Modelling production scheduling systems

2019

Integration in decision making at different organizational and time levels has important implications for increasing the profitability of organizations. Among the important issues of medium-term decision-making in factories, are production planning problems that seek to determine the quantities of products produced in the medium term and the allocation of corporate resources. Furthermore, at short-term, jobs scheduling and timely delivery of orders is one of the vital decision-making issues in each workshop. In this paper, the production planning and scheduling problem in a factory in the north of Iran is considered as a case study. The factory produces cans and bins in different types with ten production lines. Therefore, a mixed integer linear programming (MILP) model is presented for the integrated production planning and scheduling problem to maximize profit. The proposed model is implemented in the GAMS software with the collected data from the real environment, and the optimal...

Planning and scheduling attracts an unceasing attention of computer science community. However, despite of similar character of both tasks, in most current systems planning and scheduling problems are usually solved independently using different methods. Recent development of Constraint Programming brings a new breeze to these areas. It allows using the same techniques for modelling planning and scheduling problems as well as exploiting successful methods developed in Artificial Intelligence and Operations Research. Currently, scheduling is the most successful application area of constraint programming. In the paper we analyse the problems behind planning and scheduling in complex production environments. We give a survey of three conceptual models developed to model such environments. We discuss their industrial background and compare their advantages and disadvantages. The models were studied within the VisOpt project whose goal is to developed a generic scheduling engine applicable to various complex production environments. However the proposed conceptual models can be applied to other (non-production) problem areas where joined scheduling and planning capabilities are required.

XPS-99: Knowledge-Based Systems. Survey and …, 1999

Production scheduling plays a key enabling technique in improving the efficiency of modern production enterprises facing the international competition. Although the trend of software development for business application demands standard software, there exist no standard scheduling software package that can be applied to these applications. In this paper a special architecture based on object-oriented application frameworks and AI-reasoning methods is proposed as a mean to narrow this gap.

AI Magazine, 1986

the constraint knowledge utilized in actual factory environments. This led to the construction of the Intelligent Scheduling and Information System (ISIS), a series of experimental job shop scheduling systems (Fox, Allen, and Strohm 1982; Smith 1984a, 1984b). ISIS-2 was demonstrated in the context of the Westinghouse Turbine Components Plant (WTCP) in Winston-Salem, North Carolina. More recently, we initiated work on a new system called OPIS , which continues to generalize from the ISIS experience and provide greater system flexibility in approaching various scheduling tasks. We feel we have made significant progress in this work, but there are difficult issues that remain to be addressed. This article attempts to coalesce what we have learned thus far and to reflect on the potential of knowledge-based approaches to this difficult problem. Abstract To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevant knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two Bnowledge-based factory scheduling systems that implement aspects of this approach are described.

IFAC Proceedings Volumes, 1989

This paper presents the outline of an environment for the specification and prototyping of manufacturing systems software, which is under development in the framework of a research project sponsored by Italian National Research Council. The environment is heavily influenced, both at the user and at the implementation level, by the object oriented programming paradigm, which is the basis to propose a unified view to represent the hierarchical structure of commonly used reference models. The results are a library of classes to express concurrency and to support an extended client server paradigm, and a new design methodology. Scheduling is another key issue in the prototyping of manufacturing systems , and best results are obtained with knowledge based, heuristic approaches : the object oriented prograrruning paradigm allows to smoothly integrate an expert systems development tool and a simulation environment.

Computers in Industry, 2001

This research introduces an intelligent approach for identifying the optimal production schedule to satisfy product and manufacturing constraints. In this approach, product constraints are modeled using a feature-based product representation scheme. Manufacturing constraints are described as available resources including facilities and persons. Manufacturing requirements for producing the products, including tasks and sequential constraints for conducting these tasks, are represented as part of the product feature descriptions. The optimal production process and its timing parameter values are identi®ed using constraint-based search and agent-based collaboration. The intelligent optimal production scheduling system was implemented using Smalltalk, an object oriented programming language.

Journal of Intelligent Manufacturing, 1997

Tool specification for production system control requires the identification of the main characteristics of the production system. The production systems are varied, and the production control tools (such as scheduling algorithms and resource assignment rules) are often dedicated to only one class of these systems. Tool assessment for production system control requires modelling of the production system. We propose a

Resource scheduling is a difficult and time consuming problem, especially when human resources are involved. The major reason of this difficulty emerges from the complexity and the dynamically changing nature of the regulations that restrict the solutions of problem. Constraint Logic Programming (CLP) is currently considered as the most powerful computational paradigm for stating and solving such problems. However, the statement of complex regulations is still extremely hard and results in a large semantic gap between the implied formalization and the problem regulations, as perceived by the end-user. Ideally, the end-user should be able to represent and modify directly the regulations, but since a high level formalism is not available, the interference of an expert programmer is always required. In this paper, we suggest an Object-Oriented meta-representation for the abstract, natural specification of regulations. With the target of obtaining an efficient scheduling system, we describe the relationship between our formalism and the CLP one, laying the foundation for automatic transformation between the two representations.

European Journal of Operational Research, 1991

In this paper we describe a Decision Support System for dynamic job shop scheduling environments. The supported decision situation includes several real-life features which are disregarded by conventional scheduling models. The system supports the planner in constructing efficient and effective schedules. The efficiency is guaranteed by algorithmic procedures and the effectiveness by interactive manipulation techniques. The due date achievement is the objective of the algorithmic support, but other objectives can be achieved by means of the control functions which allow the user to direct the decision process.