Advanced dispatching rules for large-scale manufacturing systems

Abstract

Dispatching rules have been successfully applied to job sequencing and scheduling in large-scale manufacturing systems such as wafer fabrication plants, automatic guided vehicle systems, etc. Because they can be easily communicated and implemented, and because they can be speedily applied, dispatching rules are also one of the most prevalent approaches in this field. However, naysayers often criticize the sluggish performance levels of traditional dispatching rules. Furthermore, in many large-scale factories, scheduling systems have been installed and operational for more than 5 years with “satisfactory” results, but managers still believe that more beneficial modifications are possible. Specifically, better scheduling methods, dispatching rules, test environments, and reporting tools are needed. Over the years, a few new solutions have been proposed to address these issues. For instance, most traditional dispatching rules are based on historical data. With the emergence of data mining and online analytic processing, dispatching rules can now take predictive information into account. Further, rather than concentrating on a single performance measure, some dispatching rules are designed to optimize multiple objectives at the same time. Moreover, the content of a dispatching rule can be optimized for a largescale manufacturing system. In light of advanced computing systems, dispatching rules continue to be one of the most promising technologies for practical applications. This special issue focuses on innovative but practical dispatching rules rather than complex algorithms. This type of dispatching rule will continue to drive the mainstream of practical applications in factories for the foreseeable future. This special issue is intended to provide the details of advanced dispatching rule development and applications of those rules to job sequencing and scheduling in large-scale manufacturing systems. We are very grateful for the positive responses we have received from the authors who submitted papers and the marvelous help provided by a number of referees in the paper reviewing process. After a strict review, 25 papers were finally accepted for publication in this special issue. Zhang et al. used a genetic algorithm (GA) to optimize a set of dispatching rules for scheduling a job shop. Bayesian networks were also utilized to model the distribution of high-quality solutions in the population and to produce each new generation of individuals. In addition, some selected individuals were further improved by a special local search. One advantage of their method is that it can be readily applied in various dynamic scheduling environments which must be investigated with simulation. Lu and Romanowski considered a dynamic job shop problem in which job shops are disrupted by unforeseen events such as job arrivals and machine breakdowns. They used multi-contextual functions (MCFs) to describe the unique characteristics of a dynamic job shop at a specific time and examined 11 basic dispatching rules and 33 composite rules made with MCFs that describe machine idle time and job waiting time. The experimental data showed that schedules made by the composite rules outperformed schedules made by conventional rules. Lin et al. integrated an ant colony optimization (ACO) algorithm with a number of new ideas (heuristic initial solution, machine reselection step, and local search procedure) and T. Chen (*) Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan e-mail: tolychen@ms37.hinet.net