Carl R. Schultz

An expediting heuristic for the shortest processing time dispatching rule

Recent research on the scheduling of job shops has demonstrated the need for dispatching rules that are effective over a wide range of due-date tightness. In this paper a new dispatching rule is presented and compared with other prominent dispatching rules. The new rule employs shortest-processing-time (SPT) scheduling in a controlled manner: using due-date information to expedite jobs that are late or behind schedule, and employing a heuristic to control the scheduling of jobs with long processing times. Based on a simulation analysis, the rule is shown to perform nearly as well as the SPT rule with respect to mean job flowtime, and without the undesirable SPT side-effect of large conditional mean tardiness. The rule is also shown to be robust to changes in due-date tightness. In comparison with other well-known dispatching rules, its performance is generally superior with respect to mean flowtime, mean tardiness and conditional mean tardiness, with only the COVERT rule performing nearly as well. Unlike ...

Spare parts inventory and cycle time reduction

Today many manufacturing firms are focusing on cycle or throughput time reduction to gain a competitive advantage. In many instances, cycle times at individual workstations are adversely affected by unscheduled machine breakdowns. In this paper, existing closed-form approximations for the expected cycle time at a parallel machine workstation with general process and interarrival times and for the variability in departures from the workstation are used to demonstrate how the mean and variance of machine repair or downtime affects cycle times at a given workstation and downstream workstations. Results from the machine interference literature are used to characterize the dependence of repair time on a base stock inventory policy used for the replenishment of spare components. Expressions for the mean and approximations for the variance of repair times are obtained for machines with either a single critical component or a series of critical components when such components fail exponentially. Numerical examples are given to illustrate the effect that spare component inventory levels have on the average station cycle time and departure variability. A methodology for base stock parameter selection that considers the trade-off between reduced cycle times and inventory investment is also presented.Today many manufacturing firms are focusing on cycle or throughput time reduction to gain a competitive advantage. In many instances, cycle times at individual workstations are adversely affected by unscheduled machine breakdowns. In this paper, existing closed-form approximations for the expected cycle time at a parallel machine workstation with general process and interarrival times and for the variability in departures from the workstation are used to demonstrate how the mean and variance of machine repair or downtime affects cycle times at a given workstation and downstream workstations. Results from the machine interference literature are used to characterize the dependence of repair time on a base stock inventory policy used for the replenishment of spare components. Expressions for the mean and approximations for the variance of repair times are obtained for machines with either a single critical component or a series of critical components when such components fail exponentially. Numerical example...

On the optimality of the (S — 1,S) policy

The one-for-one (S - 1,S) inventory policy, which calls for a replenishment order after each demand equal in magnitude to the size of the demand, is often advocated for controlling the stock levels of expensive, slow-moving items. In particular, this policy has frequently been promoted for use in recoverable-item inventory systems. An important managerial question is: When is the (S - 1,S) policy optimal? Results in this article provide guidance for the selection of this policy. Conditions under which it is not economical to batch demands are developed, provided the renewal function of the renewal process of demand sizes is concave. This includes the important case of unit-sized demands. The inventory system considered is one with continuous review, constant lead times, general interarrival and discrete demand distributions, complete backlogging, and linear holding and penalty costs per unit per unit time. Examples are given for the special case when the demand process is Poisson and when the demand process is a particular compound Poisson distribution known as the stuttering Poisson distribution.

Decision support flexible manufacturing systems

The justification of flexible manufacturing systems (FMS) is a topic that has gained a lot of attention due to the strategic effect it has on the competitive stature of industrial firms. The decision to convert to FMS should follow a pattern of feasibility assessments, cost/benefit analysis where consideration of issues such as increased quality, productivity and capability as well as tangible benefits takes place. Difficulties, however, arise in the modeling of the behavior of the proposed FMS. This paper introduces the design of a decision support framework that aids the strategic planner in simulating the performance of proposed FMS and in determining the parameters that affect the costs and benefits, tangible and intangible, of such a system. The decision support system (DSS) allows the user to use subjective evaluations of benefits accruing from intangible considerations of new product design, faster turnaround on design-to-market cycles and new marketing strategies in fragmented markets. The DSS enhances the examination of issues such as changing demand, varied tasks and routings, job and machine flexibility, etc. which affect costs and benefits. It performs these important functions by allowing for the development of scenarios that aid in the evaluation of the effect of the conversion from non-flexible to flexible manufacturing on the organizations financial position.

Decision Making in a Standby Service System

A standby service option allows a firm to lower its risk of not having sufficient capacity to satisfy demand without investing in additional capacity. Standby service options currently exist in the natural gas, electric, and water utility industries. Firms seeking standby service are typically large industrial or institutional organizations that, due to unexpectedly high demand or interruptions in their own supply system, look to a public utility to supplement their requirements. Typically, the firm pays the utility a reservation fee based on a nominated volume and a consumption charge based on the volume actually taken. In this paper, a single-period model is developed and optimized with respect to the amount of standby capacity a firm should reserve. Expressions for the mean and variance of the suppliers aggregate standby demand distribution are developed. A procedure for computing the level of capacity needed to safely meet its standby obligations is presented. Numerical results suggest that the standby supplier can safely meet its standby demand with a capacity that is generally between 20 to 50% of the aggregate nominated volume.

SPT sequencing with dependent processing times

This paper investigates the applicability of SPT-based strategies to situations where the processing time for a particular task is not known until some other task has been completed. For comparison a random strategy is used. Two cases are considered: the static case which assumes that all jobs to be processed are present in the shop, and the dynamic case in which jobs arrive randomly over time. The improvement in flow time resulting from the SPT-based strategy is quantified analytically for the static case. For the dynamic case some simulation results are presented. Both sets of results indicate that SPT is a very robust strategy which results in significant reductions in a wide variety of situations.