Randall P. Sadowski

A mixed integer program for loading and scheduling multiple flexible manufacturing cells

Abstract The loading and scheduling of multiple, flexible manufacturing cells is addressed. A fexible manufacturing system may contain two or more flexible manufacturing cells, where each cell is independent but receives its jobs from a common arrival queue. The procedure for this loading and scheduling is via a mixed integer programming formulation. Several objective functions are presented including: minimizing makespan, minimizing mean flowtime, and minimizing mean lateness. The paper contains a brief discussion of why loading of multiple flexible manufacturing cells is important. It also presents the problem and system in question, reviews the applicable past research and details the necessary assumptions. The mixed integer formulation is then presented and explained, followed by a discussion of the number of variables and constraints necessary to solve the program for a real world sized system. And finally, a simple numeric example is given.

Simulation of robotic manufacturing cells: a modular approach:

This research provides a general modeling approach for the design and analysis of computer-controlled manufacturing cells containing robots, sensors, automatic machines, and orientation devices. The development process is described and alternative concepts are discussed. The developed ap proach allows a simulation model to be constructed by se lecting appropriate modules from an available library and linking them together. A demonstration manufacturing cell developed by McDonnell Douglas for the Air Force In tegrated Computer-Aided Manufacturing (ICAM) program is utilized to illustrate the concepts and a more complex exam ple is presented to illustrate the versatility. This approach is flexible because alternative configurations can be modeled quickly and easily. Individuals with significant expertise in cell design but little experience in simulation can create simulation models of manufacturing cells and perform mean ingful analysis.

A simulation-based backward planning approach for order-release

The problem of order release planning for a make-toorder production facility is addressed. Traditionally, order-release planning in a multi-stage shop is performed with material requirements planning (MRP) logic. MRP assumes infinite resource capacity and component lead times that are estimated using historical data, past experience, and rules-of-thumb. These assumptions often result in infeasible plans that make the task of scheduling difficult. An approach to order release planning termed qRP (resource planning based on queuing simulation) is discussed. qRP generates order release plans via a backward bill of material explosion logic similar to MRP except that a queuing simulation model of the facility is used. The simulation model captures the appropriate level of detail to provide a more realistic picture for planning. Component lead times are time-based (dependent on the current state of the shop) and may change from period to period. Automatic factory and simulation model generators are developed to compare this dynamic lead time approach with the static approach offered by MRP. Generalizations are made for key manufacturing attributes.

The Simulation Process: Avoiding The Problems And Pitfalls

This tutorial will present an approach to conducting a simulation project that will aid in avoiding many common problems and pitfalls. The presentation will provide recommendations on how to scope the project, develop a functional specification, formulate and construct the model, verify and validate, collect data, document the work and perform the required analysis. The intent is to provide the novice simulation modeler with proven techniques for conducting a successful simulation project. A variety of case studies will be presented during the tutorial to illustrate both the right and wrong ways to conduct a project.

Scheduling Algorithms for the Unbalanced Production Line: An Analysis and Comparison

Abstract This paper presents an analysis and comparison of scheduling algorithms for the unbalanced production line. A new heuristic algorithm is presented accompanied by an index for classifying the configuration of a production line. A factorial experiment was conducted in order to determine those factors which were significant with respect to the performance measure under consideration, the average cost of holding in-process inventory. Additional analyses were then performed on the line factors and scheduling algorithms with appropriate conclusions drawn. The best performing algorithm was then compared to the performance of Single Period integer programming over a multiple time frame. The heuristic algorithm was found to yield better average performance than the integer programming solution, although the differences were not statistically significant. Recommendations concerning the implementation of the heuristic algorithm are provided.

Animation with CINEMA

Cinema is a general purpose animation package designed to work intimately with the SIMAN simulation language. Cinema consists of two parts. The first, called CINEMA, is used to define the graphical images used in the animation. The second, called CSIMAN, is used to execute the animation. Both programs have a user-friendly graphical interface which does not require any programming. Cinema is available on microcomputers as well as Sun, VAX, and Apollo workstations.

Selling simulation and simulation results

Selling simulation as a valid analysis tool is the first, and maybe the most important, step in the simulation process. Once the decision has been made to use simulation, the analyst must then be concerned with selling the ultimate results produced by the simulation. Most simulation studies are performed by engineers or technical individuals who understand the capabilities and merits of simulation. They also frequently assume that the results of a simulation study will automatically be accepted by management. Given that the system under study has been accurately captured by the simulation and that a thorough analysis has been performed, it seems logical that results will speak for themselves and be accepted. Unfortunately, many decisions are not based solely on logic; and occasionally, results of a valid simulation are discarded by decision makers because they do not fully understand the implications or because they prefer not to believe the outcome. This paper concentrates on the issues regarding the preparation, presentation and selling of the results and attempts to provide guidelines to assure that the information provided by the simulation study is used appropriately.

Avoiding the problems and pitfalls in simulation

An approach is given for conducting a simulation project that will aid in avoiding many common problems and pitfalls. The author provides recommendations on how to scope the project, develop a functional specification, formulate and construct the model, verify and validate, collect data, document the work, and perform the required analysis. The intent is to provide the novice simulation modeler with proven techniques for conducting a successful simulation project. A variety of case studies are presented to illustrate both the right and the wrong ways to conduct a project.<<ETX>>

Generating component release plans with backward simulation

Simulation modeling has traditionally been used in the discrete parts environment for facility design and capacity planning studies. More recently, simulation-based models have been used for generating dispatch lists in scheduling related activities. Currently, there is a growing interest to break away from viewing simulation narrowly as a predictive tool. This paper introduces the concept of backward simulation as a means of determining a required current state based on a desired goal state. This concept is developed into a procedure for generating component release plans based on a master production schedule. Details of this approach are presented.

Optimum Stratified Cycle Counting

Abstract Cycle counting is a recently devised procedure for continuously updating inventory record accuracy as a replacement for an annual complete inventory count. Cycle counting has been shown to provide greater inventory record accuracy for financial control and for production planning and purchasing operations. However, cycle counting procedures have used an arbitrary basis for classifying inventoried items and for setting the count frequency within each class. In this paper, cycle counting is characterized as stratified statistical sampling. Procedures are shown for maintaining the statistical accuracy required for financial control while reducing the amount of sampling and counting costs. Other criteria can be similarly used. These procedures include the Tschuprow-Neyman optimum allocation rule, the Dalenius-Hodges strata identification rule, a search for an optimum number of strata and improvements in the cycle counting procedures and crew size toward an optimum level: compatible methodologies. A n...