Maintenance scheduling and staffing policies in a wafer fabricationfacility

We examine how to reduce the adverse effects of machine down time by prioritizing production equipment for attention by a limited number of maintenance personnel. The maintenance scheduling policies use system information to determine which machines are the most critical at a given time. This information includes repair times, queue lengths, due dates, available capacity, and steady-state utilizations. The effects of different staffing levels are also considered. A discrete-event simulation model of a wafer fabrication facility is used to examine the performance of the different policies. Results indicate that the choice of maintenance scheduling policy can significantly affect system performance under restrictive staffing-levels. Moreover, factory performance is shown to be sensitive to small changes in staffing level     

Measures of subproblem criticality in decomposition algorithms for shop scheduling

Work centre-based decomposition approaches, especially variants of the Shifting Bottleneck algorithm, have been very successful in solving job-shop-scheduling problems. These methods decompose the problem into subproblems involving a single work centre (usually a single machine), which they solve sequentially. We propose new measures of subproblem criticality and show via computational experiments that several of these provide solutions comparable in quality with those obtained from previous work in substantially less central processing unit time.     

Influence of preparation conditions on porous structures of olive stone activated by H3PO4

An olive factory residue was used as a precursor in the preparation of granular activated carbon by chemical activation with H₃PO₄. Effects of final activation temperature, time, and H₃PO₄ concentration used in the impregnation stage on the porous development were investigated. SO₂ adsorption experiments were also performed for some of the activated carbon samples to represent their adsorption performance. Activation at low temperature represented that micropores were developed first at early stages of the temperatures. Mesoporosity developed at around 250 °C, enhanced up to 400 °C, and then started to decrease due to possibly shrinking of pores. The optimum temperature for olive stone was found to be around 400 °C on the basis of total pore volume and BET surface area. It was clearly demonstrated that H₃PO₄ concentration used in the impregnation stage was not only effective for development of surface area and pore volumes but also an effective tool for tailoring the pore structure and size distribution.     

Heuristics for minimizing maximum lateness on a single machine with family-dependent set-up times

We address the problem of scheduling a single machine subject to family-dependent set-up times in order to minimize maximum lateness. We present a number of local improvement heuristics based on the work of previous researchers, a rolling horizon heuristic, and an incomplete dynamic programming heuristic. Extensive computational experiments on randomly generated test problems compare the performance of these heuristics. The rolling horizon procedures perform particularly well but require their parameters to be set based on problem characteristics to obtain their best performance.     

Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach

Priority-dispatching rules have been studied for many decades, and they form the backbone of much industrial scheduling practice. Developing new dispatching rules for a given environment, however, is usually a tedious process involving implementing different rules in a simulation model of the facility under study and evaluating the rule through extensive simulation experiments. In this research, an innovative approach is presented, which is capable of automatically discovering effective dispatching rules. This is a significant step beyond current applications of artificial intelligence to production scheduling, which are mainly based on learning to select a given rule from among a number of candidates rather than identifying new and potentially more effective rules. The proposed approach is evaluated in a variety of single machine environments, and discovers rules that are competitive with those in the literature, which are the results of decades of research.     

Quantifying the benefits of cycle time reduction in semiconductorwafer fabrication

In recent years, semiconductor manufacturing has become increasingly complex due to device size reduction. Hence the manufacturing cycle time, also called turn around time (TAT), which is defined as the time required from wafer input through probing test, becomes longer year by year. This renders the delay between the occurrence of process defects and their detection a significant problem. On the other hand, customer demands for faster delivery are increasing as product life cycles are getting shorter. Hence, TAT reduction is important for semiconductor manufacturers not only to satisfy customer requirements, but also to remain competitive in their market. This paper examines the financial benefits of TAT reduction in ramping up a new process using stochastic simulation. Results indicate that reducing TAT in the ramp-up phase is important, and that even small reductions can have significant effects over the life cycle of a process     

Impact of antibiotic stress on acid and heat tolerance and virulence factor expression of Escherichia coli O157:H7

This study was conducted to determine the effect of antibiotic stress on the virulence factor expression, simulated gastric fluid (SGF; pH 1.5) survival, and heat tolerance (56 degrees C) of Escherichia coli O157:H7. The MIC for three antibiotics (trimethoprim, ampicillin, and ofloxacin) was determined for two E. coli O157:H7 strains (ATCC 43895 [raw hamburger isolate] and ATCC 43890 [fecal isolate]) by the dilution series method. Subsequently, cells were stressed at the MIC of each antibiotic for 4 h, and poststress tolerance and virulence factor production were evaluated. Heat tolerance (56 degrees C) was determined by the capillary tube method, and SGF (pH 1.5) survival was used to assess acid tolerance. Virulence factor expression (stx, hlyA, and eaeA) was evaluated by the creation of lacZ gene fusions and then use of the Miller assay (a beta-galactosidase assay). Stressed and control cells were evaluated in triplicate. The MIC for trimethoprim was 0.26 mg/liter for both strains; for ampicillin, it was 2.05 mg/liter for both strains; and for ofloxacin, it was 0.0256 and 0.045 mg/liter for each strain. Heat tolerance and SGF survival following antibiotic stress decreased when compared with control cells (P < 0.05). Exposure to ofloxacin increased stx and eaeA expression (P < 0.05). Exposure to ampicillin or trimethoprim increased eaeA expression (P < 0.05). hly expression increased following trimethoprim stress (P < 0.05). Antibiotics can increase E. coli O157:H7 virulence factor production, but they do not produce a cross-protective response to heat or decreased pH.     

Predictable scheduling of a single machine with breakdowns and sensitive jobs

Production schedules released to the shop floor have two important functions: allocating shop resources to different jobs to optimize some measure of shop performance and serving as a basis for planning external activities such as material procurement, preventive maintenance and delivery of orders to customers. Schedule modification may delay or render infeasible the execution of external activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules that can absorb disruptions without affecting planned external activities while maintaining high shop performance. We present a predictable scheduling approach, that inserts additional idle time into the schedule to absorb the impacts of breakdowns. The effects of disruptions on planned support activities are measured by the deviations of job completion times in the realized schedule from those in the predictive schedule. We apply our approach to minimizing total tardiness on a single machine with stochastic machine failures. We then extend the procedure to consider the case where job processing times are affected by machine breakdowns, and provide specialized rescheduling heuristics. Extensive computational experiments show that this approach provides high predictability with minor sacrifices in shop performance.