Crew Scheduling Optimization with Artificial Bee Colony Algorithm

International Journal of Applied Information Systems, 2016

Timetable problem is a NP-hard problem where different constraints and various resources are applied but the resources are limited. Optimization problem is a technique which can handle different constraints. This paper focuses the Bee colony Optimization (BCO) for finding the optimal solutions of course time table.BCO is a Meta heuristic optimization scheme where NP-hard with different parameter settings are solved. There are two objectives, first objective is to provide the introduction to timetabling and second objective is the BCO and their variations with timetable design. The proposed algorithm is used to construct the course time table and optimized that time table.

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.

Production & Manufacturing Research, 2016

Fazlollahtabar (2016) Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem,

Procedia Technology, 2016

Scheduling is the proper allocation of resources over a period for performing a set of tasks with the objective of optimizing one or more performance measures. The actual assignment of starting and completion times of operations on jobs, if the manufacturing order is to be completed on time is known as Production scheduling. The Job Shop Scheduling Problem (JSSP) is one of the most difficult scheduling problems. Since JSSP is NP-complete, that is, the selection of the best scheduling solution is not polynomially bounded, heuristic approaches are often considered. This is an important practical problem in the field of production management and combinatorial optimization. Inspired by the decision-making capability of bee swarms in the nature, this paper proposes an efficient scheduling method based on Artificial Bee Colony (ABC) for solving the JSSP. Most of the researchers in production scheduling are concerned with the optimization of a single criterion. However, the performance of a schedule often involves more than one aspect and, therefore requires a multi-objective treatment. Minimization of makespan and total tardiness are the two performance measures considered in this paper. The Artificial Bee Colony algorithm was coded in MATLAB 2009. A parameter analysis was done to fix the control parameters of Artificial Bee Colony algorithm. The performance of the algorithm was analyzed on the benchmark problems provided by E. Taillard.

Information Sciences, 2001

Crew scheduling is an NP-hard constrained combinatorial optimization problem, very important for the airline industry [G. Yu (Ed.), Operations Research in Airline Industry, Kluwer Academic Publishers, Dordrecht, 1998]. We solve this problem using a genetic algorithm applied to a¯ight graph representation that represents several problem-speci®c constraints, unlike previous attempts [D. Levine, Application of a hybrid genetic algorithm to airline crew scheduling, Ph.D. dissertation, Computer Science Department, IIT , Chicago, USA, 1995; J.E. Beasley, P.C. Chu, A genetic algorithm for the set covering problem, Eur. J. Oper. Res. 94 (1996) 392±404; P.C. Chu, J.E. Beasley, A genetic algorithm for the set partitioning problem, Technical report, Imperial College, UK, 1995]. In extensive experimental comparisons on¯ight data of several airlines, the new approach performed better than other approaches in 17 out of 24 data sets. Ó (C.K. Mohan). 0020-0255/01/$ -see front matter Ó 2001 Elsevier Science Inc. All rights reserved. PII: S 0 0 2 0 -0 2 5 5 ( 0 1 ) 0 0 0 8 3 -4

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...

Sinkron, 2023

A common schedule problem found in colleges is the positioning of courses in a certain space and time. This placement process often encounters barriers that must be met so that there is no imbalance in the school schedule. One of the problems that often arise is the placement of class capacity that does not match the course requirements. In this study, the researchers used the Artificial Bee Colony Hybrid Algorithm (HABC) to construct course schedules efficiently at the college. The objective of the research was to develop a course scheduling system using the HABC algorithm by combining the Engineering of Artificial Bee Colony (ABC) and genetic algoritms, especially on the crossover process to better address the schedule problems. The research procedure used is to design and implement a course scheduling system using the Hybrid ABC algorithm. The results of the research demonstrate that the Hybrid ABC algorithm is effective in generating optimal course schedule schedules, in line with time limits, room needs, and lecturer requirements and can automate course schedule processes, saving time and resources, while ensuring optimal schedules.

Routing and Scheduling Solutions

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.

Proceedings of the 2006 Winter Simulation Conference, 2006

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

Combinatorial optimization problems are problems that have a large number of discrete solutions and a cost function for evaluating those solutions in comparison to one another. With the vital need of solving the combinatorial problem, several research efforts have been concentrated on the biological entities behaviors to utilize such behaviors in population-based metaheuristic. This paper presents bee colony algorithms which is one of the sophisticated biological nature life. A brief detail of the nature of bee life has been presented with further classification of its behaviors. Furthermore, an illustration of the algorithms that have been derived from bee colony which are bee colony optimization, and artificial bee colony. Finally, a comparative analysis has been conducted between these algorithms according to the results of the traveling salesman problem solution. Where the bee colony optimization (BCO) rendered the best performance in terms of computing time and results.