Shop floor design:layout, investments, cross-training, and labor allocation

Een wereldwijde competitie en steeds complexere producten, die sneller gefabriceerd moeten worden, met meer vernieuwing en grotere afwisseling. Dat zijn de uitdagingen waarmee productiebedijven geconfronteerd worden. In de metaalindustrie heeft de CNC-technologie (computer numerical control) zijn waarde in deze struggle for life bewezen. CNC-machines worden daarom steeds vaker gebruikt, ook door kleinere firma’s. Het onderzoek van Jos Bokhorst gaat over alle aspecten die daarbij komen kijken, van de te nemen investeringsbeslissingen tot het ontwerp van de werkplaats, trainingen en de organisatie van de werkzaamheden

A contribution towards a methodology for managing learning, multi-skilling and performance on the shop floor

High-performance work is applied in production methodologies like lean production. In this approach, workers are able to perform several different tasks and participate in continuous improvement. In order to be able to do this, workers must obtain practical knowledge of the tasks and acquire a global vision of the process. Work plans must take into account workers’ performance when they begin working on new tasks, the cost of learning new tasks in terms of training or low performance, the effects of previous knowledge and personal absorption capacity, and a final knowledge objective. We propose a task-assignment model that takes these factors into account. A particular version of the model is defined in detail. Several instances based in a real case are solved using optimization software. This proves that the model is affordable for moderate-sized problems. The information that can be obtained using this method is presented. These results form the basis for an applicable methodology to be developed in future research.Peer ReviewedPostprint (published version

Knowledge Brief: Rural Workforce Development Strategies

Rural ResilienceRegional Workforce Developmen

Implementation of Cross-training for Multi-shift Worker Allocation

In workforce allocation, gaps between workers available and workers needed at various operations result in production delays and a loss of profitability for the manufacturer. These gaps can be reduced by overtime assignments of workers from other shifts. However, for a multiple shift planning horizon, a mix of cross-training of workers over different tasks along with overtime assignments may be a good strategy. This work develops an industry-motivated cross-training framework that identifies workers and operations for normal, overtime, and training assignments. A mixed integer programming model that integrates all three assignment tasks is formulated and solved. The production scenario consists of skill level based qualifications for workers that need to be assigned to operations in every shift. Factory floor conditions such as limits on worker levels at specific operations, scheduling restrictions and worker training protocols are also considered. The data taken into account includes parameters such as man-machine ratio, tool count, and limits on skill qualifications for workers. The output of the model provides a cross-training schedule and an assignment schedule that can be used by floor managers on a shift-by-shift basis. The MIP model is implemented in C# using the .NET framework and the IBM ILOG CPLEX Optimizer

A STUDY OF QUEUING THEORY IN LOW TO HIGH REWORK ENVIRONMENTS WITH PROCESS AVAILABILITY

In manufacturing systems subject to machine and operator resource constraints the effects of rework can be profound. High levels of rework burden the resources unnecessarily and as the utilization of these resources increases the expected queuing time of work in process increases exponentially. Queuing models can help managers to understand and control the effects of rework, but often this tool is overlooked in part because of concerns over accuracy in complex environments and/or the need for limiting assumptions. One aim of this work is to increase understanding of system variables on the accuracy of simple queuing models. A queuing model is proposed that combines G/G/1 modeling techniques for rework with effective processing time techniques for machine availability and the accuracy of this model is tested under varying levels of rework, external arrival variability, and machine availability. Results show that the model performs best under exponential arrival patterns and can perform well even under high rework conditions. Generalizations are made with regards to the use of this tool for allocation of jobs to specific workers and/or machines based on known rework rates with the ultimate aim of queue time minimization

Development of Manufacturing Cells Using an Artificial Ant-Based Algorithm with Different Similarity Coefficients

Although there exists several ways of solving the cellular manufacturing problem, including several ant-based algorithms, many of these algorithms focus on obtaining the best possible answer instead of efficiency. An existing artificial-ant based algorithm AntClass, was modified so that it is easier to manipulate. AntClass uses Euclidean vectors to measure the similarity between parts, because similarity is used to group parts together instead of distances, the modified version uses similarity coefficients. The concept of heaping clusters was also introduced to ant algorithms for cellular manufacturing. Instead of using Euclidean vectors to measure the distance to the center of a heap, as in the AntClass algorithm, an average similarity was introduced to measure the similarity between a part and a heap. The algorithm was tested on five common similarity coefficients to determine the similarity coefficient which gives the better quality solution and the most efficient process