OPTIMAL PREVENTIVE MAINTENANCE POLICIES FOR UNRELIABLE QUEUEING AND PRODUCTION SYSTEMS

Preventive Maintenance (PM) models have traditionally concentrated on utilizing machine ``technical" state information such as the degree of deterioration. However, in real manufacturing systems, additional system operational information such as work-in-process (WIP) inventory levels critically impact actual PM decisions. Surprisingly, the literature on models incorporating this important aspect is relatively sparse. This thesis attempts to fill some of the research gaps in this area by considering problems of optimal preventive maintenance explicitly under the context of unreliable queueing and production-inventory systems. We propose a two-level hierarchical modeling framework for PM planning and scheduling problems. In the higher level, our objective is to characterize structure of optimal PM policies. We start with a simple case in which queueing is not taken into account in the model. We show that a randomized PM policy, like the widely used ``time-window" policy in industry, is in general not optimal. We then consider the problem of optimal PM policies for an M/G/1 queueing system with an unreliable server. The decision problem is formulated as a semi-Markov decision process. We establish some structural properties, e.g., ``control-limit" type structure, that optimal policies will satisfy. We then take the optimal PM problem a step further by considering optimal joint PM and production control policies for unreliable production-inventory systems with time-dependent or operation-dependent failures. We show the optimal joint policies retain the ``control-limit" type structure in terms of the PM portion of the policy. For the production portion of the policy, some properties are also derived, but numerical studies show that in general optimal policies have more complicated structure than the simple control-limit form. The last part of the thesis is devoted to the lower level of the framework where the objective is to optimally schedule multiple PM tasks across a group of tools. We take into account information such as interdependence of PM tasks, WIP data and resource constraints, and formulate the problem as a mixed-integer program. Results of a simulation study comparing the performance of the model-based PM schedule with that of a baseline reference schedule are presented to illustrate the fitness of our solutions

The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling

The goal of this paper is to determine if the results for dynamic job-shop scheduling problems are affected by the assumptions made with regard to the processing time distributions and the scheduler's knowledge of the processing times. Three dynamic jobshop scheduling problems (including a two station version of Conway et al.'s [2] nine station symmetric shop) are tested under seven different scenarios, one deterministic and six stochastic, using computer simulation. The deterministic scenario, where the processing times are exponential and observed by the scheduler, has been considered in many simulation studies, including Conway et al's. The six stochastic scenarios include the case where the processing times are exponential and only the mean is known to the scheduler, and five different cases where the machines are subject to unpredictable failures. Two policies were tested, the shortest expected processing time (SEPT) rule, and a rule derived from a Brownian analysis of the corresponding queueing network scheduling problem. Although the SEPT rule performed well in the deterministic scenario, it was easily outperformed by the Brownian policies in the six stochastic scenarios for all three problems. Thus, the results from simulation studies of dynamic, deterministic job-shop scheduling problems do not necessarily carry over to the more realistic setting where there is unpredictable variability present

Novel availability and performance ratio for internal transportation and manufacturing processes in job shop company

