A Real-time Simulation-Based Control Architecture for ... - CiteSeerX

A Real-time Simulation-Based Control Architecture for Supply Chain Interactions Sreeram Ramakrishnan Richard. A. Wysk Department of Industrial and Manufacturing Engineering Pennsylvania State University University Park, PA 16802, U.S.A. Abstract Discrete-event simulation techniques have been widely used for network analysis and policy optimization. In this paper, the use of high fidelity simulation models for real-time control of supply chain interactions is presented. Most formulations for supply chain problems involve optimization of an objective function consisting of various cost components (material, production, inventory, duties, taxes) subject to constraint sets (capacity, flow, inventory, duty), which treat the variables as deterministic or as well-characterized distributions. Such an approach may not be feasible to derive an active control policy. A real-time simulation-based architecture for deriving active control policies for supply chain interactions is discussed. Keywords: Simulation-based control, supply chain, control architecture

1.0

Introduction

The implementation of an efficient supply chain strategy is contingent on developing a model that represents the supply chain characteristics. This would help the trading partners understand the mechanics and processes of their own supply chain. The different decisions associated with supply chain have been classified as strategic (long-term), tactical, and operational (short-term), and can be treated as location, production, distribution, or transportations problems [1]. Supply chain models have also been classified as network design, ‘rough cut’ methods, and simulation models. The network design methods are typically normative models for strategic decisions, which focus more on the design aspect of a supply chain (network and the associated flows on them). ‘Rough cut’ methods provide guiding policies for operational decisions [1]. Multi-stage models that have been used to analyze logistics problems can be classified as [2]: (i) Deterministic analytical models, in which all the variables and known and specified, (ii) Stochastic analytical models, where at least one of the unknown variables is expressed as a probability distribution function, (iii) Economic models, which describe supply chain interactions using an economic and game-theoretic framework, and (iv) Simulation models, which are used for a broad range of reasons. Other classifications of modeling approaches have been based on solution techniques [3], level of decisions made in the model [4,5], and problem formulation techniques [6]. Figure 1 shows an IDEFΦ model to represent the various analytical and simulation models for supply chain decisions. Discrete event simulation (DES) has been used extensively for network optimization, policy optimization, identification of the causes of the uncertainties and their impact, and in the development of methods to reduce/eliminate the uncertainties. The use of DES in strategic SCM planning has been discussed in a “four-step methodology” in [7]. The role of simulation is not confined to optimization of network structures and policy decisions, but also in analysis of effects of variability – thereby providing critical input for “design for robustness”. A fifth step – simulation-based control, has been added to aid the implementation of the control policies derived from previous steps. These different levels also correspond to different levels of data abstraction. In the highest level - network optimization, the objective is to identify a few alternative structures of the supply chain in terms of the number and location of distribution centers, for example. The data requirement at this stage is “more aggregate” than that at subsequent levels. In the next level, the objective is to obtain an initial understanding of the networks’ behavior under different known demand patterns in order to select a fewer number of alternative networks that need to be analyzed further. Once a particular network structure has been decided upon, the “optimum” control policies for that network need to be identified. However, the simulation models used for policy optimization will be more detailed than those used to predict network behavior. The most data-intensive use of simulation is the context of

SCM is when simulation models are used to actively control the supply chain interactions. In this scenario, the system needs to keep track of even the “smallest” event – such as loading a part in a machine, in order to effectively control the entire supply chain. The concept of simulation-based control was successfully demonstrated in the RapidCIM architecture and its associated tools developed at The Pennsylvania State University and Texas A&M. These software tools are capable of automatically generating much of the software necessary for automating discrete manufacturing systems [8,9,10, 11, 12]. EXOGENOUS FACTORS

Shortages

I N P U T S

Demands Supplier/DC List Cost Components Objective Function

Ordering Policy EOQ (s, S) JIT Order-upto Policy

Taxes, Levies

Import and Export Issues Material Flow Control Policies Production Control Policies Warehouse Control Policies Distribution Policies Vendor Selection Production-Distribution Policies Warehouse-Customer Assignment

