Robust manufacturing system design using petri nets and bayesian methods

Manufacturing system design decisions are costly and involve significant investment in terms of allocation of resources. These decisions are complex, due to uncertainties related to uncontrollable factors such as processing times and part demands. Designers often need to find a robust manufacturing system design that meets certain objectives under these uncertainties. Failure to find a robust design can lead to expensive consequences in terms of lost sales and high production costs. In order to find a robust design configuration, designers need accurate methods to model various uncertainties and efficient ways to search for feasible configurations. The dissertation work uses a multi-objective Genetic Algorithm (GA) and Petri net based modeling framework for a robust manufacturing system design. The Petri nets are coupled with Bayesian Model Averaging (BMA) to capture uncertainties associated with uncontrollable factors. BMA provides a unified framework to capture model, parameter and stochastic uncertainties associated with representation of various manufacturing activities. The BMA based approach overcomes limitations associated with uncertainty representation using classical methods presented in literature. Petri net based modeling is used to capture interactions among various subsystems, operation precedence and to identify bottleneck or conflicting situations. When coupled with Bayesian methods, Petri nets provide accurate assessment of manufacturing system dynamics and performance in presence of uncertainties. A multi-objective Genetic Algorithm (GA) is used to search manufacturing system designs, allowing designers to consider multiple objectives. The dissertation work provides algorithms for integrating Bayesian methods with Petri nets. Two manufacturing system design examples are presented to demonstrate the proposed approach. The results obtained using Bayesian methods are compared with classical methods and the effect of choosing different types of priors is evaluated. In summary, the dissertation provides a new, integrated Petri net based modeling framework coupled with BMA based approach for modeling and performance analysis of manufacturing system designs. The dissertation work allows designers to obtain accurate performance estimates of design configurations by considering model, parameter and stochastic uncertainties associated with representation of uncontrollable factors. Multi-objective GA coupled with Petri nets provide a flexible and time saving approach for searching and evaluating alternative manufacturing system designs

Robust manufacturing system design using multi objective genetic algorithms, Petri nets and Bayesian uncertainty representation

a b s t r a c t Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties

Bottleneck analysis of a chemical plant using discrete event simulation

This paper describes a debottlenecking study for different products in a chemical plant of The Dow Chemical Company. We used discrete event simulation to represent the chemical plant operations and to identify individual processes that limit the plant production. Our analysis successfully identified different bottlenecks for each product. The simulation will be used in future evaluations of the costs and benefits of different solutions identified for validated root causes. The simulation captures plant dynamics and can be easily leveraged to other improvement opportunities in the plant with no to little customization. In this paper, we present the general approach used for identifying the bottlenecks and the analysis results.

A DISCRETE EVENT SIMULATION MODEL FOR RELIABILITY MODELING OF A CHEMICAL PLANT

ABSTRACT This paper discusses a discrete event simulation model developed to identify and understand the impact of different failures on the overall production capabilities in a chemical plant. The model will be used to understand key equipment components that contribute towards maximum production loss and to analyze the impact of a change policy on production losses. A change policy can be classified in terms of new equipment installation or increasing the stock level for the failure prone components. In this paper, we present the approach used and some preliminary results obtained from available data. INTRODUCTION Chemical plant operations typically consist of a large number of components with complex interactions and numerous failure modes. For plants that are running at a "soldout" capacity, downtime means significant production and sales losses. To run the plant with minimum downtime, it is necessary to understand the critical components within the plant and implement new components, inventory control and preventive maintenance policies for the critical components. This paper discusses a discrete event simulation model being developed to understand and identify key failure components for a chemical plant. The chemical plant considered here produces more than 15 different types of products, consists of ~40 different subsystems (such as reactors, wash tanks, refining system) and there are more than 250 different types of component failures (based on historical data), which occur in different subsystems. Based on historical data, 36% of the production losses were due to equipment failures. To maximize the plant production, a study is being carried out to identify critical subsystems and their individual components that contribute towards significant production loss. This study will also help in understanding the effect of change policies in terms of new component installation and inventory control policies for reduction in production loss. The DES modeling of this chemical plant operation presents challenges as it involves both continuous and discrete flow of material in the plant. A barrier to successful execution of a study like this is scenario overload. To efficiently execute the key task of identifying the critical components, we designed a systematic approach. After model verification and validation, the simulation model will be first used to see a "base case" production against different products without any failures. This step will define the maximum attainable production for each product without failures. The model will then be run by considering failures for a particular subsystem (for instance, reactor system). After running the simulation model by considering failures in each subsystem, a Pareto analysis will be carried out to determine which subsystems are critical. Within each subsystem, the individual components will then be evaluated to identify components causing more frequent and costly downtimes. These components can then be analyzed for change policies such as implementing new designs or changing the inventory control policies. This systematic approach examines at the system hierarchy from the outside in, instead of an exhaustive search considering each failure. This reduces the number of possible simulation scenarios and generates data that are easier to understand and evaluate. The paper is organized as follows. Section 2 provides a brief overview of the production process. Section 3 describes the DES simulation modeling approach, section 4 provides the preliminary results and section 5 presents the summary and future work for this project. PROCESS OVERVIEW The operations of the chemical plant being considered can be subdivided into following main steps. Note the combination of discrete (batch) and continuous processing steps. 1. Raw product loading (discrete) 2. Raw product mixing (discrete) 3. Reaction (discrete) 4. Intermediate storage 1 (discrete) 5. Raw product washing-(continuous