Toshimitsu Higashi

Self-organizing control of urban traffic signal network

This paper proposes a self-organizing control strategy of a signal network for urban traffic flows. The signal network is described using a system of nonlinear oscillators with the nearest neighborhood coupling. The natural frequency, split and coupling constants of the signals are adjusted dynamically according to local information of traffic flows so that the desired network offset patterns are self-organized through mutual entrainment. We provide a preliminary analysis of the coupled oscillator system. Numerical simulations demonstrate the effectiveness of the proposed method under dynamical environmental changes.

Metaheuristic scheduling of multiple picking agents for warehouse management

Purpose – This paper aims to develop a scheduler for multiple picking agents in a warehouse that takes into account distance and loading queue delay minimization within the context of minimizing makespan (i.e. picking time).Design/methodology/approach – The paper uses tabu search to solve the scheduling problem in a more global sense. Each search iteration is enhanced by a custom local search (LS) procedure that hastens convergence by driving a given schedule configuration quickly to a local minimum. In particular, basic operators transfer demand among agents to balance load and minimize makespan. The new load distribution is further improved by considering a vehicle‐routing problem on the picking assignments of the agents with relocated demands. Loading queue delays that may arise from the reassignments are systematically minimized using a fast scheduling heuristic.Findings – The proposed tabu scheduler greatly improves over a widely practiced scheduling procedure for the given problem. Variants of the t...

Scheduling multiple agents for picking products in a warehouse

In this paper, the final stage of a multiphase approach for solving the picking problem in a warehouse is addressed. Given a number of agents, each with its own set of picking sequences (trips or routes) to accomplish, a dispatching problem is described and shown to have a non-polynomial search space with respect to the number of agents and number of routes. A simulation-based scheduling procedure is proposed to solve the problem. The aim is to reduce potential delays induced by agent queues. Extensive statistical simulations on a realistic warehouse operating at varying conditions are conducted to show that the said dispatching procedure is able to make significant improvements with respect to minimizing operating time, on the average, over the case when no dispatching policy is applied to the agents

Route generation for warehouse management using fast heuristics

In this paper, fast heuristics for a centralized multi-agent route planner are presented and computationally evaluated. We solve a sub-problem of warehouse scheduling involving the routing of intelligent agents as a preliminary step in optimizing the total schedule. The problem involves the generation of routes for automated agents tasked with the transfer of items within a warehouse from storage pallets to a common loading shed. The goal is to minimize the total distance of the routes and the number of routes generated. This constitutes a multiple-objective optimization problem which is NP-hard and hence can take a prohibitively long time to solve using existing search-based techniques. The approach adapted here is to model the system as a split delivery vehicle routing problem (SDVRP) with grid distances and to solve it using heuristics based on tested operations research concepts. Twenty-two such heuristics are tested including the well-known greedy nearest-neighbor (NN) heuristic, and the established savings heuristic of Clarke and Wright. The author introduces two SDVRP variations of the NN algorithm, namely the nearest-fill (NF) and nearest-fill farthest-start (NFFS) heuristics. Existing SDVRP improvement procedures are also considered and generalized to produce numerous heuristic variations. This novel approach of applying fast vehicle routing heuristics to multi-agent routing has the advantage of yielding good quality results within a very short period of time. The results of the study show that the greedy NFFS heuristic combined with the improvement procedures, consistently produces superior results with regard to minimization of distance and the number of routes in an the instances tested.

Order scheduling of multiple stacker cranes on common rails in an automated storage/retrieval system

An order scheduling method for improving the work efficiency of multiple cranes on a common rail in an automated storage/retrieval system (AS/RS) is proposed in this paper. Most AS/RSs are equipped with one stacker crane. However, this is logistically challenging, and higher work efficiency in warehouses, such as those using more than one stacker crane, is required. In this paper, a warehouse with multiple stacker cranes working simultaneously is proposed. Unlike warehouses with only one crane, order scheduling is very difficult because there are multiple cranes working simultaneously and collisions are possible. Warehouse order must be maintained while avoiding collisions. Sometimes, detours are necessary to avoid collisions. As a result, work efficiency will decrease if a sequence of orders is not properly planned. Nevertheless, as transport works in AS/RSs are occurring randomly, scheduling cannot be conducted in advance, which makes the order scheduling of multiple cranes difficult. A fast order scheduling method is proposed here. Several orders are combined into an order cluster, and dynamic programming is used to obtain the best combination of order clustering. By utilising this method, cranes can work cooperatively, improving work efficiency to 0.95 with a two-crane AS/RS and to 0.78 with a three-crane AS/RS.

