Improved Lead Time Research Articles

Operationalizing lean principles for lead time reduction in engineer-to-order (ETO) operations: A case study

The engineer-to-order (ETO) manufacturing approach is used in companies developing and producing highly customized products. ETO operations include some characteristic business processes such as the sales and tendering process, engineering and design and project management. The characteristics of the ETO environment creates high complexity in these processes, resulting in considerable amounts of waste. The potential for lead time reduction is therefore high also in these processes, not only in the production and assembly processes. Building on lean manufacturing this paper seeks to operationalize lean principles in ETO operations. Through an in-depth case study of an ETO company this paper investigates the challenges, problems and improvement opportunities in the business processes of an ETO company by applying an adapted value stream mapping (VSM) approach. The study served to identify main sources of waste which negatively affect lead time in ETO operations. The paper further presents specific guidelines that operationalizes lean principles in order to reduce waste and improve lead time in ETO operations.

WGNE Intercomparison of Tropical Cyclone Forecasts by Operational NWP Models: A Quarter Century and Beyond

AbstractTropical cyclone (TC) track forecasts of operational numerical weather prediction (NWP) models have been compared and verified by the Japan Meteorological Agency (JMA) under an intercomparison project of the Working Group on Numerical Experimentation (WGNE) since 1991. This intercomparison has promoted validation of the global models in the tropics and subtropics. The results have demonstrated a steady increase in the global models’ ability to predict TC positions over the past quarter century.The intercomparison study started from verification for TCs in the western North Pacific basin with three global models. Up to the present date, the verification has been extended to all ocean basins where TCs regularly occur, and 12 global models participated in the project. In recent years, the project has been extended to include verification of intensity forecasts and forecasts by regional models.This intercomparison project has seen a significant improvement in TC track forecasts, both globally and in each TC basin. In the western North Pacific, for example, we have succeeded in obtaining an approximately 2.5-day lead-time improvement. The project has also demonstrated the benefits of multicenter track forecasts (i.e., consensus forecasts). Finally, the paper considers future challenges to TC track forecasting by NWP models that have been identified at the World Meteorological Organization’s (WMO’s) Eighth International Workshop on Tropical Cyclones (IWTC-8). We discuss the priorities and key issues in further improving the accuracy of TC track forecasts, reducing cases of large position errors, and enhancing the use of ensemble information.

Integrated Simulation Modeling of Business, Maintenance and Production Systems for Concurrent Improvement of Lead Time, Cost and Production Rate

Integrated Simulation Modeling of Business, Maintenance and Production Systems for Concurrent Improvement of Lead Time, Cost and Production Rate

QUANTITATIVE PRECIPITATION FORECAST USING NUMERICAL WEATHER PREDICTION AND METEOROLOGICAL SATELLITE FOR KELANTAN AND KLANG RIVER BASINS

The unusual heavy rainfall episodes over Kelantan River Basin in 2014 had caused massive destruction and several deaths. The unprecedented storm events at the north-eastern Peninsular Malaysia and many other places indicate the need for enhanced storm forecasting to improve disaster preparedness among the civilian. Quantitative precipitation forecast (QPF) from atmospheric model combined with geostationary meteorological satellite information as input to hydrodynamic model for flood forecasting system can potentially provide improved lead time for warning. In this study, a QPF model is developed using the multilayer neural network with data inputs from the numerical weather prediction (NWP) model products combined with the geostationary meteorological satellite infrared and visible image features to forecast precipitation for a flood-prone area in a tropical region. The results indicate that the model can satisfactorily produce 1-hour rainfall forecast with improved accuracy for larger forecast area. The R2 for areal average rainfall for Kelantan river basin is 0.674 and for Klang river basin is 0.893 whereas the R2 for point rainfall is 0.392 for Kelantan river basin and 0.495 for Klang river basin.

A method for probabilistic flash flood forecasting

Summary Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within a distributed hydrological model framework to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Before creating the PFFFs, it is important to characterize QPF uncertainty, particularly in terms of location which is the most problematic for hydrological use of QPFs. The SAL methodology (Wernli et al., 2008), which stands for structure, amplitude, and location, is used for this error quantification, with a focus on location. Finally, the PFFF methodology is proposed that produces probabilistic hydrological forecasts. The main advantages of this method are: (1) identifying specific basin scales that are forecast to be impacted by flash flooding; (2) yielding probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the QPFs; (3) improving lead time by using stormscale NWP ensemble forecasts; and (4) not requiring multiple simulations, which are computationally demanding.

Achievement of Quality, Productivity for Market through KaizenImplementation in Ethiopia

The research was based on the kaizen implementation in Finca’a sugar factory due to the fact that the factory was considered as the role model within the country following its achievement of earning birr 55.6 million birr from first year result of kaizen implementation. This achievement enables the factory to be the first among Kaizen implementers in 2013, and receive Excellence award from Ethiopian kaizen institute in the same year. The following results for Finca’a sugar factory such as financial, cost reduction, obtaining additional income, decrease down time, defects reduction, re usage of machines and equipments by repairing, increasing labor productivity by reducing time loss for searching for tools, additional space obtained, defect improvement, lead time improvement, clean working environments creation, team work and motivation of workers development, health and occupational safety workers improvement, increasing labor employee participation, knowledge obtained on how to meet quick delivery to reduce costs were studied.

PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD

Indonesia consistently experiences dry climatic conditions and droughts during El Niño, with significant consequences for rice production. To mitigate the impacts of such droughts, robust, simple and timely rainfall forecast is critically important for predicting drought prior to planting time over rice growing areas in Indonesia. The main objective of this study was to predict drought in rice growing areas using ensemble seasonal prediction. The skill of National Oceanic and Atmospheric Administration’s (NOAA’s) seasonal prediction model Climate Forecast System version 2 (CFSv2) for predicting rice drought in West Java was investigated in a series of hindcast experiments in 1989-2010. The Constructed Analogue (CA) method was employed to produce downscaled local rainfall prediction with stream function (y) and velocity potential (c) at 850 hPa as predictors and observed rainfall as predictant. We used forty two rain gauges in northern part of West Java in Indramayu, Cirebon, Sumedang and Majalengka Districts. To be able to quantify the uncertainties, a multi-window scheme for predictors was applied to obtain ensemble rainfall prediction. Drought events in dry season planting were predicted by rainfall thresholds. The skill of downscaled rainfall prediction was assessed using Relative Operating Characteristics (ROC) method. Results of the study showed that the skills of the probabilistic seasonal prediction for early detection of rice area drought were found to range from 62% to 82% with an improved lead time of 2-4 months. The lead time of 2-4 months provided sufficient time for practical policy makers, extension workers and farmers to cope with drought by preparing suitable farming practices and equipments.

PREDICTION OF DROUGHT IMPACT ON RICE PADDIES IN WEST JAVA USING ANALOGUE DOWNSCALING METHOD

Indonesia consistently experiences dry climatic conditions and droughts during El Niño, with significant consequences for rice production. To mitigate the impacts of such droughts, robust, simple and timely rainfall forecast is critically important for predicting drought prior to planting time over rice growing areas in Indonesia. The main objective of this study was to predict drought in rice growing areas using ensemble seasonal prediction. The skill of National Oceanic and Atmospheric Administration’s (NOAA’s) seasonal prediction model Climate Forecast System version 2 (CFSv2) for predicting rice drought in West Java was investigated in a series of hindcast experiments in 1989-2010. The Constructed Analogue (CA) method was employed to produce downscaled local rainfall prediction with stream function (y) and velocity potential (c) at 850 hPa as predictors and observed rainfall as predictant. We used forty two rain gauges in northern part of West Java in Indramayu, Cirebon, Sumedang and Majalengka Districts. To be able to quantify the uncertainties, a multi-window scheme for predictors was applied to obtain ensemble rainfall prediction. Drought events in dry season planting were predicted by rainfall thresholds. The skill of downscaled rainfall prediction was assessed using Relative Operating Characteristics (ROC) method. Results of the study showed that the skills of the probabilistic seasonal prediction for early detection of rice area drought were found to range from 62% to 82% with an improved lead time of 2-4 months. The lead time of 2-4 months provided sufficient time for practical policy makers, extension workers and farmers to cope with drought by preparing suitable farming practices and equipments.

