Rule-Based Systems

Over the last few years, the benefits of applying ontologies (semantic graph modelling) for Architecture, Engineering, Construction and Facility Management (AEC/FM) industry have been recognized by several researchers and industry stakeholders. One of the main motivations is because it eases AEC data manipulation and representation. However, a research question that still remains open is how to take advantage of semantic web technologies to interoperate the AEC/FM and other ontologies in a flexible and dynamical way in order to solve data structure heterogeneity problem. Because of this, we propose in this paper to apply a rule-based federated architecture to answer this research question.

Frequent-Pattern Tree (FP-Tree) algorithm plays a vital role in mining associations, patterns and other data mining related jobs. Currently a software risk mitigation intelligent decision network engine using rule based technique has been designed against software risk factors. A novel idea of finding the associations between different software risk mitigation factors using FP-Tree algorithm has been proposed in this research work. Huge volumes of software risk factors and software risk mitigation factors exist in software development industry. That’s why data mining researchers have been attracted towards it to find something to explore. Software risk mitigation frequent patterns will be helpful for the project managers and software developers to make decisions against the software risk factors.

The progress of information and communication technologies has greatly increased the quantity of data to process. Thus, managing data heterogeneity is a problem nowadays. In the 1980s, the concept of a Federated Database Architecture (FDBA) was introduced as a collection of components to unite loosely coupled federation. Semantic web technologies mitigate the data heterogeneity problem, however due to the data structure heterogeneity the integration of several ontologies is still a complex task. For tackling this problem, we propose a loosely coupled federated ontology architecture (FOWLA). Our approach allows the coexistence of various ontologies sharing common data dynamically at query execution through logical rules. We have illustrated the advantages of adopting our approach through several examples and benchmarks. We also compare our approach with other existing initiatives.

When and why do powerful countries seek to enact major changes to international order, the broad set of rules that condition behavior in world politics? This question is particularly important today, as Donald Trump's apparent disregard for the liberal international order and uncertainty over what China might seek to replace it with mean that queries about great power motives vis-à-vis order will remain at the forefront of debates over the future of world politics. In seeking to explain this phenomenon, prior studies have focused on the consensus-driven and inclusive origins of international orders. By contrast, I argue in this book that the propelling motivation for great power order building at important historical junctures has most often been exclusionary, centered around combatting other actors rather than cooperatively engaging with them. My core contention is that dominant actors pursue fundamental changes to order only when they perceive a major new threat on the horizon, a threat to their security or to their enduring primacy. When these actors-the great powers of world politics-seek to enact fundamentally new order principles, they do so for the purpose of targeting this perceived threat, be it another powerful state, a contrary alliance or a foreboding ideological movement. The goal of order building, then, is weakening, opposing and above all excluding that threatening entity from amassing further influence in world politics. Far from falling outside the bounds of traditional statecraft, order building is, to paraphrase Clausewitz, the continuation of power politics by other means.

and watch. During the top) service has seen an exponential a growing activity and on this field. In past few Paper, human emotions to provide dynamic content recommen deep list of movies overcome problem in existing method. Keywords When television first came player emerge in the market. with the introduction of the Netflix, Amazon Prime and many other OTT(Over the top) media around provides the has changed the way users interact with video content. films and human emotions. Emotions neurophysiologic pleasure or displeasure. The recent advancement in machine learning, Computer vision and recommendation has achieved studies on computer vision, there is a possibility of improving the quality of Recommendation System whose primary goal is to predict predictions we decide whether to learning have become very popular target their content to a specific audience. they can d The motivation in this work to use emotion technique in recommendation system to generate additional input movie recommendation system a system,

The Internet of things is a platform, which connects and controls the real word objects through a wireless network. This paper presents a novel approach to collect the data from the sensors, which resides on a Mechatronic devices. Using communication protocols these devices are integrated with the cloud based IoT data logger. There will be a communication between the data logger and the server. Android based application developed will monitor the action of devices.

