Martin Fleetwood

Operational scheduling for rough mills using a virtual manufacturing environment

Production scheduling in wood product plants, such as furniture factories, is highly complex due to its sensitivity to defects occurring in wood. Due to the huge number of choices and uncertainty of the outcome an optimum schedule cannot be computed mathematically. These choices, with complex interdependencies, have to be made on an hourly basis at different machines and must be coordinated to achieve the overall production,objectives. A decision support tool, using a 3D simulation model, is provided to predict the production performance of the null under various operational schedules, before a selected schedule is implemented. A 3D simulation model was developed accurately depicting the layout and operation of a rough mill. A historical database, some of it maintained via human input and some of it collected automatically, was analyzed to provide the basis for predicting the behavior of the mill when processing different type of raw materials and when producing different combination of products. The validated model, which links to the scheduling database (i.e., raw material available, products required) is used to test-fly alternate production schedules as often as required, thus identifying potential production problems before they occur in the mill. The next step will be to incorporate intelligent scheduling agents using techniques, such as fuzzy logic and genetic algorithms, to provide scheduling recommendations on individual machines. In the future these will be coordinated in a holonic manufacturing execution system to achieve better global performance. Performance measurement will require assessment based upon multiple criteria (e.g., productivity, recovery, grade utilization, and satisfaction of order files).

Holonic system architecture for scalable infrastructures

In a previous paper Gruver, Kotak, van Leeuwen and Norrie proposed a Holonic Systems Architecture for manufacturing co-ordination. In another project Daimler-Chrysler demonstrated that a holonic approach resulted in significant improvements in productivity and robustness of their engine assembly line design and the technique was found to be scalable and could be implemented in a stepwise fashion. Presently NRC is examining the ways of evolving a hydrogen infrastructure for fuelling hydrogen in fuel cell vehicles. Such an infrastructure will need to be scalable, reliable and cost competitive. Therefore we chose to use the holonic approach for modelling and simulation of such an infrastructure, which presently does not exist and will initially be sparse and highly distributed. This paper describes the original design features of the holonic system architecture, the hydrogen infrastructure requirements and the enhancements we made to the architecture in the process of implementing the test application. We used a 3-D simulation technology to create a virtual hydrogen infrastructure, created holons to proxy its elements (i.e., hydrogen generation, storage and dispensing stations, vehicles and highways with alternate routes) and the interface between the virtual and holonic worlds. We systematically introduce holons which proxy real physical devices, to demonstrate how this architecture could be phased in from a virtual model to the physical world without changing the holonic control.

Rough mill component scheduling: heuristic search versus genetic algorithms

Rough mill production systems cut lumber into smaller components needed to produce wood products. Because of the systems limited sorting capacity, rough mill operators need to schedule when different component sizes are made, a process called part scheduling and replacement. This scheduling process is significant because it greatly affects system performance. Three component scheduling algorithms are examined in this paper: a heuristic method that mimics how human operators manually schedule components; and two methods based on genetic algorithms, the simple genetic algorithm and the ordering messy genetic algorithm. The performance of the algorithms is analyzed and tested on four cutting bills. Results show that the ordering messy genetic algorithm outperformed the simple genetic algorithm, and heuristic component replacement performed better than replacement based on the genetic algorithms objective function. Also, heuristic cut-list selection performed better on cutting bills with more short pieces, whereas GA cut-list selection performed better on bills it with longer pieces.

A distributed decision support system for lumber jag selection in a rough mill

A rough mill production system is an unpredictable dynamic system for which the effects on production of many factors are strongly interrelated. A distributed decision support system for dynamic jag (a load of lumber) selection in defect sensitive production is proposed. Case-Based Reasoning and heuristic rules determine recommended jags for cut-lists in the decision process. Using a statistical method, cases are grouped by categorizing problems and solutions. Heuristic rules measure the similarity between current and past cases. The framework for a decision support system has two layers in which jag types are selected at the top level and specific jags are chosen on the bottom level. Decisions from different sources influence both top level and bottom level. The dynamic character of the system is taken into account by collaboration between distributed decision points that can change indices by retrieving previous cases and storing new cases. The recommended jags are tested by simulation and used for feedback to the decision support system. Finally, the system is validated by comparison with actual production.

Reconfiguring processes in a holonic sawmill

With over 40% of landmass covered by forest, lumber is arguably Canadas most plentiful natural material. The processing of lumber in sawmills is receiving increased attention as the requirement to optimize how the wood is cut becomes more prevalent. One approach to automating this processing is to utilize a Holonic Manufacturing System (HMS) as it provides an array of techniques to improve flexibility, fault tolerance and so forth This paper discusses the processes within such a holonic sawmill, and how they can be reconfigured.

