Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem

A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem

A discrete differential evolution algorithm for the permutation flowshop scheduling problem

Makespan minimization in permutation flow-shop scheduling is an operations research topic that has been intensively addressed during the last 40 years. Since the problem is known to be NP-hard for ...

A review and classification of heuristics for permutation flow-shop scheduling with makespan objective

We present the first coupled formal and empirical analysis of the Satellite Range Scheduling application. We structure our study as a progression; we start by studying a simplified version of the problem in which only one resource is present. We show that the simplified version of the problem is equivalent to a well-known machine scheduling problem and use this result to prove that Satellite Range Scheduling is NP-complete. We also show that for the one-resource version of the problem, algorithms from the machine scheduling domain outperform a genetic algorithm previously identified as one of the best algorithms for Satellite Range Scheduling. Next, we investigate if these performance results generalize for the problem with multiple resources. We exploit two sources of data: actual request data from the U.S. Air Force Satellite Control Network (AFSCN) circa 1992 and data created by our problem generator, which is designed to produce problems similar to the ones currently solved by AFSCN. Three main results emerge from our empirical study of algorithm performance for multiple-resource problems. First, the performance results obtained for the single-resource version of the problem do not generalize: the algorithms from the machine scheduling domain perform poorly for the multiple-resource problems. Second, a simple heuristic is shown to perform well on the old problems from 1992; however it fails to scale to larger, more complex generated problems. Finally, a genetic algorithm is found to yield the best overall performance on the larger, more difficult problems produced by our generator.

/pdf/scheduling-space-ground-communications-for-the-air-force-wepttih10j.pdf

Scheduling Space–Ground Communications for the Air Force Satellite Control Network

The use of random test problems to evaluate algorithm performance raises an important, and generally unanswered, question: Are the results generalizable to more realistic problems? Researchers generally assume that algorithms with superior performance on difficult, random test problems will also perform well on more realistic, structured problems. Our research explores this assumption for the permutation flow-shop scheduling problem. We introduce a method for generating structured flow-shop problems, which are modeled after features found in some real-world manufacturing environments. We perform experiments that indicate significant differences exist between the search-space topologies of random and structured flow-shop problems, and demonstrate that these differencescan affect the performance of certain algorithms. Yet despite these differences, and in contrast to difficult random problems, the majority of structured flow-shop problems were easily solved to optimality by most algorithms. For the problems not optimally solved, differences in performance were minor. We conclude that more realistic, structured permutation flow-shop problems are actually relatively easy to solve. Our results also raise doubts as to whether superior performance on difficult random scheduling problems translates into superior performance on more realistic kinds of scheduling problems.

Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance

AFSCN scheduling: How the problem and solution have evolved

Test suites for many domains often fail to model features present in real-world problems. For the permutation flow-shop sequencing problem (PFSP), the most popular test suite consists of problems whose features are generated from a single uniform random distribution. Synthetic generation of problems with characteristics present in real-world problems is a viable alternative. We compare the performance of several competitive algorithms on problems produced with such a generator. We find that, as more realistic characteristics are introduced, the performance of a state-of-the-art algorithm degrades rapidly: faster and less complex stochastic algorithms provide superior performance. Our empirical results show that small changes in problem structure or problem size can influence algorithm performance. We hypothesize that these performance differences may be partially due to differences in search space topologies; we show that structured problems produce topologies with performance plateaus. Algorithm sensitivity to problem charaeteristics suggests the need to construct test suites more representative of real-world applications.

/pdf/algorithm-performance-and-problem-structure-for-flow-shop-2572mftx3d.pdf

Algorithm performance and problem structure for flow-shop scheduling

Three algorithms are tested on the satellite range scheduling problem, using data from the U.S. Air Force Satellite Control Network; a simple heuristic, as well as local search methods, are compared against a genetic algorithm on old benchmark problems as well as problems produced by a generator we recently developed. The simple heuristic works well on the old benchmark, but fails to scale to larger, more complex problems produced by our generator. The genetic algorithm yields the best overall performance on larger, more difficult problems.

/pdf/satellite-range-scheduling-a-comparison-of-genetic-heuristic-2jj1or5se8.pdf

Laura Barbulescu

Papers

Scheduling Space–Ground Communications for the Air Force Satellite Control Network

Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance

AFSCN scheduling: How the problem and solution have evolved

Algorithm performance and problem structure for flow-shop scheduling

Satellite Range Scheduling: A Comparison of Genetic, Heuristic and Local Search