The present paper discusses an assembly line balancing problem (ALBP). ALBP discussed up to now does not consider rack spaces where tools or parts are stored. We introduce an extended resource planning and assembly line balancing problem that takes the rack space into account. An exact search method for solving the problem by using a graph structure, and a heuristics for the method are proposed. The proposed method is evaluated by computational experiments.

In this paper, a new grouping method for Group Technology using Self-Organizing Map (SOM) is proposed. The purpose of our study is to divide machines in a factory into any number of cells so that the machines in each cell can process a similar set of parts to increase productivity. A main feature of our method is to specify not only the number of the cells but also the maximum and minimum numbers of machines in a cell. Some experimental results show effectiveness of our proposed algorithm.

Progress in WDM transmission technology and the development of optical cross-connect systems has made optical backbone networks a reality. The conventional planning methodologies for such optical backbone networks calculate optimum light-path arrangements to minimize the network cost under the condition that the number of demanded light-paths is given in advance. However, the light-path demand varies according to the light-path prices. Thus, a new planning methodology for the optical backbone networks is necessary to optimize the light-path prices and to maximize the profit obtained from the network. This paper proposes a new planning methodology for the survivable optical networks. This methodology is based on economic theory for competitive markets involving plural kinds of commodities. Using this methodology, the optimum light-path prices can be decided to maximize the obtained profit. A numerical example is presented to show that the obtained profit can be improved by preparing various light-path classes with different recovery modes and introducing an appropriate light-path pricing according to the reliability of each light-path class.

The present paper addresses the design of manufacturing systems. A resource planning and task allocation problem is proposed, and a multi-agent system for this problem is discussed. In the multi-agent system, an agent exists for each resource and for each operation. The proposed multi-agent system improves the quality of resulting plans by the learning of these agents.

Modern network technologies gave rise to intelligent network reconfiguration schemes for restoration purposes and several network self-healing schemes, exploiting the capabilities of network elements (NE), have already been proposed. Each self-healing scheme has its own characteristics, regarding restoration time, flexibility, restoration cost and exploitation of NEs. Integrated self-healing networks, which combine more than one survivability techniques, mainly the Shared Self-Healing Rings (SSR) with the Dynamic Self-Healing Networks (DSN), can achieve higher network survivability and cost-effective network design. In this paper, we propose two algorithms for the design of spare and working channel capacities for integrated self-healing networks. In the first algorithm, A1, we do not take into account the capacity of network nodes, while in the second algorithm, A2, we take into account the limited capacity of network nodes. These algorithms are based on the shortest path principles, similarly to a previous algorithm (old algorithm) proposed by scientists of NEC Corporation for integrated self-healing network design. By the new algorithms we achieve more savings than by the old algorithm in total network capacity. On the other hand, strong motivation for the development of the new algorithms is the fact that the procedural steps of the old algorithm are not homogeneous; the old algorithm incorporates both heuristics and analytical methods, in contrast to the new algorithms that are pure heuristics. Moreover, we introduce restrictions in node-capacities of the network that they were not included in the old algorithm.

In this letter, base station placement is automatically determined from pre-defined candidate sites using a genetic approach, and the transmit power is obtained taking the interference situation into account in cases of interference-dominant systems. In order to apply a genetic algorithm to the base station placement problem, a real-valued representation scheme is proposed. Corresponding operators such as crossover and mutation are also introduced. The proposed algorithm is applied to an inhomogeneous traffic density environment, where a base station's coverage may be limited by offered traffic loads. An objective function is designed for performing the cell planning in a coverage- and cost-effective manner.

In this paper, we propose a floorplanning method for VLSI building block layout. The proposed method produces a floorplan under the timing constraint for a given netlist. To evaluate the wiring delay, the proposed method estimates the global routing cost for each net with buffer insertion and wire sizing. The slicing structure is adopted to represent a floorplan, and the Elmore delay model is used to estimate the wiring delay. The proposed method is based on simulated annealing. To shorten the computation time, a table look-up method is adopted to calculate the wiring delay. Experimental results show that the proposed algorithm performs well for producing satisfactory floorplans for industrial data.

This paper concerns resource planning in a VLSI assembly facility. The facility can process more than 100 sorts of WIPs (Works-In-Process) simultaneously. Specifically it performs product-mix production. An old resource estimation system, which gave a good estimation for a memory VLSI production facility, went wrong for an assembly facility. To adjust the estimation of required machinery resources of the assembly facility, a new parameter--the tuning value for the service time--is introduced. The tuning value expresses the reduction in machine utilization in the processing steps due to the product-mix. The value is empirically determined and the machinery resources can be estimated in good accuracy. Also the waiting time for processing in the incoming buffer is successfully considered in the estimation of turnaround time. However the tuning value is not enough in estimating human resources. A novel algorithm to estimate the resources for machine adjustments is proposed. The algorithm is based on a periodic assignment of multiple sorts of WIPs in a single machine, where the adjustments of machines for the product-mix are considered. The adjustments are additional operator's jobs in the product-mix. It estimates the operator request rate and machine utilization rate when multiple sorts of WIPs with different arrival rates are processed in a single machine. Finally, this resource estimation system considers the operator allocated not only to the preprocessing and postprocessing but also to the adjustments of machines for the product-mix. The estimated machinery, human resources, and turnaround time were evaluated in a real facility, and the proposed method is confirmed to be applicable in the weekly or monthly resource planning for the facility.

