Solution In Practical Time Research Articles

Exhaustive algorithms applied to the design of inductive power transfer couplers

The design of magnetic couplers for inductive power transfer has probably become the major challenge for those who wish to enter this promising research field. The number of variables that determine physical dimensions of a coupler is typically too high to allow analytical (exact) solutions in practical time when realistic magnetic materials are to be included. Thus, this paper suggests and describes a series of algorithms based on the finite element method (FEM) able to convert basic inputs (target inductances, primary current, frequency, and mechanical restrictions) into a geometric solution that satisfies user-defined targets for uncompensated power, open-circuit voltage, and short-circuit current. Advantages of these algorithms when compared with other existing design methods are: simplicity in terms of structure at the same time that require minimum user intervention to complete a full design; do not rely in expensive finite element solvers; user does not require previous background in FEM formulation. Experimental results show that the proposed design method based on two-dimensional FEM has errors of <8% when compared with three-dimensional FEM and can perform iterations in seconds. It is expected that the proposed routines encourage and provide design insights for practitioners, enthusiasts, and non-specialized engineers.

Subquery Allocation Problem and Heuristics for Secret Sharing Distributed Database System

We discuss query optimization in a secure distributed database system, called the Secret Sharing Distributed DataBase System (SSDDBS). We have to consider not only subquery allocations to distributed servers and data transfer on the network but also decoding distributed shared data. At first, we formulated the subquery allocation problem as a constraints satisfaction problem. Since the subquery allocation problem is NP-complete in general, it is not easy to obtain the optimal solution in practical time. Secondly, we proposed aheuristic evaluationfunction for the best-first search. We constructed an optimization model on an available optimization software, and evaluated the proposed method. The results showed that feasible solutions could be obtained by using the proposed method in practical time, and that quality of the obtained solutions was good.

S1401-1-2 ある在庫管理問題に対する分枝限定法の応用([S1401-1]生産システムの新展開(基礎・理論))

In this paper, we propose a heuristic algorithm based on the Branch-and-Bound Method for solving a Stock Management Probrem. In this algorithm, we divide the problem into small ones and solve them repeatedly. This algorithm achieves considerable speedup over the exact Branch-and-Bound Method. However, we need more speedup to use this in practical situations. To this end, we improve the pruning strategy in the Branch-and-Bound Method. In particular, we define a relaxation problem that can easily be solved by Dynamic Programming and use its solution for pruning. As the result of this, we can get efficient approximate solution in practical time using standard PC.

Heuristic Instruction Scheduling Algorithm Using Available Distance for Partial Forwarding Processor

Partial forwarding is a design method to place forwarding paths on a part of processor pipeline. Hardware cost of processor can be reduced without performance loss by partial forwarding. However, compiler with the instruction scheduler which considers partial forwarding structure of the target processor is required since conventional scheduling algorithm cannot make the most of partial forwarding structure. In this paper, we propose a heuristic instruction scheduling method for processors with partial forwarding structure. The proposed algorithm uses available distance to schedule instructions which are suitable for the target partial forwarding processor. Experimental results show that the proposed method generates near-optimal solutions in practical time and some of the optimized codes for partial forwarding processor run in the shortest time among the target processors. It also shows that the proposed method is superior to hazard detection unit.

Maximizing the Number of Charging Slabs for Aluminum Reheating Furnaces by Using Constraint Logic Programming

This paper discusses a complex arrangement problem for maximizing the number of charging slabs for an aluminum reheating furnaces. It is important to decide the slabs arrangement that many number of slabs enable to charge into a furnace to improve the productivity and reduce the fuel costs. Usually, the meta-heuristic approach for instance genetic algorithm (GA) or the simulated annealing (SA) has been used to solve the combinatorial optimization problem such as a cutting stock problem or a rectangle-packing problem. This problem is similar to above problems, but it is quite different from them in considering the constraint of rolling sequence, slab transportation, and change of constraint dynamically when the slabs are extracted from a furnace according to rolling sequence. In this paper, we propose constraint logic programming (CLP) based approach. The remarkable points of this approach are the addition of redundant constraints, application of slab grouping method and so on to improve the solution time. We confirm the effectiveness of the proposed approach by numerical experiments and show that it can obtain a good solution in practical time.

Optimiation Search Algorithm of Allocation Planning for Strip Coils in Hold for Shipment by Using Operational Know-how.

In the allocation planning of steel products in the hold of a ship, because the restriction which should be considered is complex and the problem scale is very large, planning systematization was difficult in the past. In this paper we study a scheduling algorithm of the allocation problem of steel products such as strip coils and sheets in the hold of a ship. Since steel products are usually stowed hierarchically in a hold, this planning becomes a three-dimensional allocation problem. First, we express the stowing-position of the products as three-dimensional integer coordinates, and model them as a combinatorial optimization problem. Next, the weight balance of the ship, loading ratio, and work efficiency of stowing are evaluated quantitatively, and they are optimized by applying the SA (Simulated Annealing) method. To obtain a preferable solution in practical time, we take the stowing know-how which the operators have in the evaluation function, and can make the search process efficient. In addition, to confirm the effectiveness of the proposed algorithm, several case studies have been carried out with actual data. Through the case studies, it has been found that the evaluation function involving the operation know-how term could reduce more unloaded coils assuring qualities of other evaluation items than the best solution obtained by the evaluation function without know-how term.

The use of dynamic programming in genetic algorithms for permutation problems

To deal with computationally hard problems, approximate algorithms are used to provide reasonably good solutions in practical time. Genetic algorithms are an example of the meta-heuristics which were recently introduced and which have been successfully applied to a variety of problems. We propose to use dynamic programming in the process of obtaining new generation solutions in the genetic algorithm, and call it a genetic DP algorithm. To evaluate the effectiveness of this approach, we choose three representative combinatorial optimization problems, the single machine scheduling problem, the optimal linear arrangement problem and the traveling salesman problem, all of which ask to compute optimum permutations of n objects and are known to be NP-hard. Computational results for randomly generated problem instances exhibit encouraging features of genetic DP algorithms.