It takes the material in the form of global environmental issues such as human relationships with the environment, ecological principles, natural resources, environmental pollution, environmental ethics, and environmental impact analysis (AMDAL) (environmental impact assessment). This research aims to develop videos for the learning of civil engineering students to increase sensitivity to the environment. This research is development research with 4D model design, which is carried out up to the 3rd stage of the four existing steps: define, design, develop, and disseminate. The research object is a video-based learning medium in environmental science courses, and the research subjects are three validators and 30 students who program Environmental Science courses. Research instruments consist of 1). Validation sheet to measure the feasibility of video-based media containing 15 statement items with preferred Likert scale scores of 1 to 5 and score ranges of 1-75;  2). Test of learning results to measure the feasibility of video-based media after learning containing 20 questions with a score of 0 or 1 and a score range of 0-100. The results showed: first, video-based learning media used in learning activities in Environmental Science courses are categorized as very feasible. The assessment results of the three validators showed that the average value of video-based learning media eligibility of 4.73 with very proper wording. Details of aspects of the assessment include the presentation of material with an average score of 4.78, narrative quality of 4.67, writing the balance of 4.78, quality of music (back sound) of 4.56, the harmony of the colour of the average score of 4.89. Second, after applying video-based learning media in Environmental Science courses, student learning outcomes show "excellent" criteria. The average score of 30 students is 80.17, indicating that the video-based learning media in Environmental Science courses is feasible.

With the urban and industrial growth, many evolving countries suffer from excessive air pollution. The growing concern about air pollution has been raised by the government and people because it affects individual's health and sustainable development globa interpose and forecast air pollution for the whole city. for air quality forecasting based on 1D CNN, Bi the PM2.5 dataset taken from UCI Machine Learning Repository. traditional deep learning models,

Because of the fast-growing demands in
automated document dispensation, a steadfast
system for automatic identification of
keywords entrenched in an electronic
document is of immense concern. The paper
envisaged an innovative approach for the
classification of multiple Expert System (ES)
methodologies at a time on the basis of
keyword extraction using a commercial text
mining tools WordStat and Compare-Suite
Pro. These ES methodologies include eleven
categories that include; rule-based systems,
database methodology ,case-based reasoning,
intelligent agent, knowledge-based systems,
fuzzy based expert system, object oriented
methodology, neural networks, system
architecture, systems, modelling, and
ontology. The keywords are selected on the
basis of frequency analysis and position of
most recurring word in context within the
article tile, abstract and keywords of respective
ES methodology. Based on the extracted
keywords, an inference engine has been
designed on java software. This software
compares the keyword established from the
articles of individual ES methodology with all
other articles of the remaining methodologies
using generation of association rules. The
inference engine developed was first calibrated
for 100 articles out of 160 and then validated
for remaining 60 articles. The validation
results shows accuracy of the experimental
results up to 85 percent. The paper concludes
that the classification of Expert Systems using
keyword extraction technique, outperforming a
base line, is a more accurate, reliable and
optimal with respect to time as compared to
other orthodox methods of text mining. At the
end it has been concluded that the techniques
may further improved by limiting design
constraints in the tools adopted in the research
for future endeavours.

Institutional change explains the change of institutions considered as rules and expectations that govern human interactions and paths of development in society. This conceptual paper describes, with an historical perspective, the most important definitions of institution and of institutional change and shows some theories that analyze these critical topics in economics to assess similarities and differences. The future challenge of institutional scholars concludes this study.

