Evaluation of scheduling rules with commensurate customer priorities in job shops

Abstract

Much of the existing job shop scheduling literature has focussed on scheduling rules and heuristics that typically do not incorporate customer specific characteristics into their scheduling decisions. This paper specifically considers the situation in which depending upon the identity of the customer, different jobs have different tardiness penalties associated with them. In addition, the tardiness penalties associated with different customers are comparable in magnitude with one another. We propose here and test four different types of scheduling rules which utilize the jobs' tardiness penalty information, and show that the performance of the shop is only mildly affected by the shape and dispersion of the weighting scheme used in determining the tardiness penalties. The performance of scheduling rules is however contingent on due-date tightness. In general, apart from the traditionally well performing shortest processing time rule, the ATC rule works well on the mean flow time measure in loose due-date situations, while COVERT performs well on mean flow time under tight due-date conditions. Lesser differences are seen to exist between mean tardiness and root mean square tardiness (TRMS) performance of different scheduling rules. Weighted mean tardiness deteriorates with increasing due-date tightness and coefficient of variation of job tardiness penalties. Finally, customer service performance worsens considerably with weighted dispatching rules when the performance criteria are not weighted by each job's tardiness penalty. Implications of our results for providing more effective management of job shops, and exploring future research directions in this area are also presented and discussed.* Corresponding author. some of which are more important than others. The importance of a customer can depend on a variety of factors, such as the firm's length of relationship with the customer, how frequently they provide business to the firm, how much of the firm's capacity they fill with orders and the potential of a customer to provide orders in the future. It is important for manufacturing to reflect these priorities in their scheduling decisions. This issue is well illustrated through the case of a computer cabinet manufacturer located in the northeastern part of the United States. The manu-0272-6963/95/$09.50

A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions

“…This can be easily done by summing the processing times of all operations for each job, and multiplying this summation with a certain allowance factor value (c) that represents the due date tightness. The values for the allowance factor have been experimentally set such that approximately x% of the jobs will finish later than their due date (Jensen et al, 1995;Russell et al, 1987). To that purpose, the job shop instances have been scheduled with the quick and easy SPT rule applicable to various objective functions for different values of the allowance factor.…”

Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption

“…W i ′ is the estimated waiting time of operation i ′ . The parameters are set as K = 2 and W i ′ = 0.4 × q i ′ following the recommendation of [33]. The random perturbations imposed on the single initial solution are implemented in the following manner.…”

Scheduling in dynamic assembly job-shops to minimize the sum of weighted earliness, weighted tardiness and weighted flowtime of jobs

“…It is therefore important that the scheduling decisions reflect these priorities (or costs) with respect to earliness, tardiness and holding in the process of dispatching jobs in assembly job-shops. In such instances, it is appropriate to associate weights for earliness, tardiness and flowtime of jobs, and gauge the performance of rules by employing the weighted measures of performance (Scudder & Hoffmann, 1987;Jensen, Philipoom & Malhotra, 1995). It also seems appropriate at this juncture to consider briefly the work done in the case of job-shop scheduling with the consideration of weights for earliness/tardiness/flowtime of jobs.…”