Abstract AS12: Integrating genomics, transcriptomics, and proteomics for the discovery of novel biomarkers to complement CA125 in ovarian cancer early detection

Abstract Recent work suggests that a 2-stage strategy incorporating serial measurements of the glycoprotein CA125 in patient serum and the Risk of Ovarian Cancer Algorithm (ROCA), followed by transvaginal ultrasound for women with high-risk scores, may have utility as a screening practice in the general population. However, CA125 only has a sensitivity of 50-60% for early stage disease, and is only present in approximately 80% of all ovarian cancers. Multiple marker panels have shown promise at increasing sensitivity and specificity over CA125 alone, but have not improved lead time for early cancer detection. Therefore, we have taken an unbiased in-depth approach to identify novel plasma biomarkers to complement CA125 in ovarian cancer early detection. Using high-resolution quantitative mass spectrometry, we have profiled the plasma proteome of preclinical patient samples, taken 1 year prior to CA125 elevation and diagnosis, and healthy controls from the Normal Risk Ovarian Screening (NROS) study. With this approach, we have identified over 30 biomarker candidates that are elevated in at least 50% of preclinical samples relative to controls in triplicate experiments, including cancer-specific post-translational modifications. As a means of preliminary validation of promising preclinical biomarkers, we have profiled the plasma proteome of 24 newly diagnosed, early stage ovarian cancer patients and 24 matched healthy controls. Further, half of these patients were selected with uninformative CA125 levels at diagnosis, allowing for the discovery of additional candidate biomarkers that may complement CA125. Correlation of preclinical and newly diagnosed patient plasma data with publicly available genomic and transcriptomic data, as well as proteomic data previously collected from ovarian cancer cell lines resulted in a prioritized list of candidate protein biomarkers and autoantibodies in patient plasma that may improve detection and lead time over CA125. For the most promising candidates, monoclonal antibodies and MagPlex assays are being optimized in order to determine their performance as useful biomarkers in additional patient sera. Multimarker panels will be tested in a training set for the development of a screening algorithm that will be evaluated in preclinical sera to determine the optimized set that improves detection and lead time over CA125. Citation Format: Clayton Boldt, Ph.D., Archana Simmons, Ph.D., Hong Wang, MS, Ph.D., Keith Baggerly, Ph.D., Robert Bast, Jr., M.D., Samir Hanash, M.D., Ph.D.. Integrating genomics, transcriptomics, and proteomics for the discovery of novel biomarkers to complement CA125 in ovarian cancer early detection [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr AS12.

Abstract 2838: TP53 autoantibody can detect CA125 screen negative ovarian cancer cases and can be elevated prior to CA125 in preclinical ovarian cancer

