Loren Paul Rees

DYNAMICALLY ADJUSTING THE NUMBER OF KANBANS IN A JUST-IN-TIME PRODUCTION SYSTEM USING ESTIMATED VALUES OF LEADTIME

Abstract In a Just-in-Time (JIT) production system with Kanbans, as used by the Japanese, the number of Kanbans employed at each workcenter remains relatively constant from month to month even though demand may change, This occurs because of the unique (and stable) conditions inherent in the production environment of Japanese firms. However, a number of American firms have considered or attempted the implementation of a JIT system without a similar production environment, thus, necessitating that the number of Kanbans at each workcenter be adjusted periodically. In this paper, a procedure for dynamically adjusting the number of Kanbans at workcenters in an unstable production environment is presented and analyzed. The adjustment methodology is presented in a step-by-step manner. This is followed by three examples presented within the context of a simulation model of a hypothetical shop. The first example will illustrate the methodology while the last two examples will demonstrate how well the methodology ...

An investigation of the factors influencing the number of Kanbans required in the implementation of the JIT technique with Kanbans

Abstract Japanese production environments exhibit a number of characteristics not present in many American firms that contribute to the success of the just-in-time (JIT) system with Kanbans. However, because of the advantages of a JIT system with Kanbans many firms have attempted implementation without the existence of the appropriate production environment to do so. The purpose of this paper is first to identify those factors that will influence the number of Kanbans required at a workcentre for the production manager implementing a JIT system in an uniquely American production environment. The factors that will be identified include the throughput velocity (i.e. the rate at which items flow through a workcentre machine), the coefficient of variation in processing times (i.e. the degree of variability of processing times), the machine utilization (i.e. the availability of slack time on a machine), and, autocorrelation of processing times (the degree to which successive processing times on a specific mach...

Using Autonomous Software Agents to Create the Next Generation of Decision Support Systems

The purpose of this research is to explore the promise of autonomous software agents in Decision Support Systems (DSS). Because definitions of software agents extant in the literature are divergent, we develop and provide a descriptive definition useful for our purpose. The benefits of agents and the particular characteristics of agents leading to DSS enrichment are examined. To facilitate this we build a DSS described elsewhere in the literature and enhance it with different types of autonomous software agents. From this experience, a general framework for agent-enabled DSS is suggested. It is concluded that such a DSS in general will be more difficult to build than traditional DSS, but at least some agent-enabled DSS will bring significant benefit to users.

Sequencing mixed-model assembly lines with genetic algorithms

This research introduces the use of an artificial-intelligence based technique, genetic algorithms (GA), to solve mixed-model assembly-line sequencing problems. This paper shows how practitioners can comfortably implement this approach to solve practical problems. A substantial example is given for which GA produces a solution in just a matter of seconds that improves upon Toyotas Goal Chasing Algorithm. The new method is then investigated on a test bed of 80 problems. Results indicate GA generates an improved sequence over Goal Chasing on 50 of the problems and also shows a performance advantage of 2% across all 80 problems using Toyotas variability of parts consumption criterion. The paper concludes that further investigation to fine tune the GA methodology is warranted. It also points out that the GA approach can readily be used by practitioners to address a variety of managerial goals concurrently, such as inventory and work load equalization.

A mathematical programming approach for determining workcentre lotsizes in a just-in-time system with signal Kanbans

SUMMARY An essential condition necessary for the implementation of the Japanese just-in-time (JIT) technique with Kanbans is Small setup times relative to processing times. Without this condition bottlenecks occur at workcentres which delay production. However, the Japanese have developed a variation of the strict JIT technique that employs a special type of Kanban, referred to as a ‘signal Kanban’, at workcentres with relatively high setup times. While a normal Kanban triggers the production of larger than normal lots this altered version of the JIT technique provides an alternative approach for firms that desire to use the JIT technique, but are unable to reduce setup times at all workstations. The purpose of this paper is to describe the signal Kanban technique and demonstrate two versions of an integer mathematical programming approach for determining the optimal lotsizes to be used in conjunction with signal Kanbans. A simulation model is subsequently employed to test the effectiveness of the integer...

Solving Multiple Response Simulation Models Using Modified Response Surface Methodology Within A Lexicographic Goal Programming Framework

Abstract paper describes a new procedure for obtaining satisfactory solutions to multiple-response, multiple-input simulation models. A modified version of response surface methodology is incorporated to obtain input values which meet user specified goals for the responses. The approach is illustrated with three examples which demonstrate the method. The desirability of incorporating this approach into an interactive computer mode is also discussed.

Necessary measures: metric-driven information security risk assessment and decision making

Use measurable, reliable real-world metrics to improve information security decision making.

Going the last mile: a spatial decision support system for wireless broadband communications

High-speed, wireless communication networks are increasing in popularity; however they can be costly and difficult to plan. In this paper we present a spatial decision support system that incorporates expert knowledge of wireless communications, area topography, demographics and propensity to pay for service in order to aid wireless network planners determine optimal placement of equipment to maximize profit or minimize cost. Moreover, the system can be useful in performing policy analysis to determine pricing, governmental subsidy levels, etc. By integrating a GIS tool into the DSS, planners can easily adjust parameters to better understand the problem at hand and move toward bringing broadband connectivity to the last mile.

Cost‐based due‐date assignment with the use of classical and neural‐network approaches

Traditional methods of due‐date assignment presented in the literature and used in practice generally assume cost‐of‐earliness and cost‐of‐tardiness functions that may bear little resemblance to true costs. For example, practitioners using ordinary least‐squares (OLS) regression implicitly minimize a quadratic cost function symmetric about the due date, thereby assigning equal second‐order costs to early completion and tardy behavior. In this article the consequences of such assumptions are pointed out, and a cost‐based assignment scheme is suggested whereby the cost of early completion may differ in form and/or degree from the cost of tardiness. Two classical approaches (OLS regression and mathematical programming) as well as a neural‐network methodology for solving this problem are developed and compared on three hypothetical shops using simulation techniques. It is found for the cases considered that: (a) implicitly ignoring cost‐based assignments can be very costly; (b) simpler regression‐based rules cited in the literature are very poor cost performers; (c) if the earliness and tardiness cost functions are both linear, linear programming and neural networks are the methodologies of choice; and (d) if the form of the earliness cost function differs from that of the tardiness cost function, neural networks are statistically superior performers. Finally, it is noted that neural networks can be used for a wide range of cost functions, whereas the other methodologies are significantly more restricted.

An investigation of the behavior of simulation response surfaces

Abstract This paper is part of a research stream whose purpose is to study the effect of simulation response surface behavior on the choice of appropriate simulation optimization search technique. This papers research lays some groundwork by examining the behavior of simulation response surfaces themselves. The point here is not to criticize existing simulation-optimization techniques (such as Response Surface Methodology (RSM). Rather, one point is to emphasize the care and precision that must be used to invoke extant procedures properly, while another is to demonstrate the need for additional methods such as nonparametric approaches. In particular, this paper examines a simple, inventory-simulation model under various experimental conditions, including some factors under a users control, and some not. Both point and region estimates of surface characteristics are determined and graphed while such factors as number of replications, simulation run length, and demand and lead-time variances are varied. It is found, for example, that even for this simple surface such optimization techniques as first-order RSM can be inappropriate over 21–98% of the feasible region, depending on the case. Four implications are noted from the research: the care that should be exercised with existing simulation-optimization techniques; the need for a simulation-optimization starter; the importance of examining global, nonparametric-metamodeling approaches to simulation optimization; and the desirability of investigating a multi-strategy approach to optimization. The paper concludes with a call for further research investigating these suggestions.