Fundamental Insights into Part Family Scheduling: The Single Machine Case

A simulation study was conducted to investigate the behavior of family scheduling procedures in a dynamic dispatching environment. Two scheduling rules that incorporate setup avoidance mechanisms (FCFS-F and SPT-F) and two that do not (FCFS and SPT) were applied to a single machine. The scheduling environment was varied by controlling several important factors: the machine utilization, the number of setup configurations (families), the size of the family setup times relative to the job run times, the frequency by which members of the part families were released for processing, and the distribution of job interarrival and job run times. The major results from the study are the following: (1) The degree of stability in the system is the most influential factor with respect to mean flow time and flow time variance. Under low variance service and interarrival time distributions, the impact of scheduling rule selection is minor. (2) Conversely, under unstable scheduling situations, family scheduling procedures can have a substantial impact. (3) Clear interaction effects are noticed between all factors. The environment most conducive to family scheduling is characterized by high resource utilization, low setup-to-run time ratio, few part families, and erratic job arrivals. (4) Under conditions favorable to family scheduling, setup avoiding procedures can be used to increase output while at the same time reduce the mean and variance of flow time. (5) The shortest processing time rule (SPT) performs well with respect to mean flow time when relative setup times are small. Overall, however, SPT-F generates the lowest mean flow time while FCFS-F produces the lowest flow time variance. This study shows that scheduling procedures that consider setups in their structure can outperform rules that do not under many different operating conditions. However, the magnitude of this advantage very much depends on the scheduling environment. The results also highlight the fact that it may be better to try to reshape the manufacturing environment than worry about selecting the correct scheduling rule. If the environment cannot be stabilized, then the choice of a setup avoiding procedure, allocation of families to machines, and setup reduction become important issues.

[1]  F. Robert Jacobs,et al.  REPETITIVE LOTS: FLOW-TIME REDUCTIONS THROUGH SEQUENCING AND DYNAMIC BATCH SIZING , 1988 .

[2]  Douglas A. Elvers,et al.  The Sensitivity of the Relative Effectiveness of Job Shop Dispatching Rules with Respect to Various Arrival Distributions , 1974 .

[3]  Jwm Will Bertrand,et al.  A comparison of due-date selection rules , 1981 .

[4]  Blair J. Berkley,et al.  A Simulation Study of Sequencing Rules in a Kanban‐Controlled Flow Shop* , 1991 .

[5]  Farzad Mahmoodi,et al.  An investigation of dynamic group scheduling heuristics in a job shop manufacturing cell , 1990 .

[6]  Barbara B. Flynn.  Repetitive lots: The use of a sequence-dependent set-up time scheduling procedure in group technology and traditional shops , 1987 .

[7]  Nancy Lea Hyer,et al.  Cellular manufacturing in the U.S. industry: a survey of users , 1989 .

[8]  E. Emory Enscore,et al.  Single machine setup-dependent sequencing using a setup complexity ranking scheme , 1988 .

[9]  James E. Day,et al.  Review of sequencing research , 1970 .

[10]  Gary D. Scudder,et al.  The use of cost-based priorities in random and flow shops , 1987 .

[11]  Nancy Lea Hyer,et al.  MRP/GT: a framework for production planning and control of cellular manufacturing , 1982 .

[12]  J. William Gavett,et al.  Three Heuristic Rules for Sequencing Jobs to a Single Production Facility , 1965 .

[13]  L. C. Lee,et al.  A study of system characteristics in a manufacturing cell , 1985 .

[14]  Charles T. Mosier,et al.  Analysis of group technology scheduling heuristics , 1984 .

[15]  Pius J. Egbelu,et al.  Job scheduling in a group technology environment for a single facility , 1985 .

[16]  Richard C. Wilson,et al.  Sequence dependent set-up times and job sequencing , 1977 .

[17]  Iie Amar Dev Amar Senior Member,et al.  Simulated Versus Real Life Data in Testing the Efficiency of Scheduling Algorithms , 1986 .

[18]  Gary D. Scudder,et al.  Application of the Net Present Value Criterion in Random and Flow Shop Scheduling , 1989 .

[19]  J. G. Wager,et al.  Set-up times in cyclic and acyclic Group Technology Scheduling systems , 1983 .

[20]  S. Kekre.  Performance of a Manufacturing Cell with Increased Product Mix , 1987 .

[21]  Asoo J. Vakharia,et al.  Job and Family Scheduling of a Flow-Line Manufacturing Cell: A Simulation Study , 1991 .