Fuqing Zhao

A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems

Modern manufacturing systems have to cope with dynamic changes and uncertainties such as machine breakdown, hot orders and other kinds of disturbances. Holonic manufacturing systems (HMS) provide a flexible and decentralised manufacturing environment to accommodate changes dynamically. HMS is based on the notion of holon, an autonomous, co-operative and intelligent entity which is able to collaborate with other holons to complete the tasks. HMS requires a robust coordination and collaboration mechanism to allocate available resources to achieve the production goals. In this paper, a basic integrated process planning and scheduling system, which is applicable to the holonic manufacturing systems is presented. A basic architecture of holonic manufacturing system is proposed from the viewpoint of the process planning and the scheduling systems. Here, the process planning is defined as a process to select suitable machining sequences of machining features and suitable operation sequences of machining equipments, taking into consideration the short-term and long-term capacities of machining equipments. A fuzzy inference system (FIS), in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS, is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines capacity. The mean time for failure (MTF) values are input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalised based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilisation machines, sometimes faced by unreliable machine, the hybrid particle swarm optimisation (PSO) with differential evolution (DE) has been applied to balance the load for all the machines. Simulation studies show that the proposed system can be used as an effective way of choosing machines in integrated process planning and scheduling.

A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.

A hybrid algorithm based on PSO and simulated annealing and its applications for partner selection in virtual enterprise

Partner selection is a very popular problem in the research of virtual organization and supply chain management, the key step in the formation of virtual enterprise is the decision making on partner selection. In this paper, a activity network based multi-objective partner selection model is put forward. Then a new heuristic algorithm based on particle swarm optimization(PSO) and simulated annealing(SA) is proposed to solve the multi-objective problem. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search(by self experience) and global search(by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the hybrid algorithm to both the standard PSO and SA models, the simulation results show that the proposed model and algorithm are effective.

Integration of Process Planning and Production Scheduling Based on A Hybrid PSO and SA Algorithm

Process planning and production scheduling play important roles in manufacturing systems. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system are proposed. Machines are chosen based on the machines reliability characteristics. This ensures the capability of the machine in fulfilling the production demand. In addition, based on the capability information, the load for each machine is balanced by using the particle swarm optimization (PSO). Simulation study shows some promising results in integrating production capability and load balancing during scheduling activity. There are few objectives could be optimized individually or simultaneously. This gives a choice to the scheduler in determining which objective is the most important

A Hybrid Self-Adaptive Pso Algorithm and its Applications for Partner Selection in Holonic Manufacturing System (HMS)

Partner selection is a very popular problem in the research of HMS, the key step in the formation of HMS is the decision making on partner selection. In this paper, collaboration process between holons is modeling with contract net protocol; and an activity network based multi-objective partner selection model is put forward. Then a new hybrid self-adaptive PSO (HAMPSO) algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) is proposed to solve the multi-objective problem. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. GA provides the optimization parameter of PSO to get a good performance during the hybrid search process. HAMPSO implements easily and reserves the generality of PSO and GA. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum by velocity mutation. We compare the hybrid algorithm to both the standard PSO and GA model. The simulation results show that the proposed model and algorithm are effective. Moreover, such HAMPSO can be applied to many combinatorial optimization problems by simple modification

A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem

In this article, a chemotaxis-enhanced bacterial foraging optimisation (CEBFO) is proposed to solve the job shop scheduling problem more effectively. The new approach, which is based on a new chemotaxis with the differential evolution (DE) operator added, aims at solving the tumble failure problem in the tumble step and accelerates the convergence speed of the original algorithm. The effectiveness of the new chemotaxis and the convergence are proved theoretically and tested in continuous problems. Furthermore, a local search operator was designed, which can improve the local search ability of novel algorithm greatly. Finally, the experiments were conducted on a set of 38 benchmark problems of job shop scheduling and the results demonstrated the outperformance of the proposed algorithm.

A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

A Discrete Water Wave Optimization (DWWO) Algorithm is proposed.An Improved Iterated greedy algorithm is integrated into the framework of DWWO.A modified initialization strategy is proposed to generate the initial population.A ruling out inferior solution mechanism is added to improve the convergence speed.The convergence of the DWWO algorithm has been proved theoretically. In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP.

A chaotic local search based bacterial foraging algorithm and its application to a permutation flow-shop scheduling problem

The permutation flow-shop scheduling problem (PFSP) is a typical combinational and non-deterministic polynomial-hard problem, with discrete solution space. In this paper, a novel bacterial foraging optimisation algorithm (BFO) has been proposed to solve the PFSP. Difficulties such as slow convergent speeds and entrapment in the local optimum were incurred by the original BFO algorithm in solving a high-dimensional combinatorial optimisation problem. In order to deal with these difficulties, a differential evolution operator and a chaotic search operator were each introduced into the original BFO algorithm to enhance the activity levels of the individual bacterium and to extend the local searching space. Theoretical analysis showed that the improved algorithm obtained more motility in chemotaxis and could converge to the global optimum with a probability of 1. Simulation results and comparisons to both continuous and combinatorial benchmark problems were used to demonstrate the effectiveness of this novel optimisation algorithm.

A hybrid harmony search algorithm with efficient job sequence scheme and variable neighborhood search for the permutation flow shop scheduling problems

Abstract The permutation flow shop scheduling problem (PFSSP), one of the most widely studied production scheduling problems, is a typical NP-hard combinatorial optimization problem. In this paper, a hybrid harmony search algorithm with efficient job sequence mapping scheme and variable neighborhood search (VNS), named HHS, is proposed to solve the PFFSP with the objective to minimize the makespan. First of all, to extend the HHS algorithm to solve the PFSSP effectively, an efficient smallest order value (SOV) rule based on random key is introduced to convert continuous harmony vector into a discrete job permutation after fully investigating the effect of different job sequence mapping schemes. Secondly, an effective initialization scheme, which is based on NEH heuristic mechanism combining with chaotic sequence, is employed with the aim of improving the solution’s quality of the initial harmony memory (HM). Thirdly, an opposition-based learning technique in the selection process and the best harmony (best individual) in the pitch adjustment process are made full use of to accelerate convergence performances and improve solution accuracy. Meanwhile, the parameter sensitivity is studied to investigate the properties of HHS, and the recommended values of parameters adopted in HHS are presented. Finally, by making use of a novel variable neighborhood search, the efficient insert and swap structures are incorporated into the HHS to adequately emphasize local exploitation ability. Experimental simulations and comparisons on both continuous and combinatorial benchmark problems demonstrate that the HHS algorithm outperforms the standard HS algorithm and other recently proposed efficient algorithms in terms of solution quality and stability.

A Hybrid Particle Swarm Optimization(PSO) Algorithm Schemes for Integrated Process Planning and Production Scheduling

Process planning and production scheduling play important roles in manufacturing systems. Their roles are to ensure the availability of manufacturing resources needed to accomplish production tasks result from a demand forecast. In this paper, instead of choosing alternative machines randomly, the fuzzy inference system is being introduced for the purposes of choosing appropriate machines. Machines will be chosen based on the machines reliability characteristics. This will ensure the capability of the machine in fulfilling the production demand. In addition, based on the capability information, the load for each machine is balanced by using the particle swarm optimization (PSO). Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling