Black hole: A new heuristic optimization approach for data clustering

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, ie, the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively Initial solutions are formed and encoded into particles of the PSO algorithm The particles ''fly'' intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles' movements to form a modified PSO algorithm Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated Case studies show that the developed PSO can generate satisfactory results in both applications

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Applications of particle swarm optimisation in integrated process planning and scheduling

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

Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first Then, the optimization model of assembly sequences is presented The mapping rules between the optimization model and the traditional PSO model are given The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints

Chaotic particle swarm optimization for assembly sequence planning

One objective of process planning optimization is to cut down the total cost for machining process, and the ant colony optimization (ACO) algorithm is used for the optimization in this paper. Firstly, the process planning problem, considering the selection of machining resources, operations sequence optimization and the manufacturing constraints, is mapped to a weighted graph and is converted to a constraint-based traveling salesman problem. The operation sets for each manufacturing features are mapped to city groups, the costs for machining processes (including machine cost and tool cost) are converted to the weights of the cities; the costs for preparing processes (including machine changing, tool changing and set-up changing) are converted to the `distance' between cities. Then, the mathematical model for process planning problem is constructed by considering the machining constraints and goal of optimization. The ACO algorithm has been employed to solve the proposed mathematical model. In order to ensure the feasibility of the process plans, the Constraint Matrix and State Matrix are used in this algorithm to show the state of the operations and the searching range of the candidate operations. Two prismatic parts are used to compare the ACO algorithm with tabu search, simulated annealing and genetic algorithm. The computing results show that the ACO algorithm performs well in process planning optimization than other three algorithms.

Application of ant colony optimization algorithm in process planning optimization

Applications of particle swarm optimisationin integrated process planning and scheduling

Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed Based on this model, a modern evolutionary algorithm, ie the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem

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Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. In this paper, the complicated operation sequencing process has been modelled as a combinatorial optimization problem, and a modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles ‘fly’ intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles' movements, forming a modified PSO algorithm. A case study involving three prismatic parts has be...

Y W Guo

Papers

Applications of particle swarm optimisation in integrated process planning and scheduling

Applications of particle swarm optimisationin integrated process planning and scheduling

Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach

Operation sequencing optimization using a particle swarm optimization approach