This research practically demonstrates how to use data mining technology to supply knowledge to the rule based system. It lays down a framework for utilization of data mining concepts to provide a sustained supply of knowledge to a rule based system at runtime. A novel ‗record couple‘ based production rule mining algorithm has been proposed to extract production rules from large datasets. Production rules thus extracted are added to the knowledge base of a rule based system.
Conventional programming techniques are not efficient for performing knowledge oriented data consistency checks, especially in the domain involving large body of regularly updating knowledge. ‗Rule based systems‘, an established technique of Artificial Intelligence (AI), is well suited to perform data consistency checks on large datasets and perform corrective measures accordingly. A problem that limits the use of rule based systems is their implementation environment, which differs from data storage environment of the business world. Rule based systems and other AI related systems are normally developed in their specific environments such as Prolog, LISP, OPS-5 etc., while data of the business world is stored in relational environment.
This research proves that rule based programming is more efficient than conventional programming techniques. Further, to increase the utility of rule based systems, relational database environment has been used for implementation. A Structured Query Language (SQL) based representation of production rules has been proposed. Rule based engine has been designed, developed, and evaluated in relational database environment.
Moreover, the problem of knowledge acquisition has been persistent in the area of rule based systems. Manual knowledge editing techniques have been used so far to add knowledge to rule based systems. Although machine learning techniques have been used to overcome knowledge acquisition bottlenecks, yet limited success has been reported so far. In recent years, data mining has emerged as a useful technique to solve knowledge acquisition problems. Most of the data mining work produces very useful information for business executives and decision makers; however it leaves to the choice of decision makers either to use it or to disregard that information which limits the utilization of data mining technology considerably. Additionally, the area of ‗production rule mining‘ is not adequately explored by the data mining research community and often it is mixed up with ‗association rule mining‘.
A system integrated with machine learning module is termed as ‗Learning Apprentice System‘. Configuration of the system emerging as result of this research is termed as the ‗architecture of a learning apprentice system‘ as it involves a ‗rule based system module‘ integrated with ‗data mining based learning module‘. ‗Production rule mining algorithm‘ and ‗feeding the extracted production rules to a rule based system designed in relational database environment‘ are novel contributions of this research.
Medical billing compliance has been used as testing domain for concepts and ideas developed during this research. The rule based system is populated with the knowledge of medical claim processing rules using a knowledge editor. The proposed production rule mining algorithm has been utilized to mine medical claim processing rules which are then applied by the system to scrub medical claims i.e. detecting errors and performing minor, legitimate, and corrective actions.
Performance of the proposed system has been proved to be efficient and has problem-solving ability as compared to the systems based on conventional programming. Conventional programming based systems are good enough to perform small and simple checks, however where complex and knowledge oriented checks are involved, the techniques proposed have proven to be much better. Evaluation of data mining driven rule based learning apprentice system proved that a lot of time were saved by prompt identification of knowledge oriented data consistency errors from the medical billing data.
Although the current system has been tested in medical billing compliance domain, it can be applied to many real life problem domains which involve large numbers of knowledge oriented data consistency checks, such as credit card processing system, loan approval system, examination system etc.

Fossil fuels are a non automobiles is using electricity which can be generated by renewable resources to power up the vehicle. This paper gives an idea about charging these Elec using solar panels and provide the maximum power generated. The proposed system is controlled using the Arduino UNO to view the amount of charge generated through an LCD. GSM modules an overcharge or undercharges. A rain sensor is also integrated to alert the user if there are any changes in climatic conditions that affect the charging of PV cells. The entire status of the battery along with the amount o transferred is uploaded to the cloud and can be seen through a webpage at any time. The main aim of this paper is in reducing the consumption of non Keywords: based; iot; charging As the population increases, are dependent on fossil fuels such as petrol, diesel, LPG, etc. Fossil fuels are a non time use. To find an alternative energy resource th vehicle engine development have been conducted, one such energy source is electrical energy. After successful development across years, vehicles powered by electric energy are becoming t of its many advantages. As such charging stations for electric vehicles are in need. To accommodate easy access, we have designed a solar-powered charging station. Solar energy is not only a renewable energy resource but to obtain from Sun. [14] [15]. Electric vehicles withdraw the required current to power up using the rechargeable batteries that are integrated with it. Around 1832, one of the very basic electrically running motor vehicles was invented by Robert Anderson, and later time first commercia investing more to develop e reasons why future generations will start using EVs mainly becaus any smoke or toxic gases to the environment. They are very quiet in nature and use renewable sources of energy which decreases the use of fossil fuels. Overall, the advantage of using EVs is more to the envi are using an Arduino controller to make the system more cost system applications wider and mobile.

