A bi-level optimization model for grouping constrained storage location assignment problems

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A bi-level optimization model for grouping constrained storage location assignment problems

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Random search method for estimating the parameters of the emission system signal

Within the framework of a computer statistical experiment, the test problem of identifying the parameters of a field electron emission signal using a regression model based on the Fowler— Nordheim law is considered. Two approaches to determining the parameter estimates are used in the work: ordinary least squares and random search with training. It is shown that the random search error can be neglected if, for the considered ratios of the noise level to the signal power, the number of statistical tests is approximately 103. The result allows us to expand the class of functionals used to identify the response without changing the method. The article notes the advantages of the random search method for the problem under consideration and the prospects of its applicability to tasks in a more general setting.

Equilibria and stability of four point vortices on a sphere

This paper discusses the problem of finding the equilibrium positions of four point vortices, of generally unequal circulations, on the surface of a sphere. A random search method is developed which uses a modification of the linearized equations to converge on distinct equilibria. Many equilibria (47 and possibly more) may exist for prescribed circulations and angular impulse. A linear stability analysis indicates that they are generally unstable, though stable equilibria do exist. Overall, there is a surprising diversity of equilibria, including those which rotate about an axis opposite to the angular impulse vector.