New Hybrid Parallel Algorithm for Variable-sized Batch Splitting Scheduling with Alternative Machines in Job Shops

The batch splitting scheduling problem has recently become a major target in manufacturing systems, and the researchers have obtained great achievements, whereas most of existing related researches focus on equal-sized and consistent-sized batch splitting scheduling problem, and solve the problem by fixing the number of sub-batches, or the sub-batch sizes, or both. Under such circumstance and to provide a practical method for production scheduling in batch production mode, a study was made on the batch splitting scheduling problem on alternative machines, based on the objective to minimize the makespan. A scheduling approach was presented to address the variable-sized batch splitting scheduling problem in job shops trying to optimize both the number of sub-bathes and the sub-batch sizes, based on differential evolution（DE）, making full use of the finding that the sum of values of genes in one chromosome remains the same before and after mutation in DE. Considering before-arrival set-up time and processing time separately, a variable-sized batch splitting scheduling model was established and a new hybrid algorithm was brought forward to solve both the batch splitting problem and the batch scheduling problem. A new parallel chromosome representation was adopted, and the batch scheduling chromosome and the batch splitting chromosome were treated separately during the global search procedure, based on self-adaptive DE and genetic crossover operator, respectively. A new local search method was further designed to gain a better performance. A solution consists of the optimum number of sub-bathes for each operation per job, the optimum batch size for each sub-batch and the optimum sequence of sub-batches. Computational experiments of four test instances and a realistic problem in a speaker workshop were performed to testify the effectiveness of the proposed scheduling method. The study takes advantage of DE＇s distinctive feature, and employs the algorithm as a solution approach, and thereby deepens and enriches the content of batch splitting scheduling.

Job shop scheduling problem with alternative machines using genetic algorithms

The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.

A naive optimization method for multi-line systems with alternative machines

The scheduling of parallel machines and the optimization of multi-line systems are two hotspots in the field of complex manufacturing systems.When the two problems are considered simultaneously,the resulting problem is much more complex than either of them.Obtaining sufficient training data for conventional data-based optimization approaches is difficult because of the high diversity of system structures.Consequently,optimization of multi-line systems with alternative machines requires a simple mechanism and must be minimally dependent on historical data.To define a general multi-line system with alternative machines,this study introduces the capability vector and matrix and the distribution vector and matrix.A naive optimization method is proposed in accordance with classic feedback control theory,and its key approaches are introduced.When a reasonable target value is provided,the proposed method can realize closed-loop optimization to the selected objective performance.Case studies are performed on a real 5/6-inch semiconductor wafer manufacturing facility and a simulated multi-line system constructed on the basis of the MiniFAB model.Results show that the proposed method can effectively and efficiently optimize various objective performance.The method demonstrates a potential for utilization in multi-objective optimization.

Formal Specifications and Verification of a Secure Communication Protocol Model

This paper presents a secure communication protocol model-EABM, by which network security communication can be realized easily and efficiently. First, the paper gives a thorough analysis of the protocol system, systematic construction and state transition of EABM. Then , it describes the channels and the process of state transition of EABM in terms of ESTELLE. At last, it offers a verification of the accuracy of the EABM model.