Louis W. Miller

Bio: Louis W. Miller is an academic researcher from RAND Corporation. The author has contributed to research in topics: Dynamic priority scheduling & Two-level scheduling. The author has an hindex of 4, co-authored 4 publications receiving 3077 citations.

Theory of scheduling

Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading theory of scheduling as one of the reading material to finish quickly.

A logic programming framework for planning and simulation

Abstract: A common theme underlying various models that explain information technology adoption is the inclusion of perceptions of an innovation as key independent variables. Although a fairly significant body of research that empirically tests these models is now in existence, some questions with regard to both the antecedents as well as the consequents of perceptions remain unanswered. This paper reports the results of a field study examining adoption of an information technology innovation represented by an expert systems application. Two research objectives that have both theoretical and practical relevance motivated and guided the study. One, the study challenges an assumption which is implicit in technology acceptance models: that of the non-existence of moderating influences on the relationship between perceptions and adoption decisions. Specifically, the study examines the effects of an important moderating influence – personal innovativeness – on this relationship. Two, the study seeks to shed further light on the determinants of perceptions by examining the relative efficacy of mass media and interpersonal communication channels in facilitating perception development. Theoretical and practical implications that follow from the results are discussed.

The Queue M/G/1 with the Shortest Remaining Processing Time Discipline

Abstract: A priority queuing model in which the processing times of jobs are known upon arrival and preemption without loss of time or processing already accomplished is studied. Priority is assigned to jobs according to the length of processing remaining with highest priority going to the job with least processing left. A preemption will occur whenever the processing time of a newly arriving job is less than the remaining processing time of the job then in service. The Laplace-Stieltjes transforms of the waiting time and time-in-system distributions are obtained and comparisons with other queuing disciplines are made.

Technical Note—A Note on the Busy Period of an M/G/1 Finite Queue

Abstract: Recursive formulas for computing the Laplace-Stieltjes transform and the mean of the distribution of the lengths of busy periods for the M/G/1 finite queue are derived by a simple method that avoids simultaneous equations.

The Complexity of Flowshop and Jobshop Scheduling

Abstract: NP-complete problems form an extensive equivalence class of combinatorial problems for which no nonenumerative algorithms are known. Our first result shows that determining a shortest-length schedule in an m-machine flowshop is NP-complete for m ≥ 3. For m = 2, there is an efficient algorithm for finding such schedules. The second result shows that determining a minimum mean-flow-time schedule in an m-machine flowshop is NP-complete for every m ≥ 2. Finally we show that the shortest-length schedule problem for an m-machine jobshop is NP-complete for every m ≥ 2. Our results are strong in that they hold whether the problem size is measured by number of tasks, number of bits required to express the task lengths, or by the sum of the task lengths.

A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

Abstract: In a general flow-shop situation, where all the jobs must pass through all the machines in the same order, certain heuristic algorithms propose that the jobs with higher total process time should be given higher priority than the jobs with less total process time. Based on this premise, a simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics. The results of the proposed algorithm have been compared with the results from 15 other algorithms in an independent study by Park [13], who shows that the proposed algorithm performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.

The Shifting Bottleneck Procedure for Job Shop Scheduling

Abstract: We describe an approximation method for solving the minimum makespan problem of job shop scheduling. It sequences the machines one by one, successively, taking each time the machine identified as a bottleneck among the machines not yet sequenced. Every time after a new machine is sequenced, all previously established sequences are locally reoptimized. Both the bottleneck identification and the local reoptimization procedures are based on repeatedly solving certain one-machine scheduling problems. Besides this straight version of the Shifting Bottleneck Procedure, we have also implemented a version that applies the procedure to the nodes of a partial search tree. Computational testing shows that our approach yields consistently better results than other procedures discussed in the literature. A high point of our computational testing occurred when the enumerative version of the Shifting Bottleneck Procedure found in a little over five minutes an optimal schedule to a notorious ten machines/ten jobs problem on which many algorithms have been run for hours without finding an optimal solution.

ALLIANCE: an architecture for fault tolerant multirobot cooperation

Abstract: ALLIANCE is a software architecture that facilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behaviour-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.