ارتباط بین طبقات سنتز اورژانس: کنترل مبتنی بر موعد مقرر و برنامه ریزی تولید کارگاهی

Emergent synthesis classifies problems of artifactual system behavior into three classes depending on the completeness in the description of the system environment and specifications. This paper introduces correlations between the problem classes and their problem solvers. To illustrate the discussed correlations, a job shop model with make-to-order manufacturing environment is presented. The problem frame of the control and planning in the model is shown to be a Class III type problem and approached by using the correlated problem solvers of the three classes. The purpose of the job shop is to evaluate the overlapped space between the specifications of the customers and the capabilities of the manufacturing system and to form the behavior of the system in order to fulfill orders with high accuracy. The structure of the model and the developed solvers indicate that to solve a Class III type problem, various Class I and Class II problem solvers are relevant.

Emergent synthesis offers a great methodology to handle and resolve complexity in artifactual systems. Harmonizing top-down and bottom-up features in forming the behavior of the system, the approach provides efficient, robust and adaptive solutions to the problem of synthesis [1]. In emergent synthesis related solutions the global behavior of the system is dynamically formed bottom-up through locally inspired interactions between the artifacts attempting to achieve the purpose of the whole system. To verify the emerging global order, top-down features are introduced that are able to modify the order by rendering the global purpose to the artifacts top-down. With taking into account the local and global goals, the artifacts build up their emerging behavior in order to accurately achieve the purpose of the whole system. Emergent synthesis introduces three types of problem classes and their emergent related problem solvers depending on whether completeness of information could be achieved in the description of the system environment and the specifications of the system. In Class I type problems full completeness can be achieved in both the description of the environment and the specifications. Although all constraints to be taken into account are known, to find a solution satisfying all the constraints leads to combinatorial explosion. Therefore, emergent related methods that can handle combinatorial explosion are implemented in this class. The problem solvers are evolutionary computation methods such as genetic algorithms and evolutionary programming. In Class II type problems the description of the specifications is complete, but the description of the environment is incomplete. The proposition of the system is to cope with the dynamic properties of the unknown environment. To deal with this problem, the environmental constraints have to be determined through being in interaction with the environment. Learning and adaptation based approaches such as reinforcement learning and adaptive behavior based methods are feasible to this class of problems. In Class III types withal the incomplete environmental descriptions, the description of the specifications is also incomplete. Besides ascertaining the dynamic environmental constraints, this class has to cope with the iterative determination of the system structure. Emergent properties, such as interactivity, self-coordination, co-evolution and self-reference are essential in this class. In this paper correlations between emergent synthesis classes are under examination. One correlation can be seen between Class II and Class III as the implementation of the Class II approaches to handle the unknown environmental changes in Class III type problems. In the aspect of the presented research work it is necessary to establish further correlations between Class I, Class II and Class III classes to solve complex problems in artifactual systems. The paper first draws a schematic functional description of emergent synthesis classes and their correlations. Although it is an oversimplified model, it shows, in the same manner, the examined correlations between the classes. Literature review follows the schematic model to support the necessity of establishing the correlations. After the review, a Class III type problem is described as the due date based control and planning of a job shop model with make-to-order manufacturing environment. The developed problem solvers and system structure illustrate that to solve a Class III type problem, various Class I and Class II problem solvers and their synthesis are relevant.

In this paper, correlations between the problem classes of emergent synthesis have been introduced. Based on this approach to the Class III type problems, the authors developed a job shop type system model in one of a kind make-to-order manufacturing environment. The aim of the model is to adapt to the dynamic environment and changing specifications, representing a Class III problem. The production planning of the model solves a Class I type problem using a genetic algorithm based optimum search method. Before running the search method the environmental constraints and the constraints of the specification are determined by the job shop control and the management. The job shop control using an adaptive method to estimate due date tightness of incoming jobs is the Class II problem solver of the model. The management interacting with customers to form the deal of the due date and cost can evaluate the appropriate weights for the machining cost and lead-time minimization so that the system can adapt to different market environments. The synthesis of the behavior of the production planning, job shop control and management deals with the Class III type problem of the system. Simulation results with a random job shop show that whilst the environment is dynamic and the specifications are changing during the simulation runs, the system performance for on time delivery of products, one of the main criteria in make-to-order manufacturing environment is accurate. The management is able to control the range of time and cost constraints of the system by evaluating the operation policies. Emergent synthesis of top-down and bottom-up features are harmonized in the model to achieve the global purposes of the whole system. Future work will focus on implementing a reinforcement learning algorithm (Class II type solver) in the management to create long term operation policies of the system in different market environments.