Integrated Supervisory and Operational Control of a Warehouse With a Matrix-Based Approach

Abstract

Abstract-This paper considers a matrix-based discrete event control approach for a warehouse. The control system is organized in two modules: a dynamic model and a controller. The model provides a complete description of the discrete event dynamics of the warehouse, and is used as a means to track the stock-keeping units, and identify and inhibit control actions that violate system's constraints. The controller has several functions. At the supervisory level, it is in charge of inhibiting operations that may lead to deadlocks, commanding the actual start of the task, and the release of the resources once a task is completed. At the operational level, it is in charge of performing decisions regarding the order in which allowable tasks waiting for service should be performed. All the modules are implemented using the same matrix-based formalism, and thus integrated with each other. The main advantages of the approach are the inherent modularity (the matrix-based control is obtained by assembling individual atomic components), and the integration between the various modules, which permits a better overall resource utilization. Simulation examples describing an actual industrial warehouse are finally provided to emphasize the main advantages of the proposed approach.Note to Practitioners-This paper illustrates the application of a discrete-event modeling and control framework based on a matrix notation to an automated warehouse. The material handling equipment must continuously transfer stock-keeping units between various buffers using a network of interconnected rail guided vehicles. Since transfer requests are issued at random times, the role of the control system is extremely challenging, as it must handle low level tasks (synchronizing hardware, avoiding conflicts) and high level ones (allocating the rail guided vehicles efficiently to avoid congestions and long service times). Giving a complete mathematical characterization of such a system is challenging, due to its inherently turbulent and discrete-event nature. The proposed original approach allows one to attack the problem from a modular, divide-and-conquer perspective. In fact, the complete model of the system is achieved by a sequence of simple modeling steps, starting from the description of the atomic components of the system, and progressively specifying constraints, objectives and control rules to obtain a complete characterization of the entire system within a unified formalism based on binary matrices. Since the final model integrates supervisory and operational functions, it permits to perform the allocation of rail guided vehicles more effectively, using in- formation that is generally unavailable to the dispatching modules. Examples and numerical results based on the equipment actually operating in the warehouse are also provided to illustrate the potential of the approach.

LQ optimal and reaching law-based sliding modes for inventory management systems

“…However, the technological advances used to facilitate the flow of information in productioninventory systems, e.g. Internet ordering (Doukidis, Pramatari, and Lekakos 2008) and automatic warehouse reviewing (Giordano, Zhang, Naso, and Lewis 2008), call for new solutions that will not only provide good steady-state performance, but will also guarantee prompt response to the changing market conditions (Ortega and Lin 2004). The recognised need for analysing the dynamical properties of productioninventory systems has resulted in a number of research efforts exploiting the tools of control theory (White 1999;Samanta and Al-Araimi 2001;Rodrigues and Boukas 2006;Wang, Rivera, and Kempf 2007;Aggelogiannaki and Sarimveis 2008).…”

Optimization of Total Energy Consumption in Flexible Manufacturing Systems Using Weighted P-Timed Petri Nets and Dynamic Programming

“…In our application, a PN-based DAP that ensures maximal work in progress is used [1], [25]- [28]. Let and be the set of circular waits in an FMS and the state vector of the resources in the critical subsystem of , respectively.…”

A review of predictive monitoring approaches and algorithms for material handling systems

“…According to [2], material handling can represent up to 70% of the costs associated with making a product. For such a fleet of machines, having an integrated supervisory system, proper operational control and asset management systems can optimize the costs and productivity of MHS.…”

“…Optimization of throughput [2], cycle time [2], output [33], and design [33,34], fault diagnosis of production systems [35] Warehouse [2,34], distribution center [33], food processing and packaging [36], pneumatic production system [35] Discrete event simulation [2], real data from a distribution center [33], simulation modular production system [mps] [35] Divide and conquer modeling approach and matrix-based method [2], heuristic decisionmaking rules [33], adjacency matrix, graph theory, optimization, decision support systems [34], Support Vector Machine [SVM] and Decision tree [35] Shorter distance and faster time compared to conventional control strategies [2], improvement in automation and throughput [33], 11 faults successfully diagnosed [35] the failure analysis of four components of a conveyor system based on a historical data collected from mining companies. The components included the drive unit, pulleys, idlers, and belts.…”