This article discusses the guide path design of automated guided vehicle (AGV) systems for which we propose a model that incorporates a dynamic multicommodity flow with capacity constraints from a pickup to a delivery point. The problem is formulated as the selection of a guide path connecting a given set of pickup/delivery points. A cell-based local search that is based on an original neighborhood technique is developed. In the proposed method, the guide path is effectively optimized based on a local search of cell-based neighborhood search preserving connectivity constraints and the subsequent solution of a dynamic multicommodity flow problem. The performance of the proposed method is strengthened by the redundant elimination of arcs and intensification of search according to the concept of the flow-concentrated cell. The effectiveness of the proposed method is demonstrated by comparing it with a general-purpose solver and recent algorithms for fixed-charge capacitated multicommodity network design problems. A real case study is presented to demonstrate the applicability of the proposed method by using simulation software.

For multiple Automated Guided Vehicles (AGVs) system, it is required to realize just-in-time delivery to minimize the earliness and tardiness of the specified delivery time not only to minimize the total traveling time. In this paper, we propose a heuristic algorithm for just-in-time and conflict-free routing problem of AGVs. The proposed method can derive a near-optimal task assignment and routing and scheduling of multiple AGVs to improve both the JIT performance and the total completion time. In the proposed method, a strategy to avoid collisions ensuring feasibility is proposed. The effectiveness of the proposed method is demonstrated by comparing the performance with CPLEX from computational results. The results show that the proposed method can obtain a better solution which is close to an optimal solution with a significantly shorter computational time than that derived from CPLEX.

In warehouse design, designers have to consider complex systems including temporary storage area, where loads are placed when they are passed from one machine to another. In this paper, a queuing network model is proposed to calculate the temporary storage area required for a warehouse. Previously, the warehouse design was made difficult because the design constraints and parameters were inter-related. In this paper, warehouse system is modeled as a network of nodes and the temporary storage area is expressed as a queue within each node. We used a typical warehouse system as a case study to evaluate the effectiveness of this design method. The proposed method can simultaneously determine the required temporary storage area, number of machines and long term storage area. Layout constraints can also be taken into consideration with this method.

In a warehouse, efficient product transportation is required for high productivity. In general, it is recognized that mixed-load transportation can decrease the moving time to some extent. However, many scheduling methods without regard to mixed-load transportation have recently been proposed. In this respect, we propose a design for scheduling problems with mixed-load transportation handled by vehicles. The proposed method uses two well-known dispatching rules and a local search method based on a simulated annealing algorithm. Two rules are applied to obtain the initial solution while ignoring the mixed-load transportation, and the local search method is applied to improve the initial solution to obtain a semi-optimal solution in consideration of mixed-load transportation. To evaluate the applicability of the proposed system, we implemented our system with several product transportation environment. The simulation results verify the effectiveness of the proposed method.

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

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.

Logistics network design problems are usually solved through experimental and intuitional approaches. These approaches are taken because of the complicated conditions that accompany problems associated with layout planning, stock analysis, network flow. In this study, the problem is divided into two parts: material flow and layout planning. An algorithm is examined in this study for a design that involves the formulation of material flow into the shape of mixed integer problems.

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.

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.