Advanced dispatching rules for large-scale manufacturing systems

The International Journal of Advanced Manufacturing Technology, Feb 2013

Toly Chen, Chandrasekharan Rajendran, Chien-Wei Wu

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Advanced dispatching rules for large-scale manufacturing systems

Toly Chen 0 1 Chandrasekharan Rajendran 0 1 Chien-Wei Wu 0 1 0 C.-W. Wu Department of Industrial Engineering, National Tsing Hua University , Hsinchu, Taiwan 1 C. Rajendran Department of Management Studies, Indian Institute of Technology Madras , Chennai, India 2 ) Department of Industrial Engineering and Systems Management, Feng Chia University , Taichung, Taiwan 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 proposed a new apparent tardiness cost (ATCw) heuristic to minimize the total weighted tardiness for unrelated parallel machines. The computational results showed that the proposed ACO algorithm outperformed other existing algorithms in terms of total weighted tardiness. To consider uncertainty and improve the scheduling of a wafer fabrication factory, Chen proposed an innovative fuzzy rule that solves the problem of slack overlapping in a nonsubjective way. The fuzzy rule considers the uncertainty in the remaining cycle time and is aimed at the simultaneous optimization of the average cycle time and cycle time standard deviation. Chen established a systematic procedure to optimize the values of adjustable parameters in the fuzzy rule. Kuo and Cheng solved a job shop scheduling problem with due date time windows and release times, in order to minimize the sum of earliness times and tardiness times. They proposed a novel hybrid meta-heuristic which combines ACO and particle swarm optimization (PSO), called ACPSO. The computational results indicated that ACPSO performs better than ACO and PSO. Hung et al. dealt with the rescheduling of photolithography in semiconductor (...truncated)


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Toly Chen, Chandrasekharan Rajendran, Chien-Wei Wu. Advanced dispatching rules for large-scale manufacturing systems, The International Journal of Advanced Manufacturing Technology, 2013, pp. 1-3, Volume 67, Issue 1-4, DOI: 10.1007/s00170-013-4843-y