An Evaluation of the Effects of Very Difficult Goals

In 1968 the first cohesive theory on goal setting proposed that difficult goals produce higher levels of performance than easy goals and that specific goals produce a higher level of performance than “do your best” goals. While over 40 years of research supports this theory, there has been some discrepancy regarding the use of very difficult goals. This study was designed to examine the effects on performance of different levels of performance improvement goals and two different types of feedback, as feedback is often used in conjunction with goal setting. A group design was used with participants receiving one of two goal levels, and two different types of feedback. While no significant effects were found between the two types of feedback, goal level produced significant results in terms of performance and accuracy.     

MODEL FORMULATION FOR FLEET SIZE ANALYSIS OF A UNIVERSITY MOTOR POOL

A GPSS/360 model was developed to investigate the behavior of a university's motor pool dispatch fleet. Time-series data were collected and frequency distributions were constructed for vehicle request patterns and trip-duration times. Regression analysis was performed to formulate trip mileage generators as functions of service time. Appropriate statistical goodness-of-fit tests were conducted to ascertain the extent of congruence between actual system behavior and expectations based on a number of theoretical distributions. Analysis of simulated response variables indicates that the model's internal structure reproduces reality to a high degree. Fifteen years of simulated experience were generated for six alternative fleet size configurations; results suggest that reductions in existing fleet capacity could be realized without effecting undue impairment in service levels.     

O(m2) ALGORITHMS FOR THE TWO AND THREE SUBLOT LOT STREAMING PROBLEM

Lot streaming is the process of splitting a job or lot into sublots to reduce its makespan on a sequence of machines. The goal in the lot streaming problem is to find the optimal size of each sublot that will minimize the makespan. The makespan is defined as the time the last sublot completes its processing on the last machine. If the sizes of these sublots are restricted to remain the same on all machines, the solution is called a consistent sublot solution. However, if the sizes of the sublots are allowed to vary, the solution is referred to as a nonconsistent or variable sublot solution. Also, if the machines must be in operation continuously from the first to the last sublot, the solution is a no idling solution. When setups are explicitly considered in the problem, there will be two cases. If setups on each machine require some portion of the first sublot be present by the machine, the problem is referred to as the attached setup time problem. If setups can be performed ahead of time before the first sublot reaches the particular machine, the corresponding problem is referred to as the detached setup problem. Finally, if the machines are allowed to be idle between the processing of sublots, the resultant solution is an intermittent idling solution. In this paper, the consistent sublot lot streaming problem with intermittent idling and no setups is discussed. The models developed also assume that the number of sublots are fixed and known. The m machine two sublot lot streaming problem is reviewed. An algorithm for the three sublot, m machine problem is derived using a network representation of the problem. The complexity of the algorithm is O (m²). Finally, using the insights from three sublot problem, a heuristic algorithm is provided for the m machine, n sublot problems. The results on the proposed heuristic are very encouraging; average percent deviation from optimal makespan is approximately at 0.76% on 155 randomly generated problems with different m and n values.