PLAN-IT: Scheduling assistant for solar system exploration

A frame-based expert scheduling system shell, PLAN-IT, is developed for spacecraft scheduling in the Request Integration Phase, using the Comet Rendezvous Asteroid Flyby (CRAF) mission as a development base. Basic, structured, and expert scheduling techniques are reviewed. Data elements such as activity representation and resource conflict representation are discussed. Resource constraints include minimum and maximum separation times between activities, percentage of time pointed at specific targets, and separation time between targeted intervals of a given activity. The different scheduling technique categories and the rationale for their selection are also considered

An approximate dynamic programming approach to food security of communities following hazards

Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.Comment: As opposed to the preemptive scheduling problem, which was addressed in multiple works by us, we deal with a non-preemptive stochastic scheduling problem in this work. Submitted to 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 201

Online Scheduled Execution of Quantum Circuits Protected by Surface Codes

Quantum circuits are the preferred formalism for expressing quantum information processing tasks. Quantum circuit design automation methods mostly use a waterfall approach and consider that high level circuit descriptions are hardware agnostic. This assumption has lead to a static circuit perspective: the number of quantum bits and quantum gates is determined before circuit execution and everything is considered reliable with zero probability of failure. Many different schemes for achieving reliable fault-tolerant quantum computation exist, with different schemes suitable for different architectures. A number of large experimental groups are developing architectures well suited to being protected by surface quantum error correcting codes. Such circuits could include unreliable logical elements, such as state distillation, whose failure can be determined only after their actual execution. Therefore, practical logical circuits, as envisaged by many groups, are likely to have a dynamic structure. This requires an online scheduling of their execution: one knows for sure what needs to be executed only after previous elements have finished executing. This work shows that scheduling shares similarities with