Scheduling and shop floor control in commercial airplane manufacturing

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2005.Includes bibliographical references (p. 73-75).Boeing is the premier manufacturer of commercial jetliners and a leader in defense and space systems. Competition in commercial aircraft production is increasing and in order to retain their competitive position, Boeing must strive to improve their operations by reducing costs. Boeing factories today still schedule and monitor the shop floor much as they have for the past 100 years. This thesis compares and contrasts several different methods for shop floor control and scheduling including Boeing's barcharts, Toyota production system, critical chain, and dynamic scheduling. Each system is will be analyzed with respect to how it handles variability in labor output required and how that affects which products are typically made under each system. In additional to qualitative comparisons, discrete event simulations comparing the various strategies will be presented. Areas for future simulation study are also discussed. The recommended approach for commercial airplane assembly is critical chain. A suggested implementation plan is presented along with methods to ease acceptance.by Vikram Neal Sahney.S.M.M.B.A

A scheduling theory framework for GPU tasks efficient execution

Concurrent execution of tasks in GPUs can reduce the computation time of a workload by overlapping data transfer and execution commands. However it is difficult to implement an efficient run- time scheduler that minimizes the workload makespan as many execution orderings should be evaluated. In this paper, we employ scheduling theory to build a model that takes into account the device capabili- ties, workload characteristics, constraints and objec- tive functions. In our model, GPU tasks schedul- ing is reformulated as a flow shop scheduling prob- lem, which allow us to apply and compare well known methods already developed in the operations research field. In addition we develop a new heuristic, specif- ically focused on executing GPU commands, that achieves better scheduling results than previous tech- niques. Finally, a comprehensive evaluation, showing the suitability and robustness of this new approach, is conducted in three different NVIDIA architectures (Kepler, Maxwell and Pascal).Proyecto TIN2016- 0920R, Universidad de Málaga (Campus de Excelencia Internacional Andalucía Tech) y programa de donación de NVIDIA Corporation

A Flexible Simulation Support for Production Planning and Control in Small and Medium Enterprises

For efficient, effective and economical production operation management in a manufacturing unit of an organization, it is essential to integrate the production planning and control system into an enterprise resource planning. Today\u27s planning systems suffer from a low range in planning data which results in unrealistic delivery times. One of the root causes is that production is influenced by uncertainties such as machine breakdowns, quality issues and the scheduling principle. Hence, it is necessary to model and simulate production planning and controls (PPC) with information dynamics in order to analyze the risks that are caused by multiple uncertainties. In this context, a new approach to simulate PPC systems is exposed in this paper, which aims at visualizing the production process and comparing key performance indicators (KPIs) as well as optimizing PPC parameters under different uncertainties in order to deal with potential risk consuming time and effort. Firstly, a production system simulation is created to quickly obtain different KPIs (e.g. on time delivery rate, quality, cost, machine utilization, WIP) under different uncertainties, which can be flexibly set by users. Secondly, an optimization experiment is conducted to optimize the parameters of PPC with regard to the different KPIs. An industrial case study is used to demonstrate the applicability and the validity of the proposed approach

Two stage Indian food grain supply chain network transportation-allocation model

This paper investigates the food grain supply chain, transportation allocation problem of Indian Public Distribution System (PDS). The different activities of Indian food grain supply chain are procurements, storage, movement, transportation and distribution. We have developed a mixed integer nonlinear programming model (MINLP) to minimize the transportation, inventory and operational cost of shipping food grains from the cluster of procurement centers of producing states to the consuming state warehouses. A recently developed chemical reaction optimization (CRO) algorithm is used for testing the model which gives the superior computational performance compared to other metaheuristics

Intelligent shop scheduling for semiconductor manufacturing

Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process. Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant

Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks - the case studies of container terminals

This paper aims to identify the optimal number of vehicles in environments with shuttle transportation tasks. These environments are very common industrial settings where goods are transferred repeatedly between multiple machines by a fleet of vehicles. Typical examples of such environments are manufacturing factories, warehouses and container ports. One very important optimisation problem in these environments is the fleet sizing problem. In real-world settings, this problem is highly complex and the optimal fleet size depends on many factors such as uncertainty in travel time of vehicles, the processing time of machines and size of the buffer of goods next to machines. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on real-world scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on real-world scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size

A simulation modelling approach to improve the OEE of a bottling line

This dissertation presents a simulation approach to improve the efficiency performance, in terms of OEE, of an automated bottling line. A simulation model of the system is created by means of the software AnyLogic; it is used to solve the case. The problems faced are a sequencing problem related to the order the formats of bottles are processed and the buffer sizing problem. Either theoretical aspects on OEE, job sequencing and simulation and practical aspects are presented