Qingsong Zhao

A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.

A two-stage preference-based evolutionary multi-objective approach for capability planning problems

As a type of long-term planning problems, capability planning problems (CPPs) have received considerable attention in the defense and military area. In this paper, we model CPPs as a type of project scheduling problems, referred to as multi-mode resource investment project scheduling problems (MRIPSPs). The makespan and the cost are simultaneously considered. To deliver decision support, a two-stage approach is developed considering both operational and strategic perspectives. At both levels, knowledge of experts or preference of decision makers is utilized. By integrating domain knowledge at the operational level and preference information at the strategic level into the optimization algorithm, a two-stage preference-based multi-objective evolutionary algorithm is proposed. A hypothetical case with 16 tasks is studied. The experimental results show that by focusing computational efforts on the sub-regions where experts or decision makers are interested, we can obtain the solutions which are not only closer to the true Pareto front in objective space, but also hold good characteristics in decision space.

A Hybrid Multiobjective Genetic Algorithm for Robust Resource-Constrained Project Scheduling with Stochastic Durations

We study resource-constrained project scheduling problems with perturbation on activity durations. With the consideration of robustness and stability of a schedule, we model the problem as a multiobjective optimization problem. Three objectives—makespan minimization, robustness maximization, and stability maximization—are simultaneously considered. We propose a hybrid multiobjective evolutionary algorithm (H-MOEA) to solve this problem. In the process of the H-MOEA, the heuristic information is extracted periodically from the obtained nondominated solutions, and a local search procedure based on the accumulated information is incorporated. The results obtained from the computational study show that the proposed approach is feasible and effective for the resource-constrained project scheduling problems with stochastic durations.

The study of guided emergent behavior in system of systems requirement analysis

Emergent behavior is a core trait of SoSs. But the emergence cannot be predicted before its appearance. We propose a method which using Semi-autonomous Agent based simulation to detect the emergent behaviors of SoSs. Agent based Modeling and Simulation are available for the observation of emergent behaviors of SoSs. We propose the multi-agent based simulation methods to demonstrate the evolution of SoSs. Semi-autonomous Agents is applied to model the elements which are consists of SoSs. The framework of Parallel executive and feedback to real world is used to verify and validate the emergent properties which are useful to the SoSs or not. Through the comparation between real world executive results and simulation outcomes, we could regulate the parameters or adjust rules running in the SoSs and guide the emergent behavior of SoSs to the stakeholders desire direction.

Study on System of Systems capability modeling framework based on complex relationship analyzing

System of Systems (SoS) capability has two forms—Capability Requirement (CR) and Capability Property (CP). This paper emphasizes on the emergence process of CP. A double layer CP modeling framework is proposed based on the complexity analyzing of SoS relationships, and the key modeling technique has discussed. The double layer framework addressed a modeling process from micro mechanism modeling to macro behavior distinguishing. The micro mechanism modeling is creating network model of SoS relationship based on analyzing and formalizing SoS complex relationship, and the macro behavior distinguishing is recognizing SoS CP based on aggregating capability index system. The framework researches in SoS internal complex relationship from the prospective of capability, focuses on relationships which have major influence on the CP, and induces the consequence of relationships through nonlinear process modeling.

An Approach for Weapon System-of-Systems Scheme Generation Based on a Supernetwork Granular Analysis

A weapon system of systems (WSoS) is a collection of interdependent systems providing multiple interdependent capabilities to support multiple tasks. In this paper, we propose a WSoS scheme generation method based on a granular analysis of a WSoS supernetwork. More precisely, we establish a WSoS supernetwork model based on a “network-centered” combat mode. Next, based on the theory of quotient space, a granular analysis is presented to reduce the complexity of the WSoS scheme generation. Then, the WSoS supernetwork is converted into a directed weighted network with multiple granularities, and an algorithm for the WSoS scheme generation based on the directed weighted network is presented. Consequently, each WSoS scheme corresponds to a path in the directed weighted network, and the capability of supporting the realization of a task by a WSoS can be analyzed based on the schemes. Finally, the feasibility of the proposed approach is demonstrated using an illustrative example.

An Architectural Approach for Capability Mapping and Gap Analysis

Abstract To cope with the increasing complexity and constantly changing capability requirements of system-of-systems, an architectural approach for capability mapping and gap analysis is proposed. Firstly, a more specific architecture framework is presented. Secondly, on the basis of the analysis of the semantic relationships between constituent architecture data elements, a capability mapping approach is proposed to manage the relations between capabilities and systems based on the mapping matrixes representing associations of core entities. Then, a four-step capability mapping process for the mapping from capabilities to systems is suggested. Moreover, the capability gap analysis on how to identify and fill the capability gaps is also studied. Finally, an example of specific Net-Centric Warfare mission of Precision Engagement is used to illustrate the feasibility of the approach.

Research on development strategy of weapon equipment in antagonistic environment

Weapon equipment development strategy is an unstructured, complex multi-criteria decision problem, especially in antagonistic environment. In this paper, a game frame which combines value engineering model and game equilibrium solution is presented for the development strategy of weapon equipment, which is one of the main novelties. Considering the development strategy of the opponent, this paper analyzes the game elements in the development process of weapon equipment in detail, and regulates the frame of weapon equipment selection quantitatively. Multi-level fuzzy comprehensive evaluation method is used to measure the effectiveness of weapon equipment. Finally, the feasibility and validity of the model are verified by the example of the modernization of the US and Russian Army combat forces.

Robust system portfolio selection with multi-function requirements and system instability

System Portfolio Selection (SPS) problem is equivalent to multi-objective optimization problem, with multi-function requirements incommensurable. In the paper, the SPS problem is re-specified with a more practical formulation, comparing to general portfolio problem. Then, both feasible and non-inferior solution is re-defined considering characteristics of SPS problem, specifically, four rules of system function combinations are proposed according to practical cases. Then, the instability of system performance is involved in SPS problem, and the robust theory is employed to solving the uncertainty of system function values with definition of robust non-inferior solution. Next, the paper proposes an immediately updating algorithm to solve the problem of solution space exponentially growing. Finally, a case study is used to demonstrate the usefulness and effectiveness of the proposed approaches. It shows that the approach can provide an efficient guidance for decision-makers in the process of making a SPS.

DSM-based complex aerospace projects development process design and optimization

With the scale and complexity of aerospace projects raising, how to shorten the development time of aero-components has become an emergent problem. In this paper, we focus on the technical needs in Complex Product Development Process Planning (CPDPP) for aerospace projects. The difficulties of designing a complex aero-component result not only from masses of tasks, but also from iterations between coupled tasks. Iteration is a somewhat negative word since it implies re-doing work and often takes a long time to converge during a whole development process. Under the support of researches in the past, this paper specifies the steps of constructing Design structure matrix (DSM) and proposes a method of handling overlapping iteration and sequential iteration, which makes designers integrate all DSMs established from different views and understand products design structure on the whole. Furthermore it proposes a new decoupling method of DSM based on relevant analyses which is evaluated by an index system consisted of sensitivity and changeability.