Purpose: Purpose of this study includes the quantification of the impact of transportation efficiency onto the workstations the transportation serves in term of throughput and total lead time elapsed by product. Besides, it aims to synchronize the capacity available among workstations throughout a production line by studying the upper limit of throughput could be afforded by each workstation as well as their connection with each other. This study is also done on the purpose of promoting fulfillment of customer demand at shorter delivery time and minimal equipment utilization. Investigation on implementation of Overall Equipment Effectiveness (OEE) in an aerospace part-manufacturing company is studied to track out the potential opportunities to be improved. Design/methodology/approach: Site observation is conducted on all the five manufacturing workstations in the aforementioned aerospace part manufacturing company. Time data of both automated processes and manual processes are collected and they are used to construct simulation model. From that, various scenarios of transportation efficiency are simulated in Experiment 1. In addition, Experiment 2 is also set to examine the maximum capacity of each workstation. All of these are to highlight the relationship between workstation and processes and to verify the condition of imbalanced capacity among workstations in the company. In short, this has necessitated the integration of workstation and transportation activities within the company. These are followed by proposal of measures to quantify the wastes identified. Findings: The paper finds that implementation of OEE alone does not consider the reasonability of customer demand fulfillment. The results show that both transportation efficiency and imbalanced capacity throughout production system are not emphasized by OEE implementation in the case company. Therefore, responsibility of all workstations and transportation process in delivering demand on time are quantified. Transportation process which serves as the connectors of manufacturing processes is quantified and monitored by proposed Transportation Measure (TM) whereas workstations are measured using novel availability and performance ratio. Research limitations/implications: Future research should be conducted to examine the impact of other station within a company such as warehouse and logistic department to the performance of equipment and materials in manufacturing workstation. Besides, the material availability as well as the skills or performance of man power could be further incorporated into the measures to consider all the entities involved in manufacturing processes. Practical implications: The proposed availability and performance ratio for both transportation and manufacturing processes, which are related to each other, help in promoting better effectiveness of production system in terms of production amount and lead time. Besides, reasonable utilization equipment and minimal consumption of material are incorporated in the measures to promote Lean way in fulfilling customer demand. The effectiveness of entire production line is examined as a unity with joint responsibility under varying transportation efficiency and cycle time of each workstation. Both measures could be implemented together to optimize the production system and quantify the hidden wastes which are neglected in the OEE implementation. Originality/value: The novel availability and performance ratio are proposed to consider customer demand, historical equipment utilization and Takt time of each workstation to examine the possibility and reasonability of demand fulfillment. This prevents both over-processing and overproduction issues which are invisible in OEE. Furthermore, delay propagation throughout production system and interrelationship between processes are quantified under transportation measure. Other novelty of the paper is that it monitors the waiting time and lead time spent in each workstation at the same time considering utilization of workstation. The proposed Transportation Measure (TM) aims to reduce the queue length and waiting time at destination workstation at minimal utilization of forklift. It also promotes less capacity investment in transportation and prioritizes its scheduling according to urgency of destination workstation.Peer Reviewe

A tactical planning model for a job shop with unreliable work stations and capacity constraints

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1988.Bibliography: leaf 54.by Shoichiro Mihara.M.S

Modeling of RFID-Enabled Real-Time Manufacturing Execution System in Mixed-Model Assembly Lines

To quickly respond to the diverse product demands, mixed-model assembly lines are well adopted in discrete manufacturing industries. Besides the complexity in material distribution, mixed-model assembly involves a variety of components, different process plans and fast production changes, which greatly increase the difficulty for agile production management. Aiming at breaking through the bottlenecks in existing production management, a novel RFID-enabled manufacturing execution system (MES), which is featured with real-time and wireless information interaction capability, is proposed to identify various manufacturing objects including WIPs, tools, and operators, etc., and to trace their movements throughout the production processes. However, being subject to the constraints in terms of safety stock, machine assignment, setup, and scheduling requirements, the optimization of RFID-enabled MES model for production planning and scheduling issues is a NP-hard problem. A new heuristical generalized Lagrangian decomposition approach has been proposed for model optimization, which decomposes the model into three subproblems: computation of optimal configuration of RFID senor networks, optimization of production planning subjected to machine setup cost and safety stock constraints, and optimization of scheduling for minimized overtime. RFID signal processing methods that could solve unreliable, redundant, and missing tag events are also described in detail. The model validity is discussed through algorithm analysis and verified through numerical simulation. The proposed design scheme has important reference value for the applications of RFID in multiple manufacturing fields, and also lays a vital research foundation to leverage digital and networked manufacturing system towards intelligence

Application of Reinforcement Learning to Multi-Agent Production Scheduling

Reinforcement learning (RL) has received attention in recent years from agent-based researchers because it can be applied to problems where autonomous agents learn to select proper actions for achieving their goals based on interactions with their environment. Each time an agent performs an action, the environment¡Šs response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent¡Šs goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. The objective of this research is to develop a set of guidelines for applying the Q-learning algorithm to enable an individual agent to develop a decision making policy for use in agent-based production scheduling applications such as dispatching rule selection and job routing. For the dispatching rule selection problem, a single machine agent employs the Q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. In the job routing problem, a simulated job shop system is used for examining the implementation of the Q-learning algorithm for use by job agents when making routing decisions in such an environment. Two factorial experiment designs for studying the settings used to apply Q-learning to the single machine dispatching rule selection problem and the job routing problem are carried out. This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling