ACO-Based Scheduling of Parallel Batch Processing Machines with Incompatible Job Families to Minimize Total Weighted Tardiness.
ABSTRACT This research is motivated by the scheduling problem in the diffusion and oxidation areas of semiconductor wafer fabrication
facilities (fabs), where the machines are modeled as Parallel Batch Processing Machines (PBPM). The objective is to minimize
the Total Weighted Tardiness (TWT) on PBPM with incompatible lot families and dynamic lot arrivals, with consideration on
the sequence-dependent setup times. Since the problem is NP-hard, Ant Colony Optimization (ACO) is used to achieve a satisfactory
solution in a reasonable computation time. A number of experiments have been implemented to demonstrate the proposed method.
It is shown by the simulation results that the proposed method is superior to the common Apparent Tardiness Cost-Batched Apparent
Tardiness Cost (ATC-BATC) rule with smaller TWT and makespan, especially TWT that has been improved by 38.49% on average.
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- "Another GA approach for minimizing maximum lateness is proposed in (Malve and Uzsoy 2007). Li & Wu (2008) propose an approach minimizing TWT, based on the idea of Ant Colony Optimization (ACO). In (Klemmt, Weigert, Almeder & Mönch, 2009) a VNS approach is compared to a Mixed Integer Programming (MIP) solution combined with TWD (cf. "
ABSTRACT: Studies on operational lot scheduling in semiconductor manufacturing show significantly varying optimization potentials, depending on a multitude of factors relating to methods and models in simulation. We present experiments examining Variable Neighbourhood Search (VNS) used to improve the objectives queuing time and tardiness for the parallel batch machine scheduling problem. The discussed results incorporate the effects of specific model characteristics and constraints, namely incompatible job families, process dedication schemes, critical time bounds, and minimal batch size constraints among others. With regard to methodical factors, we examine the effect of time window decomposition on simulation results, and we discuss fundamental VNS settings, respectively their influence on improvements measured for problem instances of size relevant for industrial applications. This study intends to identify important factors in scheduling studies and evaluates their influence on optimization potentials based on extensive experiments.Simulation Conference (WSC), Proceedings of the 2012 Winter; 01/2012
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- "The obtained solution quality in (Srinivasa Raghavan and Venkataramana, 2006) is less than those of the GA reported by Balasubramanian et al (2004). Li et al (2008) considered the scheduling problem P m |s kj ,r j ,incompatible,batch|TWT where r j denotes the ready time of job j. They form batches using the time window technique suggested by Mo¨nch et al (2005). "
ABSTRACT: In this paper, we discuss the scheduling of jobs with incompatible families on parallel batching machines. The performance measure is total weighted tardiness. This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication where the machines can be modelled as parallel batch processors. Given that this scheduling problem is NP-hard, we suggest an ant colony optimization (ACO) and a variable neighbourhood search (VNS) approach. Both metaheuristics are hybridized with a decomposition heuristic and a local search scheme. We compare the performance of the two algorithms with that of a genetic algorithm (GA) based on extensive computational experiments. The VNS approach outperforms the ACO and GA approach with respect to time and solution quality.Journal of the Operational Research Society 12/2011; 62(12):2083-2096. DOI:10.1057/jors.2010.186 · 0.91 Impact Factor
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ABSTRACT: This paper presents an ACO algorithm to search for feasible schedules of n real-time tasks on M identical processors. Unlike existing works, the proposed algorithm addresses the problem of preemptive scheduling rather than non-preemptive scheduling. A learning technique is integrated to detect and postpone possible preemptions between tasks. The proposed learning technique is also used to develop a necessary condition for the schedulability of the input task set. Experimental results show a significant success ratio improvement of the proposed scheduling algorithm.International Journal of Bio-Inspired Computation 01/2010; 2(6):383-394. DOI:10.1504/IJBIC.2010.037018 · 3.97 Impact Factor