This article intends to explain how Fi Ra Wali (read: my sago my life) can be used as a culture-based community development strategy in defending the sago forest as a local community commodity from the threat of a global crisis. Interviews and literature studies were used to obtain data. Fi ra wali as a self-concept of the Sentani community has local values that are used to strengthen the existence of socio-culturalism in the face of modernization. This article reveals that in Fi Ra Wali some social norms and rules are built spontaneously to create a culture of obedience, discipline, respect, and social awareness in maintaining their local wisdom. Fi Ra Wali is a manifestation of hidden rationality, as a manifestation of hidden knowledge that shows the strength of a traditional society that is resistant. This article concludes that Fi Ra Wali is a culturally specific strategy that has a safety value in every activity, to maintain the continuity of life, not only in the present but also in the future. Keywords—Cultural Development, Modernization, Fi Ra Wali, Sago Forest, Safety Value

One of the most important issues on information systems (IS) research is the need to align business with information systems and information technology (IT). Since information systems facilitate the success of business strategies, the importance of business-IT (or strategic) alignment is unquestionable. While organisations address alignment achievement, they are continually suffering from misalignments. These difficulties (the misalignments) encumber the achievement of alignment and lead us to the phenomenon of misalignment. This Ph.D. dissertation deals with the concept of misalignment, with special attention on enterprise architecture (EA)-based analytical potential. In the following study, the problem of business-IT alignment will be translated into the aspects and concepts of enterprise architecture. The main purpose of the proposed research is to analyse strategic misalignment between the business dimension and the information systems dimension. In this Ph.D. dissertation, an analytical solution will be built to approach the topic of strategic alignment from an EA-based perspective. The study aims to accomplish an EA-based, systematic analysis of mismatches between business and information systems. The operation, the correctness, as well as the relevance of the framework will be validated via a case study. The contribution of the proposed study lies in connecting typical misalignment symptoms to relevant EA analysis types along traditional alignment perspectives. The significance of the proposed research is the clear and accurate compound of research methods and implementation instruments to approach EA-based misalignment symptom detection. The results of the proposed research will contribute to alignment assessment by expanding the ways of addressing alignment problems. The proposed research framework has the potential to extend our understanding on assessing the state of misalignment in a complex EA model structure.

ATMs (Automatic Teller Machine) are becoming widely used in today's world because they are easy to use and offer convenience. However, today's ATM technology comes with some security risks such as the theft and misuse of cards, the possibility of losing a card, and forgetting PIN numbers. A growing number of frauds committed against automatic teller machines over the last decade motivated us to use biometrics to ensure the accuracy and security of personal identification. In this paper, we describe a biometric fingerprint and face recognition system that can replace ATM cards and PINs. The transaction is successful when fingerprint, OTP, and face match. If the fingerprint does not match, the customer is guided to enter the aadhaar number. Similarly, if face matching fails, the customer is given an option to answer the question. Here aadhaar number and correct answer to the question must be entered correctly to proceed. Aadhaar number entry and question & answer options are mainly provided for those who are unable to access the system because of damage to their physical attributes (finger and face).

Professional regulatory bodies - including ethics committee, association boards, and government licensing authorities - oversee the ethical behaviour of professionals, specifically monitoring their use of power. However there is relatively less scrutiny regarding the use of power by such regulatory bodies in the consideration of grievances against professionals. The quasi-legal nature of their administration of justice is questioned here, and significant limitations in the implementation of administrative law are addressed. The use of adversarial models of investigation is called into question, and other possibilities are outlined The shortcomings of the ‘crime and punishment’ model are outlined, and a number of alternatives are examined. The foundation of professional ethical codes are addressed and critiqued.

Rules and frames are two knowledge representation schemes whose strengths and weaknesses are complementary
to each other. Although several systems have attempted to integrate the two, few efforts have been made to incorporate the classification reasoning of the frame representations into the rule-based systems. To achieve a deep integration of the two schemes, we have developed and implemented a
classification-based programming paradigm where the rules' LHS and the functionalities of their RHS are
represented in the terminological space. The major processes of he architecture consist of a pattern matcher that is driven by semantic representations rather than by structural patterns, a rule base organizer  that classifies rules and rule classes into a taxonomy, a conflict set manager that filters rules, and a rule
interpreter that selects and executes rules. The architecture facilitates the representation of control knowledge by inferring the specificity of rules and by inheriting the control strategies from rule classes.

The paradigm not only enhances the reasoning capabiities of rule-based systems, but also encourages explicit representation of various kinds of knowledge implicit in rules so that they can be shared by different domains and used for multiple purposes.

