Edward Williams

When simulation analyses first became at least somewhat commonplace (as opposed to theoretical and research endeavors often considered esoteric or exploratory), simulation studies were usually not considered “projects” in the usual corporate-management context. When the evolution from “special research investigation” to “analytical project intended to improve corporate profitability” began in the 1970s (both authors' career work in simulation began that decade), corporate managers naturally and sensibly expected to apply the tools and techniques of project management to the guidance and supervision of simulation projects. Intelligent application of these tools is typically a necessary but not a sufficient condition to assure simulation project success. Based on various experiences culled from several decades (sometimes the most valuable lessons come from the least successful projects), we offer advisories on the pitfalls which loom at various places on the typical simulation pro...

The milling process is a widely used conventional machining operation. Due to economic reasons, the multi-pass milling process is more convenient. However, the required time for
machining increases and an optimization solution must be undertaken. In this paper, the total production time is minimized by resorting to a powerful bio-inspired algorithm, called the cuckoo optimization algorithm. The constraints are successfully handled and the optimal results are compared with those available in the literature. It is shown that the present results are better.

The combined heat and power economic dispatch (CHPED) is a complex engineering optimization problem. The goal is to minimize the system production costs by taking into
consideration different kind of constraints. This research investigates the first implementation of a prevailing bio-inspired metaheuristic called the cuckoo optimization algorithm which is
powered by a penalty function (PFCOA) for solving the CHPED problem. Two case studies of the CHPED are presented and the results are compared to those obtained by several other
optimization techniques applied in the literature. It has been proven that the implemented PFCOA is superior.

The multi-pass turning process is one of the most used machining methods in manufacturing industry. The minimization of the unit production cost is considered the key objective of this operation. In this work, the cutting parameters are carried out using a recently developed advanced bio-inspired optimization algorithm, called the cuckoo optimization algorithm (COA). The obtained results are compared with previously published results available in the literature. It has been proven that the COA competes robustly with a wide range of optimization algorithms

We describe the concept of a carousel as seen from the
business-process and the system-modeler viewpoints, compare and contrast various algorithms for picking from a carousel, and discuss the simulation modeling logic of one such algorithm in detail. The results and conclusions from simulations of two carousel systems are presented.
First. we present an introductory overview of a simulation study motivating a detailed examination of carousels. Next, we define a carousel and present an overview of the picking operation relative to a carousel.  We then describe four algorithms for picking and discuss in detail the representation of these algorithms within simulation models, followed by a  presentation of results from such simulations. In conclusion, we
summarize carousel-configuration and picking algorithm
conclusions.

Simulation has become so well regarded that many businesses, in a variety of industries, now routinely realize its benefits; many others, those new to simulation, are eager to do so. Likewise, the use of simulation, long concentrated in the heavy
manufacturing sector of the economy, has diversified into all sectors. Companies new to simulation often seek entry to this technology via retention of a consulting partner company already highly experienced and competent in its application. Too often, however, the company striving to incorporate simulation into its armoury of analytical and problem-solving tools becomes mired in dependency upon consultants
indefinitely, even for what should be relatively routine
modifications and extensions of the model originally
constructed. In this paper is documented a successful, even rapid, emergence from such dependency – a client achieving self-sufficiency in simulation.

The bond graph (BG) is a modeling and simulation tool, providing many possibilities which are used in mechatronics. Mechatronics is a multidisciplinary engineering field necessitating a unified method for the monitoring. This paper deals with a model based real-time simulator for CNC machines in order to detect eventual failures. A versatile methodology based on bond graph analysis to build a dynamic icon model library which is successful implemented for the Fault Detection and Isolation (FDI). The developed approach is investigated using SYMBOLS2000 software.

This paper presents a creative approach to modeling product orders in AutoMod. The technique presented is a highly efficient and accurate method when product orders are complicated but occur with a fairly low volume. We begin by discussing the two typical means of representing product orders:  ( I ) using an order history file and (2) using statistical distributions to model all aspects of product orders. We then discuss the "new" approach that we have developed that involves using distributions to model order volume, but then reading in actual product orders into large AutoMod variable arrays and randomly sampling orders from the AutoMod "order matrix," thus created in order to accurately represent characteristics associated with orders. We then discuss the issues associated with implementing this technique in AutoMod and the use of this technique in a simulation model or manpower and assembly cell planning that was developed for a water faucet manufacturing firm. We conclude by describing the situations where this approach is preferable to other approaches.

This paper describes the methodology, challenges and findings from a simulation modeling and analysis project dealing with the study of a complex power & free transportation system in a flexible manufacturing environment. The system under consideration transports six car body types through different stages of the production lifecycle (including body, paint and assembly). As production levels and product mix change, the interactions between different system parameters become too complex to analyze analytically. A simulation study was undertaken to identify bottlenecks and determine/improve the capacity of the P&F system at peak demand while validating various routing rules at key decision points.

As supply chains become longer (both conceptually and
geographically), more complex, and more important to the
financial health of companies participating in them, the
efficiency of operations in warehouses correspondingly
increases in importance. Historically, it did not take long for simulation technology, originally heavily used in the analysis of manufacturing operations, to earn high regard as a powerful analytical tool for the improvement of warehouse operations. In the study examined in this paper, a warehouse accepts a variety of product shipments which must travel along conveyors to be protectively stretch-wrapped before they are stored for later picking. Simulation and its accompanying statistical techniques were used to assess and optimize the configuration of machines, material-handling equipment, and labor.

Discrete-event process simulation has long since earned its place as one of the most powerful and frequently applicable analytical tools available for production process analysis and improvement. Also, the manufacturing economic sector has rapidly become more competitive, requiring both greater economy and greater efficiency of operations. In the present
application of simulation, a Tier I automotive industry supplier, beset with these economic pressures, sought to improve the productivity and efficiency of a test-&-inspection line being designed for an automotive engine plant. Simulation was successfully used to design this line to meet stringent requirements of productivity, low original equipment cost, and low operating cost. Via comparison of multiple alternatives, this simulation study identified potential bottlenecks and produced
recommendations for their improvement or removal.

Recent and ongoing developments are significantly augmenting both the demand for and the expectations of university simulation education. These developments include increased use of simulation in industry, increased variety of economic segments in which simulation is used, broader variation in demographics of simulation students, and higher expectations of both those students and their eventual employers. To meet
the challenges these developments impose, it is vital that simulation educators aggressively and innovatively improve the teaching of simulation. To this end, we explore the application of constructive alignment concepts in simulation education, and compare and contrast its application in the context of two
university course offerings. These concepts suggest  continuation of some practices and revision of others
relative to the learning objectives, learning activities, and assessment tasks in these and other simulation courses.

The bastion of simulation application has long been the manufacturing industry. In particular, the major corporations within the automotive industry, which is highly complex, multiply tiered, and a significant component of the manufacturing economic sector in both North America and Europe, have repeatedly, aggressively, and profitably applied simulation to the investigation and improvement of production systems. However, many of the smaller companies supplying components to motor vehicle manufacturers have had greater difficulty accessing and applying simulation technology. This difficulty, in the experience of the first author, is typically due to high initial costs, political inertia among managers, or both. In this case study, the authors examine the introduction and use of discrete process simulation into an automotive component supplier for the examination of various plausible scenarios whose effects production engineers and their management wished to anticipate.

Simulation  has  long  been  an  analytical  tool  of significant    importance    and    power    for    process improvement.      Historically,  the  earliest  and  most widespread  uses  of  simulation  were  in  manufacturing industries; however, it was not long before the power of simulation  was  applied  to  improve  productivity  and assess the relative merits of process change alternatives within various service industry segments such as travel, hotel  and  restaurant,  retail  stores, and  entertainment  venues  such  as  theatres  and  amusement  parks.    The study  described  in  this  paper  describes  the  successful  application  of  simulation  to  process  management  and  improvement  within  a  business  devoted  to  aftermarket  repair  of  privately  owned  automobiles  and  trucks.    We  describe  the  problems  encountered  by  the  client  and  how  the simulation  study  illuminated  a  pathway  to significant  improvements  in  customer  service  and financial profitability.

Discrete-event process simulation has long ago expanded from its initial bailiwick of manufacturing and production usage to benefit businesses across a broad spectrum of service industries such as travel, lodging, restaurants, and health care. The study presented here arose in the context of a senior-level university simulation class as a semester project. In this study,
simulation was applied to the day-to-day operations of a
drive-in facility to service customers’ privately owned motor vehicles. As a result of the simulation study, managers of the business, supported by industrial engineers, became both more aware of improvement opportunities and better able to assess their comparative potential impacts on the profitability of the business.

Historically, discrete-event process simulation was first and most widely used in manufacturing contexts. As the technology has gradually matured and knowledge of its benefits has become more widely known, simulation usage has spread to warehouse design and operation, health-care delivery, retail customer service, and (as in this paper), the transport of people and/or goods. This project simulated the Blue Bus system of the University of Michigan. We analyzed the waiting time of passengers at each stop and the utilization of buses under two strategies; one is based on the schedule, the other is adding eight buses of Bursley-Baits during peak time besides the scheduled shifts which is practical Blue Bus running strategy. Under the two strategies, we analyzed the different scenarios that number of passengers increases by some times. We compared these scenarios and found the practical strategy is more robust when there is a sharp increase of passengers.

Until the day when plant production personnel and equipment have no downtime, proper collection and analysis of downtime data will be essential to the development of valid, credible simulation models. Methods and techniques helpful to this task within simulation model building are described.

Ford Motor Company has developed a triplet of in-house simulation classes: an overview of simulation in manufacturing, an introduction to simulation methods, and language-specific classes in SIMAN and WITNESS. This set of courses is described and, as an example, the in-house short-course training of engineers and vendor- partners' engineers in simulation methodology and the use of SIMAN is compared to and contrasted with a typical university semester-length course in simulation, its methodology, and its software.

We present a sampling of some practical statistical techniques for the simulation of manufacturing systems and also briefly describe some software products that can be used to aid in the various stages of the modeling process. Use of these statistical techniques is illustrated with case studies of actual manufacturing systems.

Simulation models, whether discrete, continuous, or a combination of both, are characteristically built to improve the understanding of a system and the processes operating within that system. Continuous simulation models study continuous variables, amenable to analysis via mathematical techniques such as differential and difference equations. Discrete-event process simulation models study integer-valued or binary variables requiring analysis via methods of discrete mathematics, statistics, and operations research. Additionally, random, stochastic variation is frequently both a significant provocation for undertaking a discrete process simulation study and a significant challenge within that study.
After describing the similarities and differences between continuous and discrete-event process simulation, this paper discusses typical business motivations for use of discrete simulation and presents a methodology and workplan for such studies in the context of example applications. Next, we describe data characteristically needed to drive a discrete-event process simulation model and the statistical concerns and methods pertinent to analyses of both input data and output results. We conclude with a generic description of computer software tools for building discrete models and a brief presentation of three case studies from manufacturing.

Simulation, as a method of analyzing and predicting the performance of complex systems, is commendably Protean, able to increase understanding of and contribute to the improvement of systems in a broad spectrum of contexts. These contexts include, but are certainly not limited to, assessing the structural adequacy of mechanical systems (mechanical or civil engineering), predicting the behavior of circulatory systems (meteorology), evaluating military strategies (defense), increasing the efficiency of business processes (business process reengineering), configuring computer and communications networks (computer systems design), and enhancing the reliability, productivity, and economy of operation of manufacturing systems (industrial engineering).
Users of simulation in any of these contecxets share the challenges of verification and validation. Any simulation model must be verifiable as operating in the way intended by the modelers, valid as a representation of the actual physical system under design or study, and credible as a recipient of trust and confidence from the managers who look to the simulation for guidance in making decisions involving uncertainty and economic risk. In this paper, we describe the verification and validation techniques typically used within simulation studies of industrial processes. Such studies may, for example, improve the allocation of buffer storage within an assembly line, determine an appropriate number of pallets to be used within a recirculating pallet loop, or assist in determining routing policy for automatic guided vehicles. Via case studies, we illustrate the proper role of verification and validation, not as incidental increments to a simulation project, but as basic requisites thoroughly integrated into such a project. In conclusion, we then outline promising areas for future advances in development and usage of verification and validation techniques.

Producing and mentoring the next generation of simulation analysts requires close, effective cooperation between educational institutions and corporate beneficiaries of simulation technology. In this paper, we describe the responsibilities of both universities and corporate users relative to this cooperation. We also discuss the communication between the two necessary to this cooperation. Strengths and weaknesses within the current system of educating simulation analysts are noted with a view to desirable changes.

As supply chains become longer (both conceptually and geographically), more complex, and more important to the financial health of companies participating in them, the efficiency of operations in warehouses correspondingly increases in importance. Historically, it did not take long for simulation technology, originally heavily used in the analysis of manufacturing operations, to earn high regard as a powerful analytical tool for the improvement of warehouse operations. In the study examined in this paper, a warehouse accepts a variety of product shipments which must travel along conveyors to be protectively stretch-wrapped before they are stored for later picking. Simulation and its accompanying statistical techniques were used to assess and optimize the configuration of machines, material-handling equipment, and labor.

Simulation education has been a significant facet of university curricula, both in industrial engineering and in business management, for many years. Indeed, the importance of simulation education to both these curricula approximately coincides with the accessibility of simulation analyses via skillful programming in computer languages on mainframes and significantly precedes the availability of desktop computers and their specialized, largely point-&-click software tools. The simulation educator, whether teaching within a college of engineering (and most likely the sub-discipline of industrial engineering) or within a college of business or management, has various valuable opportunities to emphasize the reliance of simulation upon prerequisite and concurrent course work. Likewise, educators in related disciplines have opportunities to stress the usefulness of material taught in their courses to simulation analyses. When fully exploited, these cross-fertilization opportunities enhance collegiality, student motivation, and retention and integration of important concepts and techniques. In this paper, we explain these opportunities, particularly with respect to statistical concepts, computer analysis and programming skills, industrial engineering and managerial observations, and interpersonal and teamwork skills. Broadly stated, we undertake the examination of both “how the simulation educator can support the educator of related disciplines,” and the converse “how the educator of disciplines related to simulation can support the instructor of simulation.”

Simulation has long been recognized as an analytical tool of high power and wide applicability when applied to the improvement of manufacturing processes. Indeed, historically, manufacturing applications were the first major purview of simulation usage by large companies. As manufacturing processes increase in complexity, both operational and economic, the capabilities of simulation increase both in importance and in difficulty of duplication by alternative analytical methods. In this paper, we examine in detail the application of simulation to an automotive stamping plant. Simulation identified bottlenecks in material handling, pointed the way to increasing utilization of a costly stamping press, and quantified the relationships between specific capital investments under consideration and the throughput increase to be expected.

Exercises between the covers of a statistics textbook are nice: the data are quantitative and come from normal distributions, independence runs rampant, the "user" knows exactly the confidence level desired, the population variances are known from prior experience, no harsh political realities impede the undertaking of statistically indicated recommendations, etc. Real-world problems as seen by the statistical practitioner are messy in comparison: data may be qualitative, non-normal, or incomplete; the correlation Hydra grows uglier heads frequently, the user expects a binary answer, population variances are unknown (but large), and competing political factions hope to use statistics as a drunkard uses a lamppost --not for light, but for support. The insights collected in this paper will help: · the newly trained statistician making the transition from textbook exercises to practical application · the manager assisting the acclimatization of newly hired statisticians or consultants to their corporate assignments · the educator eager to give his or her students a "jump-start" on preparation for a statistician's typical day-to-day work · the recipient of statistical consulting services who is eager to understand the "behind-the-scenes" motivation for a competent consultant's concerns and questions.

Achieving efficiency of initial investment and operational expense with respect to a transfer-line manufacturing system presents many challenges to the industrial or process engineer. In this paper, we describe the integration of simulation, statistical analyses, and optimization methods with traditional process design heuristics toward meeting these challenges. These challenges include investigation of the possibility of combining selected operations, scheduling arrivals to the process from upstream operations, quantity and configuration of machines appropriate to each operation, comparing effectiveness of various line-balancing alternatives, sizes and locations of in-process buffers, choice of material-handling and transport methods, and allocation of manufacturing personnel to various tasks such as material handling and machine repair. We then describe our approach to meeting these challenges via the integration of analytical methods into the traditional methods of manufacturing process design. This approach comprised the gathering and analysis of input data (both qualitative and quantitative), the construction, verification, and validation of a simulation model, the statistical analysis of model results, and the combination of these results with engineering cost analysis and optimization methods to obtain significant improvements to the original process design.

Discrete-process simulation, at first most heavily used for analyses of manufacturing operations, has steadily expanded its areas of application into provision of health care, service industries, supply and logistics, and transportation facilities. In the application described here, simulation documented quantitatively, and provided suggestions for ameliorating, severe delays at a publicly accessible transportation facility, the tunnel between Windsor, Ontario, Canada; and Detroit, Michigan, United States.

We describe the development and analysis of a simulation model built to examine logistics and material handling issues within a flow shop. Since this project was undertaken in the context of a university course in discrete-event process simulation, we additionally describe the rôle the project played relative to the educational objectives of this course. In the context which spawned this paper, "educational objectives" extends well beyond the narrow implications of "syllabus" and "classroom" to encompass broader perspectives attained by all of the client, the students, and the instructor.

All during the past half-century, the environment of computing applications has evolved from large, comparatively slow mainframes with storage small and expensive by today’s standards to desktops, laptops, cloud computing, fast computation, graphical capabilities, and capacious flash drives carried in pocket or purse. All this time, discrete-event process simulation has steadily grown in power, ease of application, availability of expertise, and breadth of applications to business challenges in manufacturing, supply chain operations, health care, call centers, retailing, transport networks, and more. Manufacturing applications were among the first, and are now among the most frequent and most beneficial, applications of simulation. In this paper, the road, from newcomer to simulation in manufacturing to contented
beneficiary of its regular and routine use, is mapped and signposted.