Meta-Heuristic Algorithms to Solve Bi-Criteria Parallel Machines Scheduling Problem (original) (raw)
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The International Journal of Advanced Manufacturing Technology, 2014
The problem dealt with in this study has job sequence-dependent setup times under suitable machine constraints and due date constraints. The performance criterion for this problem is to minimize the sum of makespan and total tardiness. In the literature, an application of ABC algorithm for the problems which includes all the properties that we have dealt has not been discussed. Because there is no appropriate test data, a real-life data was collected from a factory. In this study, a new approach has been proposed for the solution with meta-heuristics of unrelated parallel machine scheduling problems which is a combinatorial problem. This new neighborhood approach provides different machine assignments for every candidate job sequences. This approach is used by integrating into ABC and GA. To evaluate the performances of the algorithms, the real-life problem was solved by using ABC and GA algorithms under similar conditions. It was found that all jobs can be completed in two shifts without the need for a third shift. Computational results show that ABC algorithm has better performance than GA. Keywords Scheduling. Artificial bee colony algorithm. Combinatorial artificial bee colony. Unrelated parallel machine scheduling. Meta-heuristics
2006
This paper presents a new mathematical model for a multi-criteria parallel machine scheduling problem minimizing the total earliness and tardiness penalties as well as machine costs. Machines are defined as unrelated parallel machines, so they have different speeds. To solve such a NP-hard problem, a meta-heuristic method based on genetic algorithms is proposed and developed. New operators are defined and applied in order to improve the quality of solutions. A number of test problems are carried out and the associated computational results are represented. The results show that the proposed algorithm is effective.
Nature-Inspired Algorithms for Bi-Criteria Parallel Machine Scheduling
Advances in computer and electrical engineering book series, 2019
Nature has always been a source of inspiration for human beings. Nature-inspired search-based algorithms have an enormous computational intelligence and capabilities and are observing diverse applications in engineering and manufacturing problems. In this chapter, six nature-inspired algorithms, namely artificial bee colony, bat, black hole, cuckoo search, flower pollination, and grey wolf optimizer algorithms, have been investigated for scheduling of multiple jobs on multiple potential parallel machines. Weighted flow time and tardiness have been used as optimization criteria. These algorithms are very efficient in identifying optimal solutions, but as the size of the problem increases, these algorithms tend to get stuck at local optima. In order to extract these algorithms from local optima, genetic algorithm has been used. Flower pollination algorithm, when appended with GA, is observed to perform better than other counterpart nature-inspired algorithms as well as existing heuristics and meta-heuristics based on MOGA and NSGA-II algorithms.
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.
The problem investigated in this study involves an unrelated parallel machine scheduling problem with sequence-dependent setup times, different release dates, machine eligibility and precedence constraints. This problem has been inspired from a realistic scheduling problem in the shipyard. The optimization criteria are to simultaneously minimize mean weighted flow time and mean weighted tardiness. To formulate this complicated problem, a new mixed-integer programming model is presented. Considering the NP-complete characteristic of this problem, two famous meta-heuristics including a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective ant colony optimization (MOACO) which is a modified and adaptive version of BicriterionAnt algorithm are developed. Obviously, the precedence constraints increase the complexity of the scheduling problem in strong sense in order to generate feasible solutions, especially in parallel machine environment. Therefore a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. Due to the fact that appropriate design of parameter has a significant effect on the performance of algorithms, we calibrate the parameters of these algorithms by using new approach of Taguchi method. The performances of the proposed meta-heuristics are evaluated by a number of numerical examples. The results indicated that the suggested MOACO statistically outperformed the proposed NSGA-II in solving the test problems. In addition, the application of the proposed algorithms is justified by a real block erection scheduling problem in the shipyard.
Applied Mathematical Modelling, 2014
This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.
Solving multi-objective parallel machine scheduling problem by a modified NSGA-II
Applied Mathematical Modelling, 2013
In this paper, we modify a Multi-Objective Evolutionary Algorithm, known as Nondominated sorting Genetic Algorithm-II (NSGA-II) for a parallel machine scheduling problem with three objectives. The objectives are-(1) minimization of total cost due tardiness, (2) minimization of the deterioration cost and (3) minimization of makespan. The formulated problem has been solved by three Multi-Objective Evolutionary Algorithms which are: (1) the original NSGA-II (Non-dominated Sorting Genetic Algorithm-II), (2) SPEA2 (Strength Pareto Evolutionary Algorithm-2) and (3) a modified version of NSGA-II as proposed in this paper. A new mutation algorithm has also been proposed depending on the type of problem and embedded in the modified NSGA-II. The results of the three algorithms have been compared and conclusions have been drawn. The modified NSGA-II is observed to perform better than the original NSGA-II. Besides, the proposed mutation algorithm also works effectively, as evident from the experimental results.
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...
In this paper we propose a two-stage multi-population genetic algorithm (MPGA) to solve parallel machine scheduling problems with multiple objectives. In the ÿrst stage, multiple objectives are combined via the multiplication of the relative measure of each objective. Solutions of the ÿrst stage are arranged into several sub-populations, which become the initial populations of the second stage. Each sub-population then evolves separately while an elitist strategy preserves the best individuals of each objective and the best individual of the combined objective. This approach is applied in parallel machine scheduling problems with two objectives: makespan and total weighted tardiness (TWT). The MPGA is compared with a benchmark method, the multi-objective genetic algorithm (MOGA), and shows better results for all of the objectives over a wide range of problems. The MPGA is extended to scheduling problems with three objectives: makespan, TWT, and total weighted completion times (TWC), and also performs better than MOGA.
This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.