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Artificial chromosomes with genetic algorithm (ACGA) is one of the latest Estimation of Distribution Algorithms (EDAs). This algorithm has been used to solve different kinds of scheduling problems successfully. However, due to its proba bilistic model does not consider the variable interactions, ACGA may not perform well in some scheduling problems...
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... was superior to ACGA2 DP in terms of CPU time. Although DPs and SAPT both use general pair-wise interchange (GPI) for sequencing, SAPT only uses the neighborhood generated by every possible pair- wise interchange, i.e. the neighborhood distance is 1. However, the neighborhood distance of DPs is 2 and 3 so DPs may need a longer computational time (Fig. ...
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Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four varian...
This paper describes a new parallel Branch-and-Bound algorithm for
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Multi-objective flow shop scheduling plays a key role in real-life scheduling problem which attract the researcher attention. The primary concern is to find the best sequence for flow shop scheduling problem. Estimation of Distribution Algorithms (EDAs) has gained sufficient attention from the researchers and it provides prominent results as an alt...
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... combined these dominance relations with GA and showed that the combined metaheuristic performs much better than the single GA. Chen and Chen (2011) and Chen et al. (2014) presented an extended artificial chromosomes with GA for the same problem and showed that their algorithm outperforms those of Rabadi et al. (2007) and . The GA of Chen et al. (2007), the metaheuristic of Hepdogan et al. (2009), and the GA of Chen and Chen (2011) have not been compared. ...
Scheduling involving setup times/costs plays an important role in today's modern manufacturing and service environments for the delivery of reliable products on time. The setup process is not a value added factor, and hence, setup times/costs need to be explicitly considered while scheduling decisions are made in order to increase productivity, eliminate waste, improve resource utilization, and meet deadlines. However, the vast majority of existing scheduling literature, more than 90 percent, ignores this fact. The interest in scheduling problems where setup times/costs are explicitly considered began in the mid-1960s and the interest has been increasing even though not at an anticipated level. The first comprehensive review paper (Allahverdi et al., 1999) on scheduling problems with setup times/costs was in 1999 covering about 200 papers, from mid-1960s to mid-1988, while the second comprehensive review paper (Allahverdi et al., 2008) covered about 300 papers which were published from mid-1998 to mid-2006. This paper is the third comprehensive survey paper which provides an extensive review of about 500 papers that have appeared since the mid-2006 to the end of 2014, including static, dynamic, deterministic, and stochastic environments. This review paper classifies scheduling problems based on shop environments as single machine, parallel machine, flowshop, job shop, or open shop. It further classifies the problems as family and non-family as well as sequence-dependent and sequence-independent setup times/costs. Given that so many papers have been published in a relatively short period of time, different researchers have addressed the same problem independently, by even using the same methodology. Throughout the survey paper, the independently addressed problems are identified, and need for comparing these results is emphasized. Moreover, based on performance measures shop and setup times/costs environments, the less studied problems have been identified and the need to address these problems is specified. The current survey paper, along with those of Allahverdi et al. (1999, 2008), is an up to date survey of scheduling problems involving static, dynamic, deterministic, and stochastic problems for different shop environments with setup times/costs since the first research on the topic appeared in the mid-1960s.
... The numerical results indicated that there have been many research works and have some result in single-machine problem (e.g., [19][20][21][22]). Therefore, the results of single-machine research not only provide insights into the single-machine environment but also provide a basis for intelligent algorithm for SMISP [23][24][25][26][27][28][29][30]. In order to solve the large-scale problem, it should be effective to apply the intelligent algorithm [9,31] for SMISP to find the optimal solution. ...
... (i) the number of jobs; tts value is 20, 50, or 100; (ii) processing times of jobs ; the values of processing time are assumed as integers and drawn out of [5,25], [30,100] } if (cw1 < cw2) then cw1 Algorithm 1: The code of the fitness evaluation mechanism. ...
The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP) is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA) with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.
... , n). Supposing we have the real vector: the permutation obtained when decoding the vector is (2,3,7,1,9,8,5,4,6). Introduced first by Bean [2], this strategy is called the Random Keys algorithm. ...
... One can easily notice that real-valued vectors with different values can lead to the same permutation. The real vector would represent the permutation (2,3,7,1,9,8,5,4,6). In both cases, the permutation that codifies the real vector is the same, although the vector is different, therefore the same fitness value is assigned by the objective function. ...
... In [54,55], an operator called Guided Mutation is introduced which combines a conventional mutation operator with a probabilistic model learnt at each step. Chen et al. [7] propose a hybrid EDA for solving single machine scheduling problems that combines classic univariate and bivariate probabilistic models with crossover and mutation genetic operators. Due to the complexity of studying hybrid approaches, we decided not to include these approaches in the experiments. ...
Estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems.
This paper presents an optimization algorithm based on a meta-heuristic search inspired from the behavior of monkey. The main steps of the optimization search algorithm comprise climb process, watch-jump process and somersault process. The proposed method is a completion of the Monkey algorithm to the original steps; we have added two more: one-component perturbation and all-components perturbation. This way the monkeys have the possibility of accelerating the local convergence and also of searching better values globally. The modified algorithm for these processes have been designed and presented in the paper. The performance of the modified algorithm is tested on some benchmark functions. These functions are evaluated by considering the cases in which the problem is set as 30, 50 or even 100 dimensions. The computational results have demonstrated that the algorithm performs much better for solving a set of global optimization problems.
The single machine scheduling problem with sequence-dependent setup times with the objective of minimizing the total weighted tardiness is a challenging problem due to its complexity, and has a huge number of applications in real production environments. In this paper, we propose a memetic algorithm that combines and extends several ideas from the literature, including a crossover operator that respects both the absolute and relative position of the tasks, a replacement strategy that improves the diversity of the population, and an effective but computationally expensive neighborhood structure. We propose a new decomposition of this neighborhood that can be used by a variable neighborhood descent framework, and also some speed-up methods for evaluating the neighbors. In this way we can obtain competitive running times. We conduct an experimental study to analyze the proposed algorithm and prove that it is significantly better than the state-of-the-art in standard benchmarks.
