Methodology of Flow Control Modeling - Logistics

Flow Control Modeling Methodology

Modeling as a method of cognition in logistics, as well as in other branches of knowledge, is a tool for studying any processes, objects and their systems by constructing and studying their models, and also using them to determine and refine characteristics and rationalize methods construction of newly created processes, objects. At the same time, theoretical modeling is distinguished, in which various kinds of symbolic, abstract models are used, then an experimental one, using object or process models, as well as mixed modeling using theoretical-empirical models.

Various methods of modeling have wide application in logistics, i.e. the study of logistics systems and processes by building and studying their models. At the same time, a logistic model means any image (abstract or objective) of a logistics process or logistic system used as their substitute. The main objective of modeling in logistics is the rationalization of the construction of processes and the forecast of their behavior. The key question of modeling is the implication of "What will happen if ...?".

In the methodology of modeling logistic processes, many methods and models have been developed. Characteristics, applications and types of software modeling methods in logistics are shown in Table. 6.1.

Theoretical modeling is carried out in the form of mathematical and simulation based on mathematical models and algorithms of processes.

By mathematical modeling is understood the process of establishing a certain mathematical form for a given real object, which is called a mathematical model. In logistics, two types of mathematical modeling are widely used: analytical and simulation.

Analytical modeling is a mathematical technique for investigating logistics systems that allows you to obtain exact solutions. It is carried out in the following order:

- the mathematical laws connecting the objects of the system are formulated. They are written in the form of some functional relationships (algebraic, differential, etc.);

- the equations are solved and the theoretical results are fixed;

- the obtained theoretical results are compared with practical ones in order to verify the results of theoretical studies.

The most complete study of the process of the functioning of the system can be carried out in the event that there are known explicit dependencies connecting the required characteristics with the initial conditions, parameters and variables of the system. However, such dependencies can be obtained only for relatively simple systems. With the complication of systems, their investigation by analytical methods encounters certain difficulties, which is an essential drawback of the method. In this case, in order to use the analytical method, simplify the initial model, this makes it possible to study at least the general properties of the system.

Table 6.1

Characteristics, application areas and types of simulation software in logistics

Key Features

Simulation methods

Object-oriented

Functional

(process)

Integrated

(mixed)

Characteristic features and features

Identification of object classes with the subsequent definition of actions in which objects participate. There are passive objects (materials, documents, equipment) over which actions are performed, and active objects (organizational units, specific performers, information subsystems) that perform actions

Sequential construction of the process diagram in the form of a sequence of functions with decomposition to undivided operations, the input and output of which reflect real and information objects, resources used, organizational units

It is based on the complex use of functional and object-oriented approaches. Depending on the purposes of the simulation, you can choose adequate tools for analysis and design of processes

I Controls and advantages of using in modeling of logistics processes

It is possible to use logistic systems as part of a corporate information system when designing. Increase the speed of project development; reduction of costs associated with the development of projects; reduction in operating costs of the system and its modernization

It is possible to use when solving local logistics tasks or simulating simple logistics objects.

Graphical simplicity and clarity (only two functional elements are used: a functional block and an interface arc)

The possibility of dynamic and simulation processes; successful use in modeling complex logistics facilities, including disparate regional units. Accuracy and adequacy of object reflection; a large set of graphics tools, libraries of specialized subroutines and specialized languages ​​

Software

CASE/4.0 (microTOOL); Framework (Ptech); Designer 2000 (Oracle); System Architect (Popkin); FasyCasc (Evergreen); Silverrun (CS Advisors); Prokit Workbench (Douglas Information System)

BPvvin, Erwin (Plaiinum); Design/IDEFfMeta Software);

I Think (HPS);

Visio (r) Prof. (VisioCorp.); MetaDesign (Meta Software); WorkRoute II; Process Architect (Vewstar) Key Model (Sterling Software), ARIS Easy Design (IDS prof. Scheer)

ARIS Tool-set (IDS Prof.Sheer); Workflow Analyzer for PC (Meta Software); Mocfsim (CASI); Arena (System Modeling); ProModel (ProModcl); FIX for WNT (Inteilution Inc);

ReThink + G2 (Gensym); SPARKS (Cooper * Lybrand); BDF (Texas Instruments Inc.)

