Cooperative Multiobjective Decision Support for the Paper Industry (original) (raw)
Related papers
Scheduling Solutions for the Paper Industry
Operations Research, 2002
This paper describes a decision support system for paper production scheduling. This is the first system to provide an integrated solution to paper production scheduling and to consider interactions between different stages of the manufacturing and distribution process. Using a multicriteria optimization approach, the system generates multiple enterprisewide schedules to reveal tradeoffs between the multiple, often competing, objectives. The large portfolio of algorithms used by the system is embedded in an agent-based decision support framework, called Asynchronous Team (A-Team). Successful implementations of the system in several paper mills in North America have resulted in significant savings and improved customer satisfaction.
A decision‐support system for scheduling in a customer‐oriented manufacturing environment
Integrated Manufacturing Systems, 1996
An intelligent decision‐support system was designed for assessing schedulability prior to assigning parts for scheduling. This was designed as part of a larger system for intelligent, real‐time control of a manufacturing system operation, where one of the system objectives was just‐in‐time delivery of production output. The manufacturing system was a conventional cellular manufacturing system where machines were assumed occasionally to fail. All necessary requirements for the processing of a job should actually or anticipatively be satisfied before a job is scheduled. The decision‐support system attempts to satisfy these requirements. The system thus helps the decision maker to make the right decision when system and customer constraints are violated. Illustrates the operation of the system through an example.
A Scheduling and Rescheduling Decision Support System for Apparel Manufacturing
International Journal of Operations Research and Information Systems, 2021
The manufacturing environment for apparel is subject to a variety of constraints, stochasticity, and unforeseen events. In order to create an accurate scheduling-system for this environment, these complexities must be considered. This article presents the development and the application of a scheduling and rescheduling decision support system for an apparel manufacturer. Furthermore, the results of applying the proposed system are presented and discussed. The scheduling and rescheduling decision support system presented in this article takes advantage of a variable neighborhood search and Monte Carlo simulation in order to minimize tardiness in the presence of different release times, sequence-based setup times, blocking, and resource constraints. The results show that the quality of the schedules generated by the proposed scheduling and rescheduling decision support system is superior to the current firm’s scheduling practice, which is based on an earliest due date heuristic. Moreo...
PLANET: An Intelligent Decision Support System for Resource Planning in Manufacturing Organizations
1985
This paper describes a problem solver called PLANET that has been developed in collaboration with a large computer manufacturing company to assist planning managers with the formulation and maintenance of planning models for resource allocation. PLANET is equipped with the primitives that enable it to preserve much of the richness of the process of the planning activity, namely, the generation of symbolic alternatives, and for the expression of domain specific knowledge which enables it to synthesize these alternatives into an overall planning model. This knowledge is maintained in a llmeta-model.w In contrast to modeling systems which allow for parametric perturbations of an algebraic model, PLANET1s meta-model provides it with the capability for systematic variations in the symbolic model assumptions, with concomitant structural variations induced in the algebraic model that reflect the interdependencies of those assumptions. Whenever previously held assumptions change, PLANET uses the existing model as a point of departure in formulating the revised plan. In this way, the program is able to take cognizance of the ongoing nature of organizational problem solving, and can serve an important decision support function in maintaining and reasoning about evolving plans. Center for Digital Economy Research Stem School of Business IVorking Paper IS-85-24 ''A good human decision support staff has two jobs to do. First it must reduce the set of all possible actions to the few that look potentially realistic, feasible, and good. It is this small handful that the top level decision maker actually considers when he reaches his final decision. Second, both in winnowing through the alternatives, and in projecting their consequences, the staff somehow must deal directly with the interrelations among the various parties involved. This is the only way it can hope to apply its knowledge about the parties, their goals, their resources, and the constraints under which they must operate. In general, however, we simply do not yet know how to incorporate such knowledge in numerical projection models. As a result, there is a real ceiling to what we can expect of decision support systems cast in current molds." (Reitman, 1981). Perhaps a more serious limitation of existing computer-based systems is their inability to take cognizance of the ongoing, evolutiona_lly: nature of organizational problem solving, that is, to preserve and reason about previous decisions and changes to them-something that is an integral part of a manager's job. If we pose Reitman's question again, we realize that many good alternatives a > in fact generated or synthesized in the course of formulating a plan. However, only a small subset of these become part of the "finalff plan and reflected in the algebraic model that is derived from it. Unfortunately, much of the knowledge about issues and choices that were available, and the rationales for choosing or rejecting alternatives end up in filing cabinets or voluminous reports, often permanently. This is not altogether surprising. Given the effort involved in formulating the plan in the first place, and the difficulty of coordinating the diverse inputs from the various parties involved, a systematic assessment of the ramifications of changes can become overwhelming. Yet, in the absence of this knowledge, the existing algebraic model provided by a modeling system can have the effect of unnecessarily confining users to its limited view of an inherently flexible situation. For such problems, the real decision support needed is not in helping fine tune an existing model, but one of exposing a decision maker to the multiple perspectives brought about by changes in assumptions, and of interactively assisting in the reformulation model of the situation.
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.
Integration of planning and scheduling in multi-site plants: Application to paper manufacturing
2005
In this paper, a general multi-level decomposition based framework has been proposed for integration of planning and scheduling in a multi-site, multi-product plant, with applications to paper manufacturing. The problem involves complex issues relating to large-scale production in a hybrid flowshop configuration, decisions relating to minimizing trim losses, while maintaining on-time delivery of orders. Due to these complexities, the overall problem of integrated planning and scheduling is logically partitioned into several levels, depending on the problem size. As followed in other decomposition-based approaches, the upper level models are equipped with appropriate abstractions of the lower level constraints. Also from a reactive scheduling point of view, some pro-active measures are embedded into the multi-level structure. The proposed multi-level decomposition scheme is demonstrated on a representative planning and scheduling problem.
2018
As companies increasingly customize their products, move towards smaller lot production and experiment with more flexible customer/supplier arrangements, they increasingly require the ability to respond quickly, accurately and competitively to customer requests for bids on new products and efficiently work out supplier/subcontractor arrangements for these new products. This in turn requires the ability to rapidly convert standard-based product specifications into process plans and quickly integrate new orders with their process plans into existing production schedules across the supply chain. This paper describes IP3S, a blackboard-based agent for supporting integrated process planning/production scheduling across the supply chain. IP3S agents support concurrent development and dynamic revision of integrated process-planning/productionscheduling solutions across the supply chain, maintenance of multiple problem instances and solutions across the supply chain, flexible user-oriented ...
Flexibility of scheduling tools for order production problems
Computer Integrated Manufacturing Systems, 1993
A fundamental issue in scheduling tools for order production systems is the trade-off between the feasibility of a solution with respect to a set of dynamic local constraints and the evaluation of a schedule with respect to different global optimality criteria. CIM managers clamour for more flexible and easy to handle tools than those provided by classical scheduling theory. Decision Support Systems (DSS) have been proved to be valuable tools for manipulating schedules and for managing information on the production process. This paper surveys previous work on scheduling in production systems, and presents a methodology for facing production scheduling problems. New design criteria and interactive models for effectively supporting the methodology in flexible and dynamic production environments are discussed.
A decision support system for operational production scheduling
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.