Distributed data acquisition and monitoring system for an integrated energy application

This paper describes a distributed data acquisition and monitoring system using a multi-agent framework for a demonstration project for integrated energy application implemented in the National Research Council Canada, Institute for Fuel Cell Innovation. The physical energy system includes three major components: renewable energy source - a building integrated photovoltaic system; a hydrogen generator - an electrolyser and a hydrogen storage system. A distributed data acquisition and monitoring system based on our previously developed agent system framework is implemented for remotely acquiring real-time parameters from the individual devices and real-time monitoring the system running status. As the first step to approach the distributed cooperation and coordination among different technologies in an integrated energy application, this implementation of a distributed data acquisition and monitoring system paves the way for its further deployment in the distributed coordination and control for each individual device to further make the entire system more scalable and robust.

A fuzzy multiple decision support for jag selection

A rough mill is a manufacturing facility where lumber is processed into components to be used in assembling wood products such as windows and doors. A cut-list is a list of components of specific sizes and quantities, which are scheduled to processed at one time. Selection of suitable jags (loads if lumber) from candidates for a given cut-list is a complex decision due to the imprecision in the description of jags and cut-lists. Four criteria that determine a good jag are yield, cost, percentage of order satisfied, and processing time. The importance of each criterion depends on the cut-list that is being processed. In this paper, a fuzzy multiple criteria decision making (MCDM) approach for jag selection is utilized as a decision support system (DSS) for the decision maker (DM). Fuzzy sets are defined to describe the cut-list, and fuzzy rules are constructed to set the pairwise weights between decision criteria. The fuzzy weights for multiple criteria are determined using a fuzzy analytic hierarchy process (AHP). Three cut-lists are tested in the decision support system. The generated fuzzy weights reflect the requirement of cut-lists, and proper jags are selected using fuzzy technique for order preference by similarity to ideal solution (TOPSIS).

An intelligent decision-support system for rough mills

A rough mill is where wood components are cut from lumber to produce wooden doors and windows. Because lumber is a natural material it contains various types of defects (e.g. knots and splits) but their distribution is not known in advance. Furthermore, only the approximate content and dimension of each board in a load are known ahead of time. Thus, producing required components from different types of lumber in a rough mill is quite a complex challenge. Several operations that occur in the rough mill are closely related and are analysed in this paper. An Intelligent Decision-Support System (IDSS) is proposed to improve the overall performance of the rough mill by presenting recommendations to operators. A rough mill simulator helps operators view the effect of these recommendations before implementing them on the production floor. Two key challenges for rough mills are identified, namely: selection of appropriate jags and cut-list scheduling and alternative solutions are proposed.

Identifying Aggregation Weights of Decision Criteria: Application of Fuzzy Systems to Wood Product Manufacturing

A rough mill converts lumber into components with prescribed dimensions. The manufacturing process begins with lumber being transferred from the warehouse to the rough mill where its length and width are determined by a scanner. Then, each board is cut longitudinally by the rip saw to produce strips of prescribed widths. Next, the strips are conveyed to another scanner where defects are detected. Finally, the chop saw cuts the strips into pieces with lengths based on the defect information and component requirements. These pieces are conveyed to pneumatic kickers which sort them into bins. The rough mill layout and process of a major Canadian window manufacturer is used throughout this study. The rough mill layout and process, as described by Kotak, et al. [11], is shown in Fig. 1. The operator receives an order in which due dates and required quantities of components with specific dimensions and qualities are listed. Since there is a limited number of sorting bins (which is much less than the number of components in the order), the operator must select a subset of the order, called cut list, and assign it to the kickers. After selecting the cut list, the loads of lumber (called jags) that will be used to produce the components in the cut list must be selected by the operator. The operator also determines the arbor configuration and priority of the rip saw. Methods for kicker assignment have been reported by Siu, et al. [17] and jag selection has been investigated by Wang, et al. [22], [23]. A discrete event simulation model was developed by Kotak, et al. [11] to simulate the daily operation of a rough mill for specified jags, cut lists, ripsaw configuration, and orders. Wang, et al. [22] proposed a two-step method to select the most suitable jag for a given cut list. The first step selects the best jag type based on the proximity of the length distribution of the cut list and the historical length distribution of the

Jag sequencing in rough mill operations

Raw material cost is one of the major contributors to the overall cost in rough mill operations. The challenge is to choose raw materials that can fulfil a given order in a reasonable time. However, the objective of minimizing raw material cost conflicts with the objective of minimizing the processing time. This study investigates the use of a local search mechanism to find the best jag sequence for a given order. Simulation is used to evaluate the performance of each jag sequence candidate with respect to the objective function. Since the proposed method is intended for real-time production, beam search is utilized. Numerical results for a sample order list show 22% cost reduction.