Current planning and scheduling techniques ignore the requirements of activities for work area or space. Any task or activity requires a specific work space for its execution. This demand is based on the space requirements of each resource allocated to the activity. When such required demand becomes unavailable, the activity or task can not be executed or, in some cases, is performed with a lower productivity rate. This is because performance and maneuvering of either crew or equipment may become difficult and sometimes not possible. This research provides a structured methodology to deal with the problem of limited work space availability. The research’s domain focuses on multi-story building construction with particular focus on the repetitive portion of the facility (i.e. typical floors). The research studies the issues of work space as a new decision factor for schedule generation in this type of construction. A scheduling model is developed to define and incorporate work space availability in the scheduling process. The model includes a method to quantify work space parameters (space demand and space availability) for any activity. The model also presents a procedure to compare space demand to availability and provide scheduling decisions to Sequence each activity based on these two parameters. The model allows for availability limits to be placed on resources required for the work by implementing limited resource scheduling techniques in the scheduling procedures. Horizontal and vertical logic constraints associated with repetitive work are also incorporated in the scheduling process of the model. Work continuity issues and varying productivity rates are used as scheduling decision options. The model adopts a procedure to schedule non-continuous activities using variable length segments along the typical floors. In addition, the model allows for adjusting the initial defined resource demand pools for different activities to account for any modifications that may occur to the activity duration during scheduling. Loss of productivity as a result of the combined effect of travel time and learning curve phenomena is also incorporated in the generation of the schedule by the model. The model is taken to a prototype proof of concept by developing SCaRC (Space Constrained and Resource Constrained) scheduling system. The system is implemented using a knowledge-based approach. / Ph. D.

Two types of heuristic capacity control policies a·re evaluated with a SLAM simulation model of a materials requirements planning production system. The control policy decisions are based solely on the size of the queue, as measured in standard hours of work, at each work center in the production system. Several classes of product mixes and product structures are investigated, as well as several levels of the control parameters of each control policy. The results indicate that each control policy gives rise to a unique population of weekly labor, work in process, and inventory level. Product structure is also identified as a major variable in Materials Requirements Planning systems performance. Sensitivity analysis of the cost functions for each policy indicate the conditions under which it will minimize the sum of labor costs, work in process holding costs, and inventory holding costs. The simulation model, MRPSIM, is included with a user's guide. / M.S.

This thesis investigates the applicability of backpropagation neural networks to production order dispatching in a dynamic, single machine shop where the achievement of multiple performance measures is desired. There has been relatively little research done in this area so the objectives center around the determination of information and parameters which lead to improved network performance with respect to learning as well as decision making. Results of the research showed that many of the qualities inherent to backpropagation neural networks were compatible with the requirements of the dispatching activity. The networks that were trained and tested had the ability to implicitly map the complex functional relationships between inputs reflecting system status and desired performance and outputs which represented appropriate coefficients used to determine job priority. Once trained they displayed good generalization capabilities when exposed to information they had never been exposed to before. Most importantly, they provided the basis for a complex dispatching procedure which utilized considerable shop floor information to make completely dynamic, real time dispatching decisions. Guidelines and generalizations for similar applications were developed including: input selection and presentation formats, effective training parameters, the effect of using purely dynamic vs. historical data as shop status inputs, the effect of compromising desired performance measure inputs, and the effect of changes in the underlying shop parameters. / M.S.

Our workshop aims to bring together researchers and practitioners from the fields of AI and/or production investigating, developing, or exploring AI techniques in production. We aim to provide a platform for the exchange of ideas and experiences under the general topic of ‘AI in Production’, not specializing in certain fields of production nor AI but explicitly including production planning, control and optimization. Ideally, our workshop will enable us to standardize approaches for supporting production applying AI or to transfer these approaches from one area of application in production to another. Thus, the Workshop is not only intended for experts in artificial intelligence (in production), but explicitly also for professionals from production.:This Proceedings on 'AI in Production' consists of 5 Proceedings: - Charging Strategies for Automated Guided Vehicles Using Supervised Learning - Optical Neural Networks for Low-latency and Energy-efficient Applications in Production - Flexible Data Architecture for Enabling AI Applications in Production Environments - Perception of Biases in Machine Learning in Production Research - A Structured Literature Review Dissecting Bias Categories - Supporting machine operators in paper production using machine learning based state estimation and user assistance system

Thesis: M.S., Massachusetts Institute of Technology, Department of Civil Engineering, 1982 / Bibliography: leaves 104-105. / by Patrick Little. / M.S. / M.S. Massachusetts Institute of Technology, Department of Civil Engineering

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Implementation of production planning and scheduling systems at Synlait Milk Ltd. for its process manufacturing plant required a project be carried out. Socio-technical considerations as well as ease-of-use for the end-users of a planning system were found to be of the utmost importance in the implementation of a production planning and control system. Implementation options for the systems were weighed and a viable solution found.

A major purpose of this dissertation is to design and develop data structures for the Critical Path Method-Material Requirements Planning (CPM/MRP) methodology. The data structures developed consider the trade-off between processing time required to perform the required operations on data structures and the computer capacity utilization to store data. The CPM/MRP technique was designed to combine the capabilities of the critical path method and material requirements planning system. The critical path method is a project planning and control technique which schedules projects subject to technological sequence constraints and activity durations. When combined with material requirements planning, the methodology explicitly considers both the resources required by the activities comprising the project and the lead time to acquire the required resources. CPM/MRP contains algorithms for project scheduling subject to technological sequence and resource constraints. The early start and late start algorithms find feasible early start and late start schedules for both activity start times and resource order release times. The major drawback of the FORTRAN IV computer program which incorporated the CPM/MRP algorithms was the tremendous computer memory capacity requirements. This prohibited application of the CPM/MRP to large projects. The data structures developed in this dissertation are efficient with respect to both computer memory utilization and processing time. To design the data structures, the characteristics of storable and non-storable resources and the necessary operations within each resource category is studied. Another purpose of this dissertation is to develop an algorithm to schedule operating rooms for surgery procedures in hospitals subject to resource constraints to increase operating suite utilization. Since the major reason for low operating suite utilization is lack of required resources when they are needed and where they are needed, the CPM/MRP concept is applied to schedule surgeries. The late start algorithm outlined in this dissertation schedules surgeries and resources required for each surgery. The data structures and the surgery scheduling algorithm are incorporated into a FORTRAN IV computer program. The program has been tested with actual data gathered from a hospital. The results met the objectives of both computer memory utilization and low computation time.