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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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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ABSTRACT: Ant Colony Optimization is a swarm intelligence approach that has proved to be useful in solving several classes of discrete and continuous optimization problems. One set, called scheduling problems, is extremely important both to academics and to practitioners. This paper describes how the current literature uses the ACO approach to solve scheduling problems. An analysis of the literature allows one to conclude that ACO is a hugely viable approach to solve scheduling problems. On the basis of the literature review, we were not only able to derive certain guidelines for the implementation of ACO algorithms but also to determine possible directions for future research.Engineering Applications of Artificial Intelligence 01/2013; 26(1):150–161. · 1.63 Impact Factor
Conference Paper: Swarm intelligence supported e-remanufacturing[Show abstract] [Hide abstract]
ABSTRACT: e-Remanufacturing has nowadays become a superior option for product recovery management system. So far, many different approaches have been followed in order to increase the efficiency of remanufacturing process. Swarm intelligence (SI), a relatively new bio-inspired family of methods, seeks inspiration in the behavior of swarms of insects or other animals. After applied in other fields with success, SI started to gather the interest of researchers working in the field of remanufacturing. In this paper we provide a survey of SI methods that have been used in e-remanufacturing.Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I; 06/2012