ANALYTICAL OR SIMULATION MODELS

Others Multi-period Single-period Push/Pull

CONTROLS

O U T P U T S

Constraints Assignment Transportation Capacity Supply Resource Availability

Figure 1. An IDEFΦ representation for supply chain models

2.0

Problem Statement and Research Objective

The underlying hypothesis of this proposed work is that the use of highly autocorrelated information (lead times, for example) is more critical than optimization routines in a dynamic domain such as that observed in supply chain interactions. In order to develop a framework to test the importance of using real-time data for deriving active control policies, an architecture consisting of high fidelity coordinated simulation models is proposed. In a purely deterministic domain, an approach involving high fidelity models is not expected to perform better or “as good” as the optimization techniques. However, the assumption of deterministic data is not a feasible one in a dynamic domain such as that seen in supply chain interactions. The stochastic models, can account for randomness for the various factors, but tend to provide only long-term average solutions since the variables are modeled as distributions with known parameters. However, there exists a strong correlation among key variables in the supply chain decisions – for example, the lead times among the echelons can be correlated. A delay in one of the echelons can result in increased delays in the other echelons and force supply chain entities to deviate from their assigned roles (shipment and production schedules, inventory, order placement, etc.). It is in this context that a federation of simulation models, which can monitor the behavior of the supply chain entities real-time and incorporate the strongly correlated variables in decision-making, is contended to be more critical than the optimization routine itself. The main research objective is to test the utility of a federation of real-time coordinated simulation models in actively controlling supply chain interactions. “Real-time” has been defined as a “response that occurs rapidly enough to react to the system without interrupting its operations [13]. This paper discusses the important features of the necessary scalable modeling architecture and the roles of the real time and look-ahead simulation models. The proposed architecture will be used to outline some of the implementation issues (case-specific) and the experimental designs used for analysis (not discussed in this paper).

3.0 System Architecture As discussed at the outset, the premise of this research is that a federation of real-time intelligent simulation-based controllers fit within a common architecture is an efficient method to account for random events occurring in a value chain. An object-oriented (appropriate to model distributed applications such as those found in a value chain), scalable, simulation-based control architecture is proposed in this paper. The proposed architecture will enable each

entity in the chain to constantly evaluate its performance with respect to its assigned roles and trigger a reassessment of the roles if necessitated by any changed observed by the entity through its simulation models. The proposed architecture can be used to analyze the behavior of the supply chain in “fast mode” as well as in its active control. Also, the architecture demonstrates the use of DES as a tool for software integration. 3.1 Components of the System Architecture Each entity in the supply chain has two simulation models associated with it – one running at real-time and the other a “look-ahead” simulation. The real-time simulation model (by definition, which runs at wall-clock speed) continuously monitors the activities of the supply chain entity. This model can, at appropriate conditions (discussed later), invoke the look-ahead simulation model associated with the same entity. The look-ahead model is capable of predicting the impact of a “disturbance” observed by the real-time model with respect to certain pre-determined performance measures for that entity. The types of “disturbances” and the performance measures are implementation-specific and have been discussed in a later section. A Federation Object Manager (FOM) coordinates the real-time simulation models of all the entities in the modeled supply chain. The real-time simulation model associated with each of the entities can invoke the federation of simulations to obtain the current information from each of the entity when it perceives that a deviation from its assigned role is imminent (change in shipping schedule, production quantity, etc.). This information can be then used to re-solve the problem (productiondistribution, for example) through traditional optimizing tools as and when required. A messaging system is used to achieve the required coordination and information transfer among the simulation models, between the FOM and the simulation models, and between the FOM and the optimization tool. Such an approach involving active control is not feasible using stochastic or deterministic analytical models discussed earlier. Moreover, it is contended that existing models cannot be used for predicting value chain interactions within a ‘reasonable’ timeframe and incorporate those results for active control. “Control” as used in this context, refers to automatic triggering of value chain interactions such as request for quotes (RFQs), purchase orders (POs), transshipment and resource allocation decisions in the ERP/MRP systems, real-time, based on the conditions perceived in any partner in a value chain. Figure 2 shows a Unified Modeling Language (UML) “class diagram” of the proposed architecture. A class diagram is a graph of “classifier” elements connected by their various static relationships and may also contain interfaces, packages, relationships, and even instances, such as objects and links. Class diagrams are meant to provide a graphic view of the static structural model [13]. In this class diagram, only the basic methods and attributes associated with the classes have been shown. The entity object “owns” three other objects – two simulation objects and one object representing the ERP/MRP system associated with that entity. The simulation models associated with each entity in the model are instances of two distinct simulation classes LookAheadSimulation” and the “RealTimeSimulation” classes. Two simulation classes associated with each of the entity help each entity determine when a discrepancy in its assigned role is imminent and invokes the federation by communicating with the FOM. The “LookAheadSimulation” object in each entity is responsible for the identification of situations where the entity will not be able to realize the transactions assigned to it from the previous optimization routine. It is responsible to alert the associated “RealTimeSimulation” whenever such a situation is anticipated. The FOM and the optimization tool are also treated as objects in the system, though in any implementation it is suffice to have only one instance of each. 3.2 Functioning of the Proposed System Architecture The real-time simulation model constantly monitors the state of the modeled entity (for example, a manufacturing shop floor, a Printed Circuit Board (PCB) assembly line, and warehouse). Since this simulation model is directly connected to the equipment controllers, it is possible to effectively monitor the current state of the system, in addition to controlling the material flow. The use of simulation models as control execution system has already been established for shop-floor control activities. The communication requirements between the required equipment level controllers, database systems, and the simulation model is similar to those in the RapidCIM project [8-12] and presented the Factory for Advanced Manufacturing Education (FAME) Virtual Information Center [14]. The proposed architecture assumes that the “RapidCIM” architecture can be implemented at the next (lower) level of