Abstract Detection of ovarian cancer in early stage could improve mortality from the disease by 10-30%. Most attempts to utilize serum biomarkers for early detection of ovarian cancer have focused on CA125. As CA125 is expressed by only 80% of ovarian cancers, multiple biomarkers will be required to detect ovarian cancers that fail to express the antigen. In studies with preclinical samples to date, no biomarker has been consistently elevated prior to CA125. Detecting an autologous immune response to tumor associated antigens might provide improved lead time. TP53 is mutated and overexpressed in virtually all high grade serous ovarian cancers. Autoantibodies reactive with wtTP53 have been reported in approximately 15% of ovarian cancers at the time of conventional diagnosis, but most reports have studied a limited number of cases and preclinical sera have not previously been tested. We have developed a novel immunoassay for quantitating TP53-specific autoantibody (AAb) in small volumes (2 uL) of serum. Samples from the MD Anderson Cancer Center Tissue Bank (MDACC), the MDACC Ovarian SPORE NROS screening study, the Australian Ovarian Cancer Study (AOCS) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) were used for discovery and validation. Assay cut-offs were determined by including less than 2% false positive samples in each control group (98% specificity of the assay). Elevated titers of TP53 AAb were detected in 13 of 50 sera (26%) from MDACC patients with stage III-IV ovarian cancer and in 4 of 216 sera (1.9%) from healthy controls from the NROS study. Using the same cut-off we detected TP53 AAb in 28 of 108 cases (25.9%), 7 of 109 benign controls (6.4%) and 10 of 464 normal controls (2.1%) from the AOCS study. TP53 AAb was found in 3 of 12 cases (25.0%) in early stage (I/II) and 22 of 90 cases (24.4%) in late stage (III/IV). To further validate the result in the AOCS, we analyzed 2,471 serial serum samples from the UKCTOCS trial. Elevated TP53 AAb was found in 18 of 87 cases (20.7%) and 8 of 435 normal controls (1.8%). Increased titers were detected in 6 of 32 cases (18.8%) in early stage and 12 of 55 cases (21.8%) in late stage. Positivity was found in 11 of 49 cases (22.5%) detected with rising CA125 and 7 of 38 (18.4%) cases not detected with rising CA125. TP53 AAb titers rose prior to CA125 in 7 of 11 cases (63.6%). In 11 TP53 AAb (+) and CA125 (-) cases, TP53 AAb titers rose a mean of 13.5 months and a median of 5 months prior to CA125. In 7 TP53 AAb (+) and CA125 (-) cases, titers rose a mean of 33 months and a median of 29 months prior to cancer diagnosis. Consequently, quantitative assessment of TP53 AAb titers holds promise for earlier detection of ovarian cancer in combination with CA125. Citation Format: Wei-Lei Yang, Archana Simmons, Zhen Lu, Keith Baggerly, Karen Lu, Alex Gentry-Maharaj, Usha Menon, Ian Jacobs, Robert C. Bast. TP53 autoantibody can detect CA125 screen negative ovarian cancer cases and can be elevated prior to CA125 in preclinical ovarian cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2838. doi:10.1158/1538-7445.AM2015-2838

A framework for choosing among different lean-based improvement programs

This paper proposes a practical framework for choosing among different lean-based improvement programs in a one-machine environment and applies it in a company in the aeronautics industry. For the studied environment, i.e., a single machine and machining process bottleneck, the application of the proposed framework resulted in a potential 84.63 % improvement in lead time, which can be obtained by improving the mean time between failures by 1373 % and requires an effort of 5880 h. Alternatively, a 34.76 % reduction in lead time is also possible through a 1960-h investment in labor; this improves the coefficient of variation of repair time by 72 %. It is believed that the model proposed here can help managers in practice to quantify and choose between different options of lean-based improvement programs in single-machine or single-cell environments with flowshop standards and a predominant machine, which is typical of lean manufacturing systems.

Improving lead time of pharmaceutical production processes using Monte Carlo simulation

Reliable product supply is one of the most critical missions of the pharmaceutical industry. The lead time, i.e. the duration between start and end of an activity, needs to be well managed in any production facility in order to make scheduling predictable, agile and flexible. We present a method for measuring and improving production lead time of pharmaceutical processes with a primary focus on Parenterals (i.e. injectables) production processes. Monte Carlo simulation is applied for quantifying the total lead time (TLT) of batch production as a probability distribution and sensitivity analysis reveals the ranking of sub-processes by impact on TLT. Based on these results, what-if analyses are performed to evaluate effects of investments, resource allocations and process improvements on TLT. An industrial case study was performed at a production site for Parenterals of F. Hoffmann-La Roche in Kaiseraugst, Switzerland, where the presented method supported analysis and decision-making of production enhancements.

Analysing optimum push/pull junction point location using multiple criteria decision‐making for multistage stochastic production system

This research focuses on solving the multistage process push/pull junction point location problem. An aim is to implement a hybrid push/pull production system that can satisfy both high service‐levels and low inventory levels. Simultaneously, we consider sophisticated variability, such as multi‐products, random setup, indiscriminate break‐downs, yield loss, batch processes, and other contingencies. The problem can be solved by a multiple criteria decision‐making (MCDM) method. A technique for order‐preference by similarity‐to‐ideal solution (TOPSIS) is used to select a suitable option. The optimisation involves evaluation of stochastic performance measures within alternative scenarios among candidate junction‐point locations using a discrete event simulation model. A practical thin film transistor‐liquid crystal display (TFT‐LCD) process case‐study is utilised to illustrate the proposed method. After implementing a hybrid push/pull production strategy, simulation results indicate that the inventory level was reduced by over 18% while the service level remained about the same. For another scenario, a 3.4% decrease in service‐level can be paid off by a 46% decrease in inventory level and 34% improvement in lead time.

