The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular flows are minimised or the number of parts flow processed within cells is maximised. In this paper, a tabu search heuristic-TSCF-that consists of dynamic tabu tenure with a long-term memory mechanism is presented to solve the cell formation problem. Test problems adopted from the literature and generated randomly are used to evaluate the performance of the proposed algorithm. In addition, two methods for quickly generating the initial solutions are proposed, namely the group-and-assign (GAA) method, and the random approach. Computational results indicate that the GAA method, accompanied by the TSCF algorithm can produce optimal solutions in less than or equal to 0.005 s for all small-and mediumsized problems. The proposed algorithm should thus be useful to both practitioners and researchers.

Based on basic ideas of DP algorithm and backtracking method, a new query optimization algorithm based on layered backtracking is proposed, in optimization of simple query, " optimal " solution can be provided; As for some complex applications, it achieve balance between complex enumerative algorithm and quality of solution based on algorithm, and get the " second best " optimal result to improve efficiency of algorithm and save resources needed in operating environment.

Consensus-based estimators have been applied in the state estimation for cooperative multi-sensor systems, and most of current studies are for the continuous-time or discrete-time case. With regards to some engineering applications, such as ballistic target tracking, it is more suitable to adopt the continuous-discrete state-space model to formulate a dynamic system, which can capture the evolution characteristics of this state process more accurately. This paper presents a novel consensus continuous-discrete Gaussian filtering (CCDGF) estimator. On the basis of strong Taylor approximation for continuous state, the estimator utilizes the fully symmetric interpolatory quadrature (FSIQ) rule to numerically resolve the first two moments of propagated Gaussian density. Then, the average consensus protocol is leveraged to iterate the local innovations of Gaussian filtering framework at each sensor. The consensus estimates with odd-degree accuracy can be obtained through sufficient exchanges of neighborhood information. Finally, it is demonstrated by simulation examples that the CCDGF estimator can achieve performance close to its centralized counterpart, and has higher tracking accuracy with the increase of quadrature degree. INDEX TERMS Consensus-based estimator, continuous-discrete state-space system, cooperative ballistic target tracking, fully symmetric interpolatory quadrature, Gaussian filtering.