each of the real-time simulation model of each entity, and has not been discussed in this paper.

Figure 2. Representative “class diagram” For example, consider a manufacturing shop floor that has been modeled as a supply chain entity. The RT simulation model associated with this entity can incorporate different conditions (implementation-specific) at which the status of the systems needs to be verified. The real-time simulation model can be used to verify, for example, if a pre-defined number of batches are being processed in the desired time intervals to ensure that the required production rate is being maintained. This can be achieved by monitoring the number of batches being processed and crosschecking it with the production schedule for that entity. These conditions need not be based on production rates alone. Unscheduled maintenances (caused by equipment failure, tool break, etc.) can also be observed by the realtime model. In all such scenarios, as defined in the logic of each of the real-time simulation models, the look-ahead model can be invoked. The look-ahead model imports the current status of the shop floor (in this case), and then evaluates the impact of the disturbance on its assigned roles (production schedule, shipment, delaying RFQs, inventory replenishment, etc.). The result is communicated to the real-time model, which in turn decides to invoke the FOM. Based on the situation faced by the value chain (reduction in inventory in certain echelons, increase production in some facilities, change warehouse assignment, etc.) the FOM requests requisite information from the real-time simulation models. The simulation models obtain the current information from their respective MRP/ERP systems and communicate it to the FOM. This information is aggregated and then input to the optimization tool. The aggregation of the information is dependent upon the optimization tool used and the problem formulation, and is hence case-specific. The new ‘solution’ is then made available to the FOM, which communicates it to the “RealTimeSimulation” objects of each entity in the value chain. The “RealTimeSimulation” object is responsible to make necessary changes to the MRP system associated with it so as to reflect the new strategy developed by the solver. Even though, the FOM aggregates the information and invokes the optimization routines, it needs to be emphasized that the FOM can be invoked by any entity in the supply chain and that it serves as a coordination manager and not as a centralized decision maker for the value chain. The proposed architecture assumes that all modeled entities in the supply chain are willing to share information with the FOM and a centralized decision making algorithm or model is acceptable. 3.3 Coordination of Simulation Models and Messaging System The architecture helps coordinating the value chain interactions while maintaining the distributed nature of the domain. The FOM acts as a facilitator for the entities in the value chain by acting as a “router” for the information