Genesis of twin tropical cyclones as revealed by a global mesoscale model: The role of mixed Rossby gravity waves

In this study, it is proposed that twin tropical cyclones (TCs), Kesiny and 01A, in May 2002 formed in association with the scale interactions of three gyres that appeared as a convectively coupled mixed Rossby gravity (ccMRG) wave during an active phase of the Madden‐Julian Oscillation (MJO). This is shown by analyzing observational data, including NCEP reanalysis data and METEOSAT 7 IR satellite imagery, and performing numerical simulations using a global mesoscale model. A 10‐day control run is initialized at 0000 UTC 1 May 2002 with grid‐scale condensation but no sub‐grid cumulus parameterizations. The ccMRG wave was identified as encompassing two developing and one non‐developing gyres, the first two of which intensified and evolved into the twin TCs. The control run is able to reproduce the evolution of the ccMRG wave and thus the formation of the twin TCs about two and five days in advance as well as their subsequent intensity evolution and movement within an 8–10 day period. Five additional 10‐day sensitivity experiments with different model configurations are conducted to help understand the interaction of the three gyres, leading to the formation of the TCs. These experiments suggest the improved lead time in the control run may be attributed to the realistic simulation of the ccMRG wave with the following processes: (1) wave deepening (intensification) associated with a reduction in wavelength and/or the intensification of individual gyres, (2) poleward movement of gyres that may be associated with boundary layer processes, (3) realistic simulation of moist processes at regional scales in association with each of the gyres, and (4) the vertical phasing of low‐ and mid‐level cyclonic circulations associated with a specific gyre.

A network design model considering inventory cost from a third party logistics perspective

The importance of third party logistics (3PLs) service providers is increasing in supply chain management, particularly in warehousing and transportation services as clients expect 3PLs to improve lead times, fill rates, and inventory levels. Therefore, in a dynamic and uncertain business environment, 3PLs are under pressure to meet client service requirements. Few studies have addressed the problem of logistic network design from viewpoint of 3PLs. The main objective of this paper is to propose a new network design problem from 3PLs perspectives by considering inventory to determine economic order quantity. This paper proposes a new nonlinear mixed integer programming model for integrated logistics network design that in facility location-allocation and inventory location problems. To optimize the model, new auxiliary variables and constraints are added. The model was solved by using DICOPT solver.

Axiomatic design of hybrid manufacturing systems in erratic demand conditions

In order to respond to part demands on time in an erratic demand environment, a complete road map for the design of hybrid cellular manufacturing systems (HMS) is proposed in this study, based on axiomatic design (AD) principles, such that all functional requirements of a complete HMS design are satisfied by the corresponding design parameters. With this methodology, first, a preliminary HMS is formed including exceptional operations. To facilitate one-piece flow within the cells of the HMS, these exceptions are eliminated by the proposed decision rules, where alternative machines are employed (Satoglu et al. 2010). Then, the sufficiency of current resource capacities is verified, using a simulation technique. In other words, stock accumulations are identified and then value stream through the system without any obstacles is ensured. These are achieved by means of procedures for identifying and eliminating the bottleneck resources. The proposed methodology is completed with the HMS layout design. This complete methodology based on AD principles is a unique aspect of this study. The methodology is applied on the data of an automotive supplier, and the results are discussed. Finally, using simulation models, it is shown that applying the proposed model results in a statistically significant improvement in lead time of the current available system.

Predictability of Seasonal Sahel Rainfall Using GCMs and Lead-Time Improvements Through the Use of a Coupled Model