This thesis addresses philosophical problems concerning improper
assertions. The first part considers the issue of defining lying: here,
against a standard view, I argue that a lie need not intend to deceive
the hearer. I define lying as an insincere assertion, and then resort
to speech act theory to develop a detailed account of what an
assertion is, and what can make it insincere.
Even a sincere assertion, however, can be improper (e.g., it can be
false, or unwarranted): in the second part of the thesis, I consider
these kinds of impropriety. An influential hypothesis maintains that
proper assertions must meet a precise epistemic standard, and
several philosophers have tried to identify this standard. After
reviewing some difficulties for this approach, I provide an innovative
solution to some known puzzles concerning this issue. In my view,
assertions purport to aim at truth, but they are not subject to a norm
that requires speakers to assert a proposition only if it is true.

On Bernard Suits’s celebrated analysis, to play a game is to engage in a “voluntary attempt to overcome unnecessary obstacles”. Voluntariness is understood in terms of the players having the “lusory attitude” of accepting the constitutive rules of the game just because they make possible playing it. In this paper I suggest that the players in Netflix’s hit show Squid Game play the ‘squid games’, but they don’t do so voluntarily, but are forced to play. I argue that this means that we should rethink Suits’s analysis by claiming that all that is needed for players to play is that someone has put the rules in force for them for the reason that it makes playing the game possible. I then illustrate the virtues of the revised analysis by showing how it helps us to defuse Hurka’s recent counterexamples to Suits’s view.

Tempo rating which aims to obtain the accurate tempo for the workers is an important process for time study applications. It is possible to reach the normal time after finding the precise tempo values. Westinghouse method, based on the factors of skill, effort, environmental conditions and consistency, can be considered as one of the well-known methods among tempo rating approaches. However, crisp numerical values with linguistic terms are used for evaluating each factor in Westinghouse method. In this study, a fuzzy rule based methodology is proposed due to the vague environment of decision making process of tempo rating. The developed methodology includes fuzzy rules for determining the evaluation scores of each factor. The proposed rating system is applied in a bus-bar production system so as to demonstrate its validity. The proposed rating system yields high level of conciseness on the basis of predetermined motion time system. Significance: Since time study activities are performed in all the industries including every sector, tempo rating which is critical for finding the normal time of the tasks is very important and has a wide range of industrial applications. With this study, a new tempo rating methodology based on fuzzy rule based system is presented. The proposed methodology provides easiness to use and produces accurate values.

Software  industry  has  been  rapidly  growing  from the last couple of decades. Due to this growth and advancement
many  issues  are  going  to  be  occurred.  There  are  number  of factors  that  affect  the  whole  software  development  process. This  research  has  focused  on  the  most  important  risk  factors that  affect  the  overall  software  project  and  risk  mitigation reviewed from the literature and my own experience. The main aim of this research is to collect the most important risk factors and risk mitigation for the development of intelligent network using an Artificial Intelligence technique known as Rule Based System.  This  is  helpful  for  the  project  managers  to  make decisions.  For  the  accomplishment  of  this  task  Firstly  an exploratory survey has been conducted to prioritize the twenty requirements  and  fifty  software  risk  mitigation  factors.  A sample of 200 respondents is selected out of which 100 are the university students and 100 are the  IT professionals of  a well reputed  software  house.  Secondly  a  Rule  Based  technique  is used to generate the risk mitigation intelligent network. Rules have been stored in the form of risk mitigation in relationship with  risk  factors.  The  system  follows  five  steps  of  execution. First  of  all  risk  factors  has  been  prioritized  based  upon  the associations  of  risk  factors  and  risk  mitigation.  Secondly,  it creates  new  relationships  between  them.  Thirdly,  enter  these relationships asrules into the Knowledge Base or Rule Base. At
the fourth stage, an RBS engine executes these rules and finally
an intelligent risk mitigation network is generated.

This article tries to explain our rule-based Arabic Named Entity recognition (NER) and classification system. It is based on lists of classified proper names (PN) and particularly on syntactico-semantic
patterns resulting in fine classification of Arabic NE. These patterns use syntactico-semantic combination of morpho-syntactic and syntactic entities. It also uses lexical classification of trigger words and NE
extensions. These linguistic data are essential not only to name entity extraction but also to the taxonomic classification and to determining the NE frontiers. Our method is also based on the contextualisation and
on the notion of NE class attributes and values. Inspired from X-bar theory and immediate constituents, we built a rule-based NER system composed of five levels of syntactico-semantic combination. We also show how the fine NE annotations in our system output (XML database) is exploited in information retrieval and information extraction.

