Debjit Roy

Performance analysis and design trade-offs in warehouses with autonomous vehicle technology

Distribution centers have recently adopted Autonomous Vehicle-based Storage and Retrieval Systems (AVS/RSs) as a potential alternative to traditional automated storage and retrieval systems for processing unit-load operations. The autonomy of the vehicles in an AVS/RS provides a level of hardware sophistication, which can lead to the improvements in operation efficiency and flexibility that will be necessary in distribution centers of the future. However, in order to exploit the potential benefits of the technology, an AVS/RS must be designed using a detailed understanding of the underlying dynamics and performance trade-offs. Design decisions such as the configuration of aisles and columns, allocation of resources to zones, and vehicle assignment rules can have a significant impact on the performance of AVS/RSs. In this research, the performance impact of these design decisions is investigated using an analytical model. The system is modeled as a multi-class semi-open queuing network with class switching and a decomposition-based approach is developed to evaluate the system performance and obtain insights. Numerical studies provide various insights that could be useful in the design conceptualization of AVS/RSs.

State of the Practice: A Review of the Application of OR/MS in Freight Transportation

Freight transportation is an important part of the global supply chain. As distances shipped grow and supply chains become more complex and fragile, operations research OR can play an important role in improving the efficiency and robustness of supply networks. This article describes the state of the practice in OR and freight transportation, highlighting recent successful and widely used analytical techniques in oceanic transportation and port operations, and barge, freight rail, intermodal, truckload, less than truckload, and air freight transportation, as well as the use of OR techniques in third-party logistics.

A Stochastic Petri Net Approach for Inventory Rationing in Multi-Echelon Supply Chains

Manufacturing supply chains are considered as discrete event dynamical systems (DEDS) where coordination of material and information flows is essential to satisfy customer orders and to improve the bottomline of the constituent organizations. A critical problem that is often faced by distribution centres that hold finished good inventory is that of inventory rationing. Inventory rationing is a useful strategy to tackle the problem of conflicting objectives i.e., minimizing inventory costs (holding and backorder) on the one hand and achieving the desired customer service levels (CSLs) on the other. The focus of this paper is to formulate Generalized Stochastic Petri net models to address the inventory rationing problem in the context of multi-echelon make-to-stock distribution chains, where the goods flow through multiple echelons, typically from product manufacturers all the way up-to the retail outlets. The statistical inventory control (SIC) policies modeled by the GSPN are (R, s, S) and a variant that we propose, (R∗, s, S). We compare the performance of the model under two rationing settings. The first setting considers a case without cooperation, where the individual local stockpoints maximize their own performance. The second setting considers a case with cooperation, where the local stockpoints cooperate with each other to maximize the overall system performance. We provide a methodology to approximately determine the optimal rational fractions with different weights assigned to expected backorder and holding cost components (b/h). We present some interesting results obtained after rigorous numerical experimentation on the model.

Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems

Technological innovations in warehouse automation systems, such as Autonomous Vehicle based Storage and Retrieval System (AVS/RS), are geared towards achieving greater operational efficiency and flexibility that would be necessary in warehouses of the future. AVS/RS relies on autonomous vehicles and lifts for horizontal and vertical transfer of unit-loads respectively. To implement a new technology such as AVS/RS, the choice of a design variable setting, interactions among the design variables, and the design trade-offs need to be well understood. In particular, design decisions such as the choice of vehicle dwell-point and location of cross-aisles could significantly affect the performance of an AVS/RS. Hence, we investigate the effect of these design decisions using customized analytical models based on multi-class semi-open queuing network theory. Numerical studies suggest that the average percentage reduction in storage and retrieval transactions with appropriate choice of dwell-point is about 8 percent and 4 percent respectively. While end of aisle location of the cross-aisle is commonly used in practice, our model suggests that there exists a better cross-aisle location within a tier (about 15 percent from end of aisle); however, the cycle time benefits by choosing the optimal cross-aisle location in comparison to the end of aisle cross-aisle location is marginal. Detailed simulations also indicate that the analytical model yields fairly accurate results.

Modeling, Analysis, and Design Insights for Shuttle-Based Compact Storage Systems

Shuttle-based compact systems are new automated multi-deep unit-load storage systems with lifts that promise both low operational cost and large volume flexibility. In this paper, we develop novel queuing network models to estimate the performance of both single-tier and multi-tier shuttle-based compact systems. Each tier is modeled as a multi-class semi- open queuing network, whereas the vertical transfer is modeled using an open queue. For a multi-tier system, the models corresponding to tiers and vertical transfer are linked together using the first and second moment information of the queue departure processes. The models can handle both specialized and generic shuttles, and both continuous and discrete lifts. The accuracy of the models is validated through both simulation and a real case. Errors are acceptable for conceptualizing initial designs. Numerical studies provide new design insights. Results show that the best way to minimize expected throughput time in single-tier systems is to have a depth/width ratio around 1.25. Moreover, specialized shuttles are recommended for multi-tier systems because the higher cost of generic shuttles is not balanced by savings in reduced throughput time and equipment needs.