AbstractThe ability of several atmosphere-only and coupled ocean–atmosphere general circulation models (AGCMs and CGCMs, respectively) is explored for the prediction of seasonal July–September (JAS) Sahel rainfall. The AGCMs driven with observed sea surface temperature (SST) over the period 1968–2001 confirm the poor ability of such models to represent interannual Sahel rainfall variability. However, using a model output statistics (MOS) approach with the predicted low-level wind field over the tropical Atlantic and western part of West Africa yields good Sahel rainfall skill for all models. Skill is mostly captured in the leading empirical orthogonal function (EOF1), representing large-scale fluctuation in the regional circulation system over the tropical Atlantic. This finding has operational significance for the utility of AGCMs for short lead-time prediction based on persistence of June SST information; however, studies have shown that for longer lead-time forecasts, there is substantial loss of skill, relative to that achieved using the observed JAS SST.The potential of CGCMs is therefore explored for extending the lead time of Sahel rainfall predictions. Some of the models studied, when initialized using April information, show potential to at least match the levels of skill achievable from assuming persistence of April SST. One model [NCEP Climate Forecasting System (CFS)] was found to be particularly promising. Diagnosis of the hindcasts available for the CFS (from lead times up to six months for 1981–2008) suggests that, especially by applying the same MOS approach, skill is achieved through capturing interannual variations in Sahel rainfall (primarily related to El Niño–Southern Oscillation in the period of study), as well as the upward trend in Sahel rainfall that is observed over 1981–2008, which has been accompanied by a relative warming in the North Atlantic compared to the South Atlantic. At lead times up to six months (initialized forecasts in December), skill levels are maintained with the correlation between predicted and observed Sahel rainfall at approximately r = 0.6. While such skill levels at these long lead times are notably higher than previously achieved, further experiments, such as over the same period and with comparable AGCMs, are required for definitive attribution of the advance to the use of a coupled ocean–atmosphere modeling approach. Nonetheless, the detrended skill achieved here by the January–March initializations (r = 0.33) must require an approach that captures the evolution of the key ocean–atmosphere anomalies from boreal winter to boreal summer, and approaches that draw on persistence in ocean conditions have not previously been successful.

Queuing network modelling techniques for response time enhancement in electronics assembly

The concept of quick response manufacturing points up that industrial manufacturing companies pressed to compete on speed by emerging competitor must change the management mind-set and reduce lead times throughout the organisation. As a factory's work-in-process, lead times and capacity are the result of complex dynamics in the production system, to predict whether a decision will improve lead times, it is necessary to be able to forecast both process and queue times. In this paper, a novel approach based on analytical techniques for queuing network modelling, called rapid modelling technology (RMT), is implemented via the MPX© software package for complex shop floor operations modelling. The main scopes of the MPX© utilisation are to evaluate the human and economic resources of a printed circuit board (PCB) producer company and to find solutions for the optimisation of the PCB manufacturing system performances.

Genetic Algorithm and Particle Swarm Optimization for Solving Balanced Allocation Problem of Third Party Logistics Providers

Third party logistics (3PL) service providers play a growing responsibility in the management of supply chain. The global and competitive business environment of 3PLs has recognized the significance of a speedy and proficient service towards the customers in the past few decades. Particularly in warehousing, distribution, and transportation services, a number of customers anticipate 3PLs to improve lead times, fill rates, inventory levels, etc. Therefore, the 3PLs are under demands to convene a range of service necessities of customers in an active and uncertain business environment. As a consequence of the dynamic environment in which supply chain must operate, 3PLs should sustain an effective distribution system of high performance and must make a sequence of inter-related decisions over time for their distribution networks. Warehouses play an important role in sustaining the continual flow of goods and materials between the manufacturer and customers. The performance of the 3PL supply chain network can be effortlessly enhanced by a balanced allocation of customers to warehouses. In this paper, the authors develop a genetic algorithm and a particle-swarm-optimisation algorithm for solving the balanced allocation problem and the results are encouraging.

A genetic algorithm for one-job m-machine flowshop lot streaming with variable sublots

Lot streaming is a technique used to split the processing of lots (batches) into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production makespan. In this technique, a production lot may be split into equal, consistent or variable sublots. Recent literature shows that, when production setup time is considered, significant lead time improvement is possible if variable sublots are used. In this research, however, we noted that lot streaming problems with variable sublots are difficult to solve using off-shelf optimisation packages even for problems of smaller sizes. Thus, efficient solution procedures are needed for solving such problems. In this paper, we develop a hybrid genetic algorithm for a model that appeared in recent literature for one -job m -machine lot streaming problems with variable sublots and setup. Computational results showed that the performance of the proposed genetic algorithm is encouraging.