Graduated Spatial Pattern formation of Micro Robot Group

This study proposes how to make a spatial pattern of a micro robot group in distributed autonomous systems. The autonomous formation of spatial pattern is important for the advancement of cooperative robotic systems. Although some people are also studying about spatial pattern formation by the swarm of robots, their way of pattern formation is mainly that robot can get global information such as the positions of all robots. However, micro robot is not usually able to get such information because micro robot’s scale effect restricts their functions. Therefore, we propose the method of spatial complicated pattern without getting global information. Then we propose a dynamical spatial pattern formation using CA theory, and examine the group behavior with actual hardware, micro robot “MARS„. Finally, we propose inverse design of a more complicated polygon pattern formation from a circle state using Turing Instability theory.

Control methodology of stacker cranes for collision avoidance considering dynamics in a warehouse

We propose a control methodology of stacker cranes to avoid collisions in warehouse environment including the constraint on trajectories caused by dynamics. In a warehouse environment, there are three problems: constraint on trajectories caused by the consideration of dynamics (e.g., vibration control), safety in the case of an emergency stop, and practical calculation time. In the proposed method, the motion is planned through two approaches. In the first, the trajectories of cranes are chosen from candidates that satisfy the constraint on dynamics and checked to determine whether the selected trajectory ensures safety in an emergency. The calculation cost is reduced by confining the candidate trajectories using the characteristic of the number of cranes. If a collision cannot be avoided in the first approach, we adjust the confined candidate trajectories and find a suboptimal trajectory. Measures used in the second approach are to delay the movement of cranes or to generate detours. The simulation results show the effectiveness of the proposed method.

Autonomous strategy organization in the dynamical quantitative environment

This paper proposes a system, which manages both efficiency and flexibility in the manufacturing system. It has been considered that an autonomous distributed system is superior to a centralized system in terms of fail-proof expansion, load dispersion, flexibility, and the possibility of the evolution. However, it is difficult to manage both efficiency and flexibility according to conventional method in an autonomous distributed system. To solve the problem, we propose a method. The method is that the carrier organizes the whole number of the carrier in the system, according to the volume of the task and the complexity of it. And we evaluate the proposed method of self-organizing behavior by a multi-AGV autonomous system in the quantitative dynamic environment. It shown that the system has higher efficiency in this system than in the centralized system with self-organization in the quantitative dynamic environment.

Analytic Flow Design Method for an Automated Distribution Center with Multiple Shipping Areas

Abstract An analytic flow design algorithm for an automated distribution center with multiple shipping areas is presented. The main elements of the analytic flow design are the number of devices and the volumes of product flow between the devices. In the design of an automated distribution center, it is necessary to satisfy the demand throughput while minimizing construction costs. In the conventional design process, system engineers utilize experimental and intuitional approaches. However, conventional approaches are time-consuming and the design outcome is dependent on the skill of the designer. Therefore, a theoretical design algorithm is needed. We propose an analytic flow design algorithm using a dynamic network flow model and considering time-variable flow volumes according to shipment and storage schedules. To verify the feasibility of the proposed method, we perform analytic flow design using real data and confirm that the proposed method can yield a feasible analytic flow design in several minutes.

Conceptual Warehouse Design Algorithm Using a Network Flow Model

Currently, third-party logistics (3PL) services are growing rapidly. In the current business climate, the goal of each 3PL firm is to reduce the time required for warehouse design. In this paper, a warehouse design problem is discussed. The problem is to determine the number of machines used in a warehouse and the product flow among the devices. In the conventional design process, such a problem would be solved with experimental and intuitional approaches. However, conventional approaches require large amounts of time. In addition, the design outcome is dependent on the skill of the designer. In order to provide theoretical guidelines for the design itself and shorten the design process, we propose a new design method using an extended network flow model. The network flows are expressed as the combination of the flow in the preparation time and that in the lead time. The mathematical model is constructed and solved as a threshold optimization algorithm including the mixed integer linear programming problem. We then evaluated the feasibility of the proposed model and method using real data, and confirmed the effect of the proposed method with regard to time reduction and the obtained design results.