Xinyu Shao

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.

Integration of process planning and scheduling-A modified genetic algorithm-based approach

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, a new integration model and a modified genetic algorithm-based approach have been developed to facilitate the integration and optimization of the two functions. In the model, process planning and scheduling functions are carried out simultaneously. In order to improve the optimized performance of the modified genetic algorithm-based approach, more efficient genetic representations and operator schemes have been developed. Experimental studies have been conducted and the comparisons have been made between this approach and others to indicate the superiority and adaptability of this method. The experimental results show that the proposed approach is a promising and very effective method for the integration of process planning and scheduling.

Integrating data mining and rough set for customer group-based discovery of product configuration rules

Product configuration design is of critical importance in design for mass customization. This paper will investigate two important issues in configuration design. The first issue is requirement configuration and a dependency analysis approach is proposed and implemented to link customer groups with clusters of product specifications. The second issue concerns the engineering configuration and it is modelled as an association relation between clusters of product specifications and configuration alternatives. A novel methodology and architecture are proposed for accomplishing the two configuration tasks and bridging the gap between them. This methodology is based on integration of popular data mining approaches (such as fuzzy clustering and association rule mining) and variable precision rough set. It focuses on the discovery of configuration rules from the purchased products according to customer groups. The proposed methodology is illustrated with a case study of an electrical bicycle.

An agent-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that these two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, an agent-based approach has been developed to facilitate the integration of these two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement.

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.

Design and implementation of a process-oriented intelligent collaborative product design system

With the competition between companies being fiercer and customers being more demanding, the efficiency of design decision-making must be improved to shorten the product design cycle. Intelligent collaborative product design is needed in the product design process. In this paper, based on the Analysis-Synthesis-Evaluation (ASE) design paradigm and the parameterization of product design, an agent that incorporates the working mechanism of expert systems is designed to assist a designer in his work. In line with the modularization of the product design, a module-based multi-agent system is developed and deployed according to the CORBA standards to facilitate the coordination among designers within the range of enterprises and even virtual enterprises. In the light of preliminary tests on the Internet, the agent and the agent system show some, advantages to increase the efficiency of the design coordination and decision-making in the product design process.

A review on Integrated Process Planning and Scheduling

Traditionally, process planning and scheduling for parts were carried out in a sequential way, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that performance of a manufacturing system can be improved greatly. In this paper, we present a review of the reported research in Integrated Process Planning and Scheduling (IPPS), discuss the extent of applicability of various approaches and suggest some future research trends.

An effective hybrid teaching–learning-based optimization algorithm for permutation flow shop scheduling problem

Abstract Permutation flow shop scheduling (PFSP) is among the most studied scheduling settings. In this paper, a hybrid Teaching–Learning-Based Optimization algorithm (HTLBO), which combines a novel teaching–learning-based optimization algorithm for solution evolution and a variable neighborhood search (VNS) for fast solution improvement, is proposed for PFSP to determine the job sequence with minimization of makespan criterion and minimization of maximum lateness criterion, respectively. To convert the individual to the job permutation, a largest order value (LOV) rule is utilized. Furthermore, a simulated annealing (SA) is adopted as the local search method of VNS after the shaking procedure. Experimental comparisons over public PFSP test instances with other competitive algorithms show the effectiveness of the proposed algorithm. For the DMU problems, 19 new upper bounds are obtained for the instances with makespan criterion and 88 new upper bounds are obtained for the instances with maximum lateness criterion.

A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem

Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic algorithm. Computational results and comparisons show that the proposed algorithms can improve the results obtained by existing algorithms developed for MMUL/BS.

A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple constraints

Two-sided assembly lines are widely applied to produce the large-sized high-volume products, such as buses and trucks. Balancing the lines is a vital design problem for the industries, and the problem is NP-hard. Besides the fundamental constraints of the conventional line balancing problem, some specific constraints may occur in the two-sided assembly line problem, including the zoning constraints, the positional constraints, and the synchronism constraints, which make the problem more complex. In this paper, an integer programming (IP) model is constructed and solved for the two-sided assembly line balancing problem which contains the above three constraints. A novel metaheuristic named late acceptance hill-climbing (LAHC) is also proposed to solve the problem effectively. The proposed algorithm is tested on several sets of instances. The computational results of the LAHC algorithm are compared with those of IP and the lower bounds of the instances. The experiment validates the effectiveness of the LAHC algorithm.