Solving a task scheduling problem is a key challenge for automatic test technology to improve throughput, reduce test time, and operate the necessary instruments at their maximum capacity. Therefore, this paper attempts to solve the automatic test task scheduling problem (TTSP) with the objectives of minimizing the maximal test completion time (makespan) and the mean workload of the instruments. In this paper, the formal formulation and the constraints of the TTSP are established to describe this problem. Then, a new encoding method called the integrated encoding scheme (IES) is proposed. This encoding scheme is able to transform a combinatorial optimization problem into a continuous optimization problem, thus improving the encoding efficiency and reducing the complexity of the genetic manipulations. More importantly, because the TTSP has many local optima, a chaotic non-dominated sorting genetic algorithm (CNSGA) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. This approach introduces a chaotic initial population, a crossover operator, and a mutation operator into the non-dominated sorting genetic algorithm II (NSGA-II) to enhance the local searching ability. Both the logistic map and the cat map are used to design the chaotic operators, and their performances are compared. To identify a good approach for hybridizing NSGA-II and chaos, and indicate the effectiveness of IES, several experiments are performed based on the following: (1) a small-scale TTSP and a large-scale TTSP in real-world applications and (2) a TTSP used in other research. Computational simulations and comparisons show that CNSGA improves the local searching ability and is suitable for solving the TTSP.

A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem

A multi-objective evolutionary algorithm based on Pareto prediction for automatic test task scheduling problems

Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close to the real Pareto Front, variable neighborhood strategy is adopted. Variable neighborhood approach is proposed to render the crossover span reasonable. Additionally, because the search space of the TTSP is so large that many duplicate solutions and local optima will exist, the Starting Mutation is applied to prevent solutions from becoming trapped in local optima. It is proved that the solutions got by VNM can converge to the global optimum by using Markov Chain and Transition Matrix, respectively. The experiments of comparisons of VNM, MOEA/D, and CNSGA (chaotic nondominated sorting genetic algorithm) indicate that VNM performs better than the MOEA/D and the CNSGA in solving the TTSP. The results demonstrate that proposed algorithm VNM is an efficient approach to solve the multiobjective TTSP.

https://downloads.hindawi.com/journals/mpe/2014/423621.pdf

A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem

To improve the disassembly efficiency, cooperative disassembly is essential for complex products. Operator task allocation is a significant challenge for cooperative disassembly sequence planning; however, it has not been studied previously to the best of our knowledge. To deal with this problem, a novel cooperative disassembly sequence and task planning (CDS&TP) method was proposed based on the genetic algorithm. A mathematical model of the CDS&TP problem was constructed based on the product’s disassembly hybrid graph model (DHGM). Focusing on the characteristics of the CDS&TP, a multi-layer chromosome coding method was proposed to describe the node layer and operator constraint layer. Thus, the initial population could be deduced through the node layer and operator constraints relationship rule model, which was proposed to construct the operator constraint and node layers. To obtain the task sequence of each operator and the disassembly time of each sequence, a chromosome fitness calculation formula was presented based on the disassembly set, which is the collection of all of the detachable nodes with a disassembly priority higher than the node. The chromosome evolution rules, such as the selection, crossover, and mutation operators, were redesigned to obtain the (approximate) optimal multiplayer cooperative disassembly sequences and task planning. Finally, a case study was proposed to validate the method.

Product cooperative disassembly sequence and task planning based on genetic algorithm

The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications, despite the fact that it is an NP-complete problem. The main feature of TTSP is the close interactions between task sequence and the scheme choice. Based on this point, the parallel implantation of genetic algorithm, called Parallel Genetic Algorithm (PGA), is proposed to determine the optimal solutions. Two branches--the tasks sequence and scheme choice run the classic genetic algorithm independently and they balance each other due to their interaction in the given problem. To match the frame of the PGA, a vector group encoding method is provided. In addition, the fitness distance coefficient (FDC) is first applied as the measurable step of landscape to analyze TTSP and guide the design of PGA when solving the TTSP. The FDC is the director of the search space of the TTSP, and the search space determinates the performance of PGA. The FDC analysis shows that the TTSP owes a large number of local optima. Strong space search ability is needed to solve TTSP better. To make PGA more suitable to solve TTSP, three crossover and four selection operations are adopted to find the best combination. The experiments show that due to the characteristic of TTSP and the randomness of the algorithm, the PGA has a low probability for optimizing the TTSP, but PGA with Nabel crossover and stochastic tournament selection performs best. The assumptions of FDC are consistent with the success rate of PGA when solving the TTSP.

Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem

Parallel test is a new important feature of the future ATS (automatic test system). The optimized parallel test task scheduling is a key problem to the parallel test. In this paper, the genetic algorithm and simulated annealing algorithm are combined effectively to find the multi-UUT (unit-under-test) parallel test tasks array to suffice multiple objectives. The mathematical model and multiple objectives of the multi-UUT parallel test task scheduling are put forward first. Then the gene coding and genetic annealing operating methods are introduced. The result of simulation shows that the algorithm is feasible and efficient.

Optimizing the Multi-UUT Parallel Test Task Scheduling Based on Multi-objective GASA

Parallel test is a new direction for the future automatic test systems (ATS) to reduce test time and costs of the increasingly complex and time-consuming Units-Under-Test (UUTs) test. It is also one of the key technologies of the DOD's "NxTest". In fact, future ATSs must provide some parallel testing features so as to have unparalleled performance. The kernel of the Parallel ATS is Parallel TPS. At present, there are no practical architectural models or patterns for the parallel TPS's R&D. This paper introduces the parallel TPS's analysis, design and application based on software architecture, components and patterns. A brief discussion of the parallel ATS's architecture and system requirements are provided. In order to achieve high levels of reusability and flexibility, an object-oriented meta-model of the parallel TPS is presented, described with Unified Modeling Language (UML). The layered component-based framework of the Parallel TPS is designed based on that meta-model. The framework makes full uses of the design patterns and is more likely to achieve reliability and extendibility. Based on such a framework and design patterns, not only can the parallel TPS reuse both its design architecture and source code, but also can be developed more efficiently and easier to maintain and extend.

Cheng Jin-jun

Papers

Optimizing the Multi-UUT Parallel Test Task Scheduling Based on Multi-objective GASA

Parallel TPS design and application based on software architecture, components and patterns