Autonomy In Mobile Fulfillment System: Goods-To-Man Picking System

Nowadays, issues regarding to e-commerce unpredictability become a problem in warehouse operations. This unpredictability is make difficult by fulfillment challenges. Designing a goods-to-man picking system and dispatching order strategy based on service in random order (SIRO) can be one of promising alternative to reduce AGV empty travel distance. The focus is on the warehouse operations, start from item classification on dynamic slots location, multi-attribute AGV dispatching rules and AGV battery management. The system aims to minimize total cost of AGV by assign the multi-attribute dispatching rules and bidding process to get on time delivery as many orders that can be completed, dealing with minimum battery-charging effects on the system operation. The planning system considers dynamic nature of customer order demand, and the simulation based development is used to model real time dynamic slots storage location and AGVs availability. The computational experiments showed this methodology most likely could reduce total cost by perform more than one AGV in operating system

Dispatching Vehicles Considering Uncertain Handling Times at Port Container Terminals

This paper considers the problem of vehicle dispatching at port container terminals in a dynamic environment. The problem deals with the assignment of delivery orders of containers to vehicles while taking into consideration the uncertainty in the travel times of the vehicles. Thus, a real-time vehicle dispatching algorithm is proposed for adaptation to the dynamic changes in the states of the container terminals. To evaluate the performance of the proposed algorithm, a simulation study was conducted by considering various values of decision parameters under the uncertainty in travel times. Further, the performance of the proposed algorithm was compared with those of heuristic algorithms from previous studies

Design and Analysis of Multiple-Load Automated Guided Vehicle Dispatching Algorithms

This paper addresses the problems of dispatching multiple-load automated guided vehicles (AGVs) in flexible manufacturing systems (FMSs). A pickup-or-delivery-en-route (PDER) rule is proposed to address the task-determination problem that indicates if a partially loaded AGV’s next task should be picking up a new job or dropping off a carried load. A workload-balancing (WLB) algorithm is developed to deal with the pickup-dispatching problem that determines which job should be assigned to an AGV. A simulation experiment is conducted to compare the PDER rule with an existing task-determination rule in 2 representative FMSs. We use another simulation experiment to compare the WLB rule with 4 existing pickup-dispatching rules in 3 FMSs. The results show that the PDER rule can significantly improve the system throughput and reduce the average time in system of parts, while the WLB rule also has an outstanding throughput performance

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg

Agent-based material transportation scheduling of AGV systems and its manufacturing applications

制度:新 ; 報告番号:甲3743号 ; 学位の種類:博士(工学) ; 授与年月日:2012/9/10 ; 早大学位記番号:新6114Waseda Universit

A Review Of Design And Control Of Automated Guided Vehicle Systems

This paper presents a review on design and control of automated guided vehicle systems. We address most key related issues including guide-path design, estimating the number of vehicles, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing, and conflict resolution. We discuss and classify important models and results from key publications in literature on automated guided vehicle systems, including often-neglected areas, such as idle-vehicle positioning and battery management. In addition, we propose a decision framework for design and implementation of automated guided vehicle systems, and suggest some fruitful research directions

Modelling of dual-cycle strategy for container storage and vehicle scheduling problems at automated container terminals

This study proposes a new approach to determine the dispatching rules of AGVs and container storage locations, considering both unloading and loading processes simultaneously. We formulate this problem as a mixed integer programming model, aiming to minimise the ship’s berth time. Optimal solutions can be obtained in small sizes, however, large-sized problems are hard to solve optimally in a reasonable time. Therefore, a heuristic method, i.e. genetic algorithm is designed to solve the problem in large sizes. A series of numerical experiments are carried out to evaluate the effectiveness of the integration approach and algorithm

Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems

Due to increasing demand for unique products, large variety in product portfolios and the associated rise in individualization, the efficient use of resources in traditional line production dwindles. One answer to these new challenges is the application of matrix-shaped layouts with multiple production cells, called Matrix Production Systems. The cycle time independence and redundancy of production cell capabilities within a Matrix Production System enable individual production paths per job for Flexible Mass Customisation. However, the increased degrees of freedom strengthen the need for reliable production control systems compared to traditional production systems such as line production. Beyond reliability a need for intelligent production within a smart factory in order to ensure goal-oriented production control under ever-changing manufacturing conditions can be ascertained. Learning-based methods can leverage condition-based reactions for goal-oriented production control. While centralized control performs well in single-objective situations, it is hard to achieve contradictory targets for individual products or resources. Hence, in order to master these challenges, a production control concept based on a decentralized multi-agent bidding system is presented. In this price-based model, individual production agents - jobs, production cells and transport system - interact based on an economic model and attempt to maximize monetary revenues. Evaluating the application of learning and priority-based control policies shows that decentralized multi-agent production control can outperform traditional approaches for certain control objectives. The introduction of decentralized multi-agent reinforcement learning systems is a starting point for further research in this area of intelligent production control within smart manufacturing

Agent-based transportation planning compared with scheduling heuristics

Here we consider the problem of dynamically assigning vehicles to transportation orders that have di¤erent time windows and should be handled in real time. We introduce a new agent-based system for the planning and scheduling of these transportation networks. Intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. We use simulation to compare the on-time delivery percentage and the vehicle utilization of an agent-based planning system to a traditional system based on OR heuristics (look-ahead rules, serial scheduling). Numerical experiments show that a properly designed multi-agent system may perform as good as or even better than traditional methods

Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems

We consider the real-time scheduling of full truckload transportation orders with time windows that arrive during schedule execution. Because a fast scheduling method is required, look-ahead heuristics are traditionally used to solve these kinds of problems. As an alternative, we introduce an agent-based approach where intelligent vehicle agents schedule their own routes. They interact with job agents, who strive for minimum transportation costs, using a Vickrey auction for each incoming order. This approach offers several advantages: it is fast, requires relatively little information and facilitates easy schedule adjustments in reaction to information updates. We compare the agent-based approach to more traditional hierarchical heuristics in an extensive simulation experiment. We find that a properly designed multiagent approach performs as good as or even better than traditional methods. Particularly, the multi-agent approach yields less empty miles and a more stable service level