Application and Evaluation of Bee-Based Algorithms in Scheduling (original) (raw)
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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.
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The biological inspired optimization techniques have proven to be powerful tools for solving scheduling problems. Marriage in Honeybee Optimization is a recent biological technique that attempts to emulate the social behavior in a bee colony and although has been applied to only a limited number of problems, it has delivered promising results. By means of this technique in this chapter the authors explore the solution space of scheduling problems by identifying an appropriate representation for each studied case. Two cases were considered: the minimization of earliness-tardiness penalties in a single machine scheduling and the permutation flow shop problem. The performance was evaluated for the first case with 280 instances from the literature. The technique performed quite well for a wide range of instances and achieved an average improvement of 1.4% for all instances. They obtained better solutions than the available upper bound for 141 instances. In the second case, they achieved an average error of 3.5% for the set of 120 test instances.
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In this paper, we propose a novel efficient model based on Bees Algorithm (BA) for the Resource-Constrained Project Scheduling Problem (RCPSP). The studied RCPSP is a NP-hard combinatorial optimization problem which involves resource, precedence, and temporal constraints. It has been applied to many applications. The main objective is to minimize the expected makespan of the project. The proposed model, named Enhanced Discrete Bees Algorithm (EDBA), iteratively solves the RCPSP by utilizing intelligent foraging behaviors of honey bees. The potential solution is represented by the multidimensional bee, where the activity list representation (AL) is considered. This projection involves using the Serial Schedule Generation Scheme (SSGS) as decoding procedure to construct the active schedules. In addition, the conventional local search of the basic BA is replaced by a neighboring technique, based on the swap operator, which takes into account the specificity of the solution space of proj...
Procedia Engineering, 2014
Job shop scheduling is predominantly an Non deterministic polynomial (NP)-complete challenge which is successfully tackled by the ABC algorithm by elucidating its convergence. The Job Shop Scheduling Problem (JSSP) is one of the most popular scheduling models existing in practice which is among the hardest combinatorial optimization problems. The ABC (Artificial Bee Colony) technique is concerned, it is observed that the entire specific artificial bees move about in a search space and select food sources by suitably adapting their location, know-how and having a full awareness of their nest inhabitants. Moreover, several scout bees soar and select the food sources discretely without making use of any skills. In the event of the quantity of the nectar in the fresh source becoming larger than the nectar quantity of an available source, they remember the fresh location and conveniently disregard the earlier position. In this way, the ABC system integrates local search techniques, executed by employed and onlooker bees, with universal search approaches, administered by onlookers and scouts. In our ambitious approach we have employed these three bees to furnish optimization in makespan, machine work load and the whole run period in an optimized method. In this way, with the efficient employment of our effective technique we make an earnest effort to minimize the makespan and number of machines. This paper compares GA to minimize the make span of the job scheduling process with ABC and proved that ABC algorithm produces the better result.
Lecture Notes in Management and Industrial Engineering, 2019
Job shop scheduling for labor-intensive and project type manufacturing is a too hard task because the operation times are not known before production and change according to the orders' technical specifications. In this paper, a case study is presented for scheduling a labor-intensive and project type workshop. The aim is to minimize the makespan of the orders. For this purpose, the artificial bee colony algorithm (ABC) is used to determine the entry sequence of the waiting orders to the workshop and dispatching to the stations. 18 different orders and 6 welding stations are used for the scheduling in this case. The input data of the algorithm are the technical specifications (such as weight and width of the demanded orders) and processing times of the orders which vary according to the design criteria demanded by the customers. According to the experimental results, it is observed that the ABC algorithm has reduced the makespan.
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.
Bees Algorithm for multi-mode, resource-constrained project scheduling in molding industry
Computers & Industrial Engineering, 2017
In a resource-constrained environment project planning and scheduling becomes an extremely complex problem. For real life project schedules multi-mode resource requirements remarkably increase the complexity of and enlarge the respective solution spaces. Therefore schedulers require systematic methodologies compatible with the real world implementations in order to generate cost effective schedules. Similarly, plastic injection molding is known to be a "make-to order" process. The manufacturing of the mold which is a unique and essential component of plastic injection is considered kind of a project. The aim of this study is set to investigate the possibility of utilizing Bees Algorithm for single-resource, multi-mode, resource-constrained mold project scheduling in order to generate a systematic approach to solve the problems of this nature. A Bee-Based Mold Scheduling Model is therefore proposed and employed on a set of problems with different dimensions for the proof of concept. Detail description of an injection molding project together with respective performance analysis is also provided. After the implementation of the proposed methodology, it is well proven that, even for high number of activities and limited resources, the proposed method generates suitable schedules for the projects of this kind the implementation and respective modelling is explained and the results are discussed in detail within the text.
Scheduling independent tasks: Bee Colony Optimization approach
2009 17th Mediterranean Conference on Control and Automation, 2009
The problem of static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. The problem is solved by the Bee Colony Optimization (BCO). The BCO algorithm belongs to the class of stochastic swarm optimization methods. The proposed algorithm is inspired by the foraging habits of bees in the nature. The BCO algorithm was able to obtain the optimal value of objective function in all small to medium size test problems. The CPU times required to find the best solutions by the BCO are acceptable.