Disadvantages of the method, limitations of use in the modeling of logistics processes

It takes considerable time to acquire design experience. The complexity of the methodology makes it difficult to describe the information systems of large organizations

The subjectivity of the details of operations and, as a consequence, the high laboriousness of adequate construction of processes. Description of the processes IDEFO/3 does not establish a rigid syntax framework, which can lead to the creation of incomplete or inconsistent models. The use of logistic business processes in modeling requires a high qualification of the developer both in the field of information technology and in logistics management

Focus exclusively on information technology professionals

The virtues of analytical modeling include a great power of generalization and multiple uses.

Another type of simulation is simulation simulation, which implies imitation of the characteristics of the process or their reproduction.

Logistic systems function in an environment of uncertainty. When managing flows, factors must be considered, many of which have an incidental impact. In these conditions, the creation of an analytical model that establishes clear quantitative relationships between the different components of logistical processes can be either impossible or too expensive.

In simulation simulation, the regularities that determine the nature of quantitative relationships within logistical processes remain poorly disclosed. In this regard, the logistical process remains for the experimenter "black box". The researcher sort of fits, "fakes" parameters of the simulation model, while changing the conditions of the process, and observes the result. Determining the conditions under which the result meets the requirements is the goal of working with the simulation model.

Simulation modeling involves two main processes:

- constructing a model of a real system;

- setting up experiments on this model.

The following objectives may be pursued:

- to understand the behavior of the logistics system;

- choose the strategy that ensures the most efficient functioning of the logistics system.

Here are the main conditions under which it is recommended to apply simulation:

- there is no complete mathematical formulation of this problem, or analytical methods for solving the formulated mathematical model have not yet been developed;

- Analytical models are available, but the procedures are so complex and time-consuming that simulation modeling provides a simpler way to solve the problem;

- Analytical solutions exist, but their implementation is impossible due to insufficient mathematical training of existing staff.

Thus, the main advantage of simulation is that this method can solve more complex problems. Simulation models allow us to easily take into account random effects and other factors that create difficulties in an analytical study.

Simulation simulates the process of the system's functioning in time. Moreover, the elementary phenomena that make up the process are simulated, with the preservation of their logical structure and sequence of flow in time. Models allow you to run the program with the specified parameters. Changing the parameters step by step, you can find their optimal values.

Simulation modeling has a number of significant flaws, which must also be taken into account.

1. Studies using this method are expensive, since a highly skilled programmer is required to build a model and experiment on it, a large amount of computer time is required, since the method is based on statistical tests and numerous program runs. Also it is necessary to take into account that models are developed for specific conditions and, as a rule, are not replicated.

2. The possibility of false imitation is great. Processes in logistics systems are of a probabilistic nature and can be modeled only when certain kinds of assumptions are introduced. For example, developing an imitation model of the commodity supply of the area and taking the average speed of the car on a route equal to 25 km/h, we proceed from the assumption that there should be good road conditions. However, the weather can deteriorate, and as a result of the formation of ice and "slugs" the speed on the route will drop to 10 km/h, so the process will go differently.

The presence of advantages and disadvantages of simulation modeling was noted by R. Shannon, indicating that "the development and application of simulation models are more art than science." Consequently, success or failure largely depends not on the method, but on how it is applied .

In order to implement the three basic modeling methods (see Table 6.1), the expertise methodology is often used in logistics, which is implemented with the help of expert systems.

Expert systems in logistics are understood as special computer programs that help specialists make decisions related to flow control. The expert system can accumulate the knowledge and experience of several expert experts working in different fields. The opportunity to get expert advice on various issues through access to a computer allows one to solve complex tasks in a qualified manner, improves the productivity of staff and at the same time does not require the cost of maintaining a staff of highly paid specialists. Thus the reference to the personal computer can be both individual, and collective, as at an expert method of "a brain attack".

The use of expert systems allows:

- take quick and quality solutions in the field of flow management;

- save know how company, since the personnel using the system can not take the experience and knowledge contained in the expert system out of the company;

- use the experience and knowledge of highly qualified specialists at non-prestigious, dangerous workplaces.

The shortcomings of expert systems include the limited possibility of using the "common sense". Logistic processes include many operations with a variety of goods. It is impossible to take into account all the features in the expert program. Therefore, in order not to put a box weighing 100 kg on a box weighing 5 kg, it is the common sense, supplementing the knowledge of the expert system, that a user should possess.

Expert systems are applied at various stages of the logistics process, making it easier to solve problems that require considerable experience and time. For example, in the warehouse when making a decision to replenish stocks, when the manager needs to evaluate a large amount of diverse information: the expected prices for the goods purchased, the tariffs for delivery, the need for simultaneous replenishment of stocks for different assortment items, etc. The use of expert systems here allows us to take not only correct, but also fast solutions, which is often no less important.

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