Most of the data mining projects generate information (summarized in the form of graphs and charts) for business executives and decision makers; however it leaves to the choice of decision makers either to use it or disregard it. The manual use of the extracted knowledge limits the effectiveness of data mining technology considerably. This chapter proposes an architecture, in which data mining module is utilized to provide continuous supply of knowledge to a rule based expert system. Proposed approach solves the knowledge acquisition problem of rule based systems and also enhances effective utilization of data mining techniques (i.e. by supplying extracted knowledge to rule based system for automated use). The chapter describes the details of a data mining driven rule based expert system applied in medical billing domain. Main modules of the system along with the final analysis of performance of the system have also been presented.

Chapter 31, p. 669 - 692, of vol. III, tome 2 of the General History of Latin America, "Consolidación del orden colonial",published by the UNESCO, dealing with the institutional mechanisms of exercising power and solutions of conflicts within the consolidated colonial order established by Spanish Authorities Overseas. After a short introduction to the development of the historiography about administration and bureaucracy the contribution confronts an outline of idiosyncracy of the conquerors and crown's policy to impose its authority, following in general terms the methods used by early modern monarchies in Europe.- 6 illustrations on types of municipal palaces.

Development of software is a very crucial and complex process. IT professionals have been facing difficulties and risks for the development of software projects. This research focuses on the effectiveness of two data mining algorithms that are Apriori algorithm and FP-Tree Algorithm in Software Engineering domain. These two algorithms have shown the capability of generating association rules in software engineering data. Associations between software risk factors and risk mitigation have been found using these algorithms which is a unique idea. Both the algorithms have different methodologies but with the same goals in the form of rules generation. This research paper targeted three things. Tracing of these two algorithms applied to the given dataset, Comparison in terms of pros and cons of both the algorithms and the combination of the two, novel adaptive architecture in a clear way. Embedding data mining techniques in software engineering have shown spark and generates fruitful results.

We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global modelling perspective. First, we formulate the broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables, and that includes traditional top-1 subgroup discovery in its definition. This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Second, as finding optimal subgroup lists is NP-hard, we propose SSD++, a greedy heuristic that finds good subgroup lists and guarantees that the most significant subgroup found according to the MDL criterion is added in each iteration, which is shown to be equivalent to a Bayesian one-sample proportions, multinomial, or t-test between the subgroup and dataset marginal target distributions plus a multiple hypothesis testing penalty. We empirically show on 54 datasets that SSD++ outperforms previous subgroup set discovery methods in terms of quality and subgroup list size.

In the middle of the 20th century, it was a common Wittgenstein-inspired idea in philosophy that for a linguistic expression to have a meaning is for it to be governed by a rule of use. In other words, it was widely believed that meanings are to be identified with use-conditions. However, as things stand, this idea is widely taken to be vague and mysterious, inconsistent with “truth-conditional semantics”, and subject to the Frege-Geach problem.

In this paper I reinvigorate the ideas that meaningfulness is a matter of being governed by rules of use and that meanings are best thought of in terms of use-conditions. I will do this by sketching the Rule-Governance view of the nature of linguistic meaningfulness, showing that the view isn’t by itself subject to the two problems, and explain why the idea has had a lasting appeal to philosophers from Strawson to Kaplan and why we should find it continually attractive.

The use of AI technologies at BHP Stainless, has enabled the development of the Adaptive Guidance System, or AGS. This system, based on an earlier successful pilot project, applies expert knowledge in the generation of thickness reduction schedules and corresponding mill setups , for the cold rolling of stainless steel strip. Considerable process management and control skill is usually associated with the production of high quality stainless material. Since its installation in June 93, the AGS system has made a significant impact on the diagnosis, quality and production of stainless material, as well as, customer support and ongoing product improvement. Traditional knowledge acquisition work with key rolling operators plus the extensive use of machine-learning has lead to the formation of the several rule based models that guide the detailed schedule generation and mill setups .