Metaheuristics in large-scale global continues optimization

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems.

Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey

An integrated scheduling method for AGV routing in automated container terminals

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

/pdf/multi-objective-agv-scheduling-in-an-fms-using-a-hybrid-of-3v6904gfif.pdf

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Greedy randomized adaptive search for dynamic flexible job-shop scheduling

A Hybrid EA for Reactive Flexible Job-shop Scheduling

As a kind of effective tool in solving complex optimization problems, intelligent optimization algorithms are paid more attention to their advantages of being easy to implement and their wide applicability. This paper proposes an enhanced northern goshawk optimization algorithm to further improve the ability to solve challenging tasks. Firstly, by applying the polynomial interpolation strategy to the whole population, the quality of the solutions can be enhanced to keep a fast convergence to the better individual. Then, to avoid falling into lots of local optimums, especially late in the whole search, different kinds of opposite learning methods are used to help the algorithm to search the space more fully, including opposite learning, quasi-opposite learning, and quasi-reflected learning, to keep the diversity of the population, which is noted as a multi-strategy opposite learning method in this paper. Following the construction of the enhanced algorithm, its performance is analyzed by solving the CEC2017 test suite, and five practical optimization problems. Results show that the enhanced algorithm ranks first on 23 test functions, accounting for 79.31% among 29 functions, and keeps a faster convergence speed and a better stability on most functions, compared with the original northern goshawk optimization algorithm and other popular algorithms. For practical problems, the enhanced algorithm is still effective. When the complexity of the TSP is increased, the performance of the improved algorithm is much better than others on all measure indexes. Thus, the enhanced algorithm can keep the balance between exploitation and exploration and obtain better solutions with a faster speed for problems of high complexity.

/pdf/an-enhanced-northern-goshawk-optimization-algorithm-and-its-6iw17z8e.pdf

An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems

Considering Particle Swarm Optimization (PSO) could enhance solutions generated during the evolution process by exploiting their social knowledge and individual memory, we used PSO as a local search strategy in Genetic Algorithm (GA) framework for fine tuning the search space. GA is to make sure that every region of the search space is covered so that we have a reliable estimate of the global optimal solution and PSO is for further pruning the good solutions by searching around the neighborhood. In this paper, proposed approach is used for subspace clustering, which is an extension of traditional clustering that seeks to find clustering in different subspaces within a dataset. Subspace clustering is to find a subset of dimensions on which to improve cluster quality by removing irrelevant and redundant dimensions in high dimensions problems. The experimental results demonstrate the positive effects of PSO as a local optimizer.

A hybrid EA for high-dimensional subspace clustering problem

https://conferences.sun.ac.za/index.php/cie/cie-42/paper/download/296/151

A hybrid evolutionary algorithm for fms optimization with agv dispatching

cheduling is one of the most significant fields in manufacturing system. In this paper, we also consider how to handle materials during searching the optimal solutions of scheduling problem, because its impact in practical flexible manufacturing system (FMS) cannot be ignored. So we used the state-of-art automated guided vehicle (AGV) as a material-handling system in the FMS. We focus on the combination of an operation scheduling, which means to obtain the optimization of manufacturing scheduling, and the routing of AGVs, which is to transport materials of different operations between different machines in FMS. We use network structure to model FMS with AGV system as a material handling system. System constraints and decision variables about FMS especially related to AGVs dispatching can be presented on the network. That is to say that network modeling describes both operation scheduling information and AGV routing path information on a directed network model. We proposed a random key-based particle swarm optimization (PSO) algorithm with crossover and mutation operation to avoid premature convergence and to maintain diversity of the swarm. Numerical analyses for case study show the effectiveness of proposed approach comparing with Genetic Algorithm (GA).

The 2012 International Symposium on Semiconductor Manufacturing Intelligence

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