Reinforcement Learning (RL) is an efficient method for solving Markov Decision Processes (MDPs) without a priori knowledge about an environment, and can be classified into the exploitation oriented method and the exploration oriented method. Q-learning is a representative RL and is classified as an exploration oriented method. It is guaranteed to obtain an optimal policy, however, Q-learning needs numerous trials to learn it because there is not action-selecting mechanism in Q-learning. For accelerating the learning rate of the Q-learning and realizing exploitation and exploration at a learning process, the Q-ee learning system has been proposed, which uses pre-action-selector, action-selector and back propagation of Q values to improve the performance of Q-learning. But the Q-ee learning is merely suitable for deterministic MDPs, and its convergent guarantee to derive an optimal policy has not been proved. In this paper, based on discussing different exploration methods, replacing the pre-action-selector in the Q-ee learning, we introduce a method that can be used to implement an active exploration to an environment, the Active Exploration Planning (AEP), into the learning system, which we call the Q-ae learning. With this replacement, the Q-ae learning not only maintains advantages of the Q-ee learning but also is adapted to a stochastic environment. Moreover, under deterministic MDPs, this paper presents the convergent condition and its proof for an agent to obtain the optimal policy by the method of the Q-ae learning. Further, by discussions and experiments, it is shown that by adjusting the relation between the learning factor and the discounted rate, the exploration process to an environment can be controlled on a stochastic environment. And, experimental results about the exploration rate to an environment and the correct rate of learned policies also illustrate the efficiency of the Q-ae learning on the stochastic environment.

With the proliferation of the transistor count in VLSI design, more and more design groups try to figure out an efficient way to combine their designs. The Internet features distributed computing and resource sharing. Consequently, a hierarchical design can adequately be solved in the Internet environment. In this paper, we demonstrate the facilitation of the Internet environment by solving the area minimization floorplan problem. We propose the RMG algorithm taking advantage of the Internet. Based on the model of transfer latencies, the RMG algorithm reduces the computing time by shortening the critical path in the floorplan tree. Our experimental results show that the Internet is suitable for Electronic Design Automation (EDA).

Reinforcement learning is an efficient method for solving Markov Decision Processes that an agent improves its performance by using scalar reward values with higher capability of reactive and adaptive behaviors. Q-learning is a representative reinforcement learning method which is guaranteed to obtain an optimal policy but needs numerous trials to achieve it. k-Certainty Exploration Learning System realizes active exploration to an environment, but, the learning process is separated into two phases and estimate values are not derived during the process of identifying the environment. Dyna-Q architecture makes fuller use of a limited amount of experiences and achieves a better policy with fewer environment interactions during identifying an environment by learning and planning with constrained time, however, the exploration is not active. This paper proposes a RTP-Q reinforcement learning system which varies an efficient method for exploring an environment into time constraints exploration planning and compounds it into an integrated system of learning, planning and reacting for aiming for the best of both methods. Based on improving the performance of exploring an environment, refining the model of the environment, the RTP-Q learning system accelerates the learning rate for obtaining an optimal policy. The results of experiment on navigation tasks demonstrate that the RTP-Q learning system is efficient.

In order to lead an ongoing software project to success, it is important to flexibly control its dynamically-changing software process. However, it is generally impossible not only to exactly pre-define the production process but also to prescribe the process change process (meta-process). To solve the problem, we have focused on communication between the project staff through which process change requests presented by individuals can be immediately shared, designed, verified, validated and implemented. This paper proposes a communication model which can represent a wide variety of communication states between the project manager and developers discussing how to implement process change requests. The communication model has been derived by investigating the sort of process change requests and, based on the model, we have implemented a cooperative process planning system (called CooPs). CooPs is a communication environment designed for software projects and supports information sharing for discussing the process change requests. By using CooPs, the software project can flexibly deal with not only expected change requests but also unexpected ones. To evaluate the applicability of the communication model and the capabilities of CooPs, we have conducted an experiment which is an application of CooPs to the ISPW6 example problem. This paper describes the concepts of CooPs, the system implementation, and the experiment.

High-speed collision detection is important to realize a highly-safe intelligent vehicle. In collision detection, high-computational power is required to perform matching operation between discrete points on surfaces of a vehicle and obstacles in real-world environment. To achieve the highest performance, a hierarchical matching scheme is proposed based on two representations: the coarse representation and the fine representation. A vehicle is represented as a set of rectangular solids in the fine representation (fine rectangular solids), and the coarse representation, which is also a set of rectangular solids, is produced by enlarging the fine representation. If collision occurs between an obstacle discrete point and a rectangular solid in the coarse representation (coarse rectangular solid), then it is sufficient to check the only fine rectangular solids contained in the coarse one. Consequently, checks for the other fine rectangular solids can be omitted. To perform the hierarchical matching operation in parallel, a hierarchically-content-addressable memory (HCAM) is proposed. Since there is no need to perform matching operation in parallel with fine rectangular solids contained in different coarse ones, the fine ones are mapped onto a matching unit. As a result, the number of matching units can be reduced without decreasing the performance. Under the condition of the same execution time, the area of the HCAM is reduced to 46.4% in comparison with that of the conventional CAM in which the hierarchical matching scheme is not used.

In this paper, we consider the working VP and backup VP routing problems jointly and employ the integer programming based approach to maximize the system resource utilization and the network survivability. The VP planning problem is formulated as a nonlinear combinatorial optimization problem. The objective function minimizes the resource usage while maximizing the network survivability. By proper transformation of the objective function and applying cutting plane method, the original formulation is transformed into an integer linear programing formulation which is suitable for applying Lagrangian relaxation techniques. After Lagrangian relaxation, the problem is further decomposed into several tractable subproblems. Unlike others' work, the candidate path set does not need to be prepared in advance and the best paths are generated while solving subproblems in our approach. Heuristic algorithms based on the solving procedure of the Lagrangian relaxation are developed. Closely examining the gap between the heuristic upper bounds and the Lagrangian lower bounds reveals that the proposed algorithm can efficiently provide a nearly optimal solution for the survivable VP layout design in ATM networks.

This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss an extension of the GA for solving both path planning and trajectory planning simultaneously.

This paper discusses the development of a design tool which supports a process for constructing PVC-based, ATM networks. Because of mathematical complexities, a heuristic approach has been adopted to find an optimal network configuration. Through a GUI, users define a physical network, and PVC networks which are logically constructed within the physical network. Based on the defined network configurations and user traffic demand, the tool evaluates performance measures. In response to the results of the evaluation, network designers can modify the network configuration to improve the performance. With the aid of this tool, they can repeat this interactive process until the estimated performance measures meet a desired quality. The tool has been applied to the design of several private ATM networks which will be constructed in the near future. The response time of this design tool is so fast that wait time can be negligible.

This paper is concerned with two important planning problems for transport network planning; circuit routing problems and facility planning problems. We treated these optimization problems by taking into account survivability requirements. In the circuit routing problem tackled in this paper, therefore, optimization of circuit restoration plans, namely allocation of spare capacity for assumed failure scenarios is considered together with optimization of circuit routing in a no failure case. In the facility planning problems, failure scenarios of new facilities whose installation is yet to be determined are considered. In this paper, we present a formulation of these two optimization problems, and give 1) optimization algorithms based on the IA (Increment Assignment) method for routing problems and 2) optimization algorithms based on a combination of the GA (Genetic Algorithm) and the IA method for facility planning problems. The IA based routing algorithm can cope flexibly with various constraints on practical network operations and is applicable to large-scale complicated network models without causing a rapid increase in computation time. The GA based facility planning algorithm includes the IA based algorithm as a function for evaluating objective function values. Taking advantage of the important features of the IA based algorithm, we propose an acceleration technique for the GA based facility planning algorithm. In this paper, several numerical examples are provided and the effectiveness of the proposed algorithms is numerically evaluated.

Estimating the macroscopic demand for telephones is essential to long-term planning construction of telecommunication networks facilities. Although there are several useful forecast equations, they need some types and/or vast amounts of data that are sometimes unavailable, especially in developing countries. This paper presents a sophisticated telephone demand estimation technique that is based on the demands of residential and business users. It uses several parameters to estimate the increase in telephone demand. A simplified equation is also presented that is a function of only one parameter: normalized gross domestic product (GDP) per capita. This simplified equation is shown to be useful by using data for more than ten countries.

A new line simulator, SEMALIS has been developed. This simulator can handle complicated lot processings to maintain processing quality and efficient line operations to improve line performance. The current manufacturing line consists of five resource models: lot, process sequence, equipment, lot processing, and line operations. The parameters of these models are defined so as to accurately reflect the state of the line operations. From our simulation results, we confirmed that SEMALIS accurately identifies bottlenecks or starvations where equipment can be added or reduced to optimize equipment utilization through resource planning, and that SEMALIS can also be used to evaluate the long-term effects of line operating methods on the line performance of ASIC manufacturing lines.

This paper presents a heuristic floorplanning method that improves the method proposed by Vijayan and Tsay. It is based on tentative insertion of constraints, that intentionally produces redundant constraints to make it possible to search in a wide range of solution space. The proposed method can reduce the total area of blocks with the removal and insertion of constraints on the critical path in both horizontal and vertical constraint graphs. Experimental results for MCNC benchmarks showed that the quality of solutions of the proposed method is better than [7],[8] by about 15% on average, and even for the large number of blocks, the proposed method keeps the high quality of solutions.