scholarship, which provided me with financial security during the period of my PhD study. My thanks also goes to Professor Dr. Rosni
Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems
ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni, 2007
This paper describes a population-based approach that uses a honey bees foraging model to solve job shop scheduling problems. The algorithm applies an efficient neighborhood structure to search for feasible solutions and iteratively improve on prior solutions. The initial solutions are generated using a set of priority dispatching rules. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony, tabu search and shifting bottleneck procedure on a set of job shop problems are presented. The results indicate the performance of the proposed approach is comparable to other efficient scheduling approaches.
JAIS (Journal of Applied Intelligent System), 2022
Artificial Bee Colony (ABC) which is a development of the intelligent swarm model and is a branch of artificial intelligence based on self-organization systems. Artificial Bee Colony (ABC) is an intelligent algorithm that is inspired by the food search process carried out by bees. This is like what is done when there are many jobs that need to find the optimal value, where each job to be processed has a specific route of operations to be performed on a set of machines, and a different flow shop and variant: all jobs follow the same machine sequences. We will focus on the latter. In this study, ABC is implemented to optimize work scheduling, in this case 7 different variations are used with mxn values between 10x3 to 40x15 on 10 to 40 jobs. To evaluate the results, the Relative Percentage Increase (RPI) has been used in the test with an achievement range between 1.9 to 18.9.
Symmetry
Considering green scheduling and sustainable manufacturing, the energy-efficient hybrid flow shop scheduling problem (EHFSP) with a variable speed constraint is investigated, and a novel multi-population artificial bee colony algorithm (MPABC) is developed to minimize makespan, total tardiness and total energy consumption (TEC), simultaneously. It is necessary for manufacturers to fully understand the notion of symmetry in balancing economic and environmental indicators. To improve the search efficiency, the population was randomly categorized into a number of subpopulations, then several groups were constructed based on the quality of subpopulations. A different search strategy was executed in each group to maintain the population diversity. The historical optimization data were also used to enhance the quality of solutions. Finally, extensive experiments were conducted. The results demonstrate that MPABC can achieve an outstanding performance on three metrics DIR, c and nd for the...
Crew Scheduling Optimization with Artificial Bee Colony Algorithm
Crew Scheduling Optimization with Artificial Bee Colony Algorithm, 2017
Crew scheduling is one of the most important optimization problems for airline companies. It is the scheduling of weekly or monthly work schedule under certain constraints, such as working hours and weekly permits. There are many studies using analytical and heuristic approaches in the literature in order to solve this problem. In studies using heuristic approaches, genetic algorithms are used frequently. In this study, an artificial bee colony algorithm, which is a heuristic method, is used instead of the approaches applied to the current problem. Weekly work schedules are optimized according to daily working hours and days off for crew scheduling under a number of different personnel. From the simulation results, it is clearly seen that the artificial bee colony algorithm produces successful results within reasonable time.
A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems
Mathematical Problems in Engineering, 2015
The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems.