K. Roscoe Davis

Determining buffer location and size in production lines using tabu search

In this paper, we address the problem of buffer location and storage size in a manufacturing line. A simulation-search heuristic procedure based on tabu search, combined with simulation, was developed and tested. Simulation is used to model the manufacturing process and tabu search is used to guide the search to overcome the problem of being trapped at local optimal solutions. The procedure employs a Swap Search routine and a Global Search routine. With the Swap Search routine, the procedure identifies good performing buffer profiles and determines the maximum output level for any given storage level. With the Global Search routine, the procedure can locate promising neighborhood of buffer profiles quickly. The results obtained are then used to plot a line specific output curve. The procedure is capable of modeling a variety of manufacturing processes with a variety of scheduling policies and dispatching rules.

An investigation of output flow control, bottleneck flow control and dynamic flow control mechanisms in various simple lines scenarios

Flow control mechanisms have been a topic of academic research for several years. With the growth of business-wide information systems such as enterprise resource planning and supply chain, better planning, scheduling and control of the business transformation process is required in order to achieve increased throughput, reduced inventories, shorter lead times and reduced tardiness. This research compares two new approaches to flow control, output flow control and bottleneck flow control to a real-time flow control system, dynamic flow control. Both output and bottleneck flow control mechanisms are much simpler to implement and manage than dynamic flow control in that they do not require continual feedback and rescheduling. Line characteristics, such as location of breakdowns with respect to the bottleneck, the location of the bottleneck when breakdowns occur, and the impact of variability of processing times on the performance measures (output, WIP level, lateness, and number of tardy jobs) for these three flow control mechanisms are compared. Both output and bottleneck flow control mechanisms perform favourably (particularly bottleneck) under different scenarios and warrant further study across a wider range of scenarios (mixed models, job shops, etc.).

A Goal Programming Model for Allocating State Promotional Effort to Regional Markets in Accordance With Tourism Potential

Tourism is a growing industry which contributes an ever-increasing amount of revenue to state governments. As a result, states have begun to compete heavily for the tourist trade via promotional and advertising campaigns. This is natural since each state has unique attractions which may be unfamiliar to residents of other states. However, while there is little doubt that promotion is necessary and useful, few models are designed specifically for the allocation of state promotional resources. This is due in part to the limited funds available to state agencies. The budget constraint prevents them from conducting sophisticated research and developing realistic models. This article describes a goal-programming approach for allocating a states promotional effort to specific regions of the country based on the tourism potential of these regions. As such, the model does not optimize but offers guidelines for states to allocate their promotional effort. Allocation is based on several variables which reflect tourism potential, including income variables, travel propensity variables, a gravitational variable, and demographic variables. The model is developed using data obtainable from the federal government, thus minimizing the cost of application and development. The model uses Virginia as a demonstration state.

Addressing the N/1 scheduling problem—A heuristic approach

Abstract The problem of sequencing n-jobs on one machine (n/1) to minimize maximum job lateness has been the subject of much prior research. Most of this research has been directed at identifying optimal solutions to the problem via algorithmic search techniques. A weakness in employing an algorithm for solving the problem, however, is that lengthy computational times may result because of the necessity of searching n! sequences. By employing a multiple heuristic approach this limitation can be avoided. An optimal or near optimal schedule can be identified in a finite number of steps. This paper describes a multiple heuristic model that is effective more than eighty-ninety percent of the time in providing an optimal schedule for the N/l/L max scheduling program. Ten separate heuristics are described, and the results of testing the heuristics over fifteen hundred and sixty randomly generated problems is presented. Three of the heuristics are combined to form the heuristic-scheduling model.

Evaluating time/cost factors of implementation via GERT simulation

Managers in striving for system implementation often disregard the time and cost that must be expended in achieving implementation success. This paper explores the use of GERT network analysis as a tool for planning and ascertaining the expected time and cost of implementation. The supporting base for the study is an implementation process that employs computer gaming for achieving organizational change.

Some aspects of price strategy

A firms success, within a rapidly growing and dynamic market, depends upon its ability to respond to market demand and to react to competitive forces. A key factor determining success is the pricing policy employed by the firm. Product pricing, however, is not simple; supply and demand, as well as the interaction and reaction of competitors, must be taken into consideration. By simulating a competitive market environment, however, a firm should be able to evaluate different pricing strategies prior to employing a strategy in practice. The goal of this research was to propose a simulation model to serve this purpose. Particularly, the objective was to demonstrate that if an industry can be characterized as one in which cost as well as price decline with cumulative volume, a pricing policy leading to market dominance exists. A simulation model is desirable for evaluating the proper pricing strategy for achieving such a market position.

Terminal based computer systems for industrial marketing decisions

During the past decade a number of computer systems and models have been developed to aid marketing management. Computer models now exist which can be used to forecast product demand, evaluate the introduction of a new product, and determine when to abandon or withdraw a product from the market (Assmus, 197 1; Hamelman, 1972; Hegeman, 1967). Yet despite the existence of these models, as well as other related models, marketing managers only sparingly use computers in their work (Kotler, 1970). Kegerreis (197 l), in his article, “Marketing Management and The Computer,” describes the situation with these comments:

Evaluating the financial requirements of a life insurance company via simulation

Abstract During the 19 years between 1950 and the middle of 1968, 2 084 life insurance companies were organized in the United States. However, during this period 934 companies were merged, reinsured, or otherwise retired from business. Several probable reasons can be given for the retirement of many of these companies. Quite often companies are begun with a lack of managerial experience. Some companies often enter the field with a lack of experience in the basic operations of the insurance business. Some companies fail simply because they start with inadequate capital.