required from each entity when the situations necessitates re-solving the value chain problem. The system is flexible since any entity can invoke the FOM as and when required. Moreover, the two simulation models associated with each entity can be used for anticipatory control (through the look-ahead simulation models) and for reacting to a random event (with the help of real-time simulation models). A messaging system has been devised to achieve the required coordination among the different entities in the architecture. A pattern of interaction among instances, as in this example, can be represented using a UML interaction diagrams - sequence diagrams and collaboration diagrams. Sequence diagrams show the explicit sequence of stimuli and are better for real-time specifications, while collaboration diagrams show the relationships among instances and for procedural design [13]. For brevity, the interaction diagrams for this architecture have not been presented here. The real-time simulation model associated with each of the entities modeled in the architecture is linked to a “lookahead” model, which can be invoked by the RT model when deemed necessary. The necessary communication links between the RT model and the look-ahead model for an entity is accomplished using “EVENT” blocks in Arena. An event block can be implemented in either Visual C++ or Visual Basic Application (VBA) embedded in Arena software. Whenever an entity in the simulation model reaches one of these blocks, the function “cevent” in the interface code is executed. Depending on the number of conditions that need to be checked in the RT model, different types of events will have to be implemented. For example, each time a batch has been processed by an equipment (or cell, assembly line), the model needs to verify that the batch is “on-time”. This is achieved using a specific “event”. Another “event” is used to detect any equipment failure and alert the look-ahead model in such an eventuality. In the ongoing implementation of the “EVENT” blocks, Visual C++ 6.0 has been used as the primary programming language. VBA in conjunction with Structured Query Language (SQL) has been used for all dataretrieval and data manipulation operations in the models. At each decision point in RT simulation, a subroutine is used to activate a fast mode simulation by sending a message to determine the impact of a particular condition observed by the RT model (delay in shipping, changed production schedule, etc.). The communication between the real-time simulation and the look-ahead manager is implemented using a Remote Procedure Call (RPC). The look-ahead manager acts as a server under the client/server environment constructed by the RPC. While implementing RPC, it needs to be noted that since two modes (real-time mode and fast mode) cannot run simultaneously on one computer, the implementation of the architecture requires that the two models be run on different computers. Moreover, multiple models, even under the same mode, cannot run simultaneously on the computer. Once the look-ahead manager receives a request, it opens the fast-mode Arena model. The look-ahead model is initialized using the current data “saved” from the real-time model, and is allowed to run for a predetermined time period. This time is implementation-specific, and no attempt to determine the optimal look-ahead period is being made in this research activity. Upon completion, the look-ahead model provides the RT model the predicted values for pre-defined performance measures (available inventory, on-time delivery, inventory turnovers, etc.). Another “EVENT” block to verify if the information given by the look-ahead model would result in a deviation from its assigned role (shipment and production schedule, etc.). The structure of all the messages that are exchanged between the two models are defined in the “MESSAGE” block and are implemented using “DELAY” blocks in the model. A windows-based router (a component of RapidCIM architecture) is currently used as the message router. Each entity is “logged” on to the router with an identification name, defined in a “default.map” for that implementation. Currently, the coordination among the real-time models is ensured by a simplistic approach of initiating all the models simultaneously using a batch file. The use of a High Level Architecture (HLA)-like architecture is also being investigated. The messaging between the real-time models and the FOM is also based on “MESSAGE” and “DELAY” blocks discussed earlier, except that the FOM is not a simulation model, but a program that has a “listener” to identify a request from the entities. The simulation models, using pre-defined message types, provide the information requested by the FOM. Separate “EVENT” blocks for the different message types will act as “listeners” for the simulation models (separate entities are created for the sole purpose of identifying any message from FOM in the model). These “EVENT” blocks and the “special” entity types serve as a sub-model in the main simulation model. The nature of the information required by the FOM is dependent on the supply chain modeled using the architecture, database design, and the optimization tool used. These interactions will be discussed in greater detail in a subsequent paper using a case study. As mentioned at the outset, the main objective of this research activity is to determine if the use of a federation of real-time simulation models can reduce supply chain operational costs compared to traditional optimization methods, which use characterized distributions for variable information or treat them as deterministic data. A case study based on the electronics manufacturing industry is being investigated in order to outline the specifics of the

simulation-based control architecture and to demonstrate the utility of the proposed architecture. The importance of obtaining real-time data from the PCB assembly line in order to make decisions on scheduling, part routing, and potential changes in process plans has already been established [15-18].

4.0

Conclusions

The need for developing a methodology that can utilize real-time data to obtain “active” control policy decisions in a supply chain was discussed in this paper. A federation of coordinated simulation models, each acting as the active controller for an entity in the supply chain, has been proposed. The basic components of the architecture, its functioning, and the role of the simulation models have been outlined. This architecture forms the basis to test the hypothesis that an active control policy derived using high-fidelity real-time simulation models can yield better results than traditional analytical models. The implementation of a case study and the experiment designs will be discussed in a subsequent paper.

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