Estimating performance in a Robotic Mobile Fulfillment System

This paper models Robotic Mobile Fulfillment Systems and analyzes their performance. A Robotic Mobile Fulfillment System is an automated, parts-to-picker storage system where robots bring pods with products to a workstation. It is especially suited for e-commerce distribution centers with large assortments of small products, and with strong demand fluctuations. Its most important feature is the ability to automatically sort inventory and to adapt the warehouse layout in a short period of time. Queueing network models are developed for both single-line and multi-line orders, to analytically estimate maximum order throughput, average order cycle time, and robot utilization. These models can be used to quickly evaluate different warehouse layouts, or robot zoning strategies. Two main contributions are that the models include accurate driving behavior of robots and multi-line orders. The results show that: (1) the analytical models accurately estimate robot utilization, workstation utilization, and order cycle time, (2) maximum order throughput is quite insensitive to the length-to-width ratio of the storage area and (3) maximum order throughput is affected by the location of the workstations around the storage area.

A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs

Efficient handling of containers at a terminal can reduce the overall vessel sojourn times and minimise operational costs. The internal transport of containers in these terminals is performed by vehicles that share a common guide path. The throughput capacity of a terminal may increase by increasing the number of vehicles; however, simultaneously congestion may reduce the effective vehicle speed. We model this situation accurately using a traffic flow-based closed queuing network model. The vehicle internal transport is modelled using a load-dependent server that captures the interaction between the number of vehicles in a transport segment and the effective vehicle speed. Using a non-linear traffic flow model, we show that the throughput reductions due to vehicle congestion can be as large as 85%. Hence, the effect of vehicle congestion during internal transport cannot be ignored. The model can also be used to determine the appropriate number of vehicles required to achieve the required terminal throughput by explicitly considering the effect of vehicle congestion.

Minimization of Internal Shrinkage in Castings Using Synthesis of Neural Networks

New methodologies need to be evolved to minimize the shrinkages in castings and to enhance the process of direction solidification so that sound castings can be made. Existing foundry practices employed to achieve these objectives are based on the subjective judgement of the foundry experts. Present state-of-art processes followed to achieve these objectives are based on trial-and-error production, which are intolerably subjective and do not guarantee a satisfactory result. To this end, an intelligent shrinkage minimization module is required which can learn the real behavior of the solidification process so that it can perform the task of casting design feature modification in real time and intensify the process of directional solidification for a given casting. In this research, synthesis of two NN models, such as K-SOM network and BPN, are adopted to tackle the underlying problem. In the test problem considered, the NN-based model was able to minimize the shrinkages by accurately modifying the casting design features and augmenting the process of directional solidification. The proposed methodology simplifies the task of foundry designers to perform casting design modifications in a more flexible and intelligent manner.

Solving a part classification problem using simulated annealing-like hybrid algorithm

Abstract Part classification and coding is still considered as laborious and time-consuming exercise. Keeping in view, the crucial role, which it plays, in developing automated CAPP systems, the attempts have been made in this article to automate a few elements of this exercise using a shape analysis model. In this study, a 24-vector directional template is contemplated to represent the feature elements of the parts (candidate and prototype). Various transformation processes such as deformation, straightening, bypassing, insertion and deletion are embedded in the proposed simulated annealing (SA)-like hybrid algorithm to match the candidate part with their prototype. For a candidate part, searching its matching prototype from the information data is computationally expensive and requires large search space. However, the proposed SA-like hybrid algorithm for solving the part classification problem considerably minimizes the search space and ensures early convergence of the solution. The application of the proposed approach is illustrated by an example part. The proposed approach is applied for the classification of 100 candidate parts and their prototypes to demonstrate the effectiveness of the algorithm.

Blocking Effects in Warehouse Systems With Autonomous Vehicles

Autonomous vehicle-based storage and retrieval system (AVS/RS) offers considerable flexibility with respect to throughput capacity in the transfer of unit loads in high density storage areas. AVS/RS relies on autonomous vehicles to provide horizontal movement within a tier and uses lifts to provide vertical movement between tiers. In these systems, vehicle blocking delays in the aisles and cross-aisles could significantly impact system throughput and transaction cycle times. In this research, protocols are developed to address vehicle blocking, and a semi-open queueing network model is proposed to analyze system performance and evaluate design trade-offs. A decomposition-based method is used to solve the queueing network and quantify the effect of blocking. This model is adopted to analyze the effect of varying tier configuration parameters such as number of storage locations, depth/width ratio, number of vehicles, and vehicle utilization on blocking delays. These insights are useful for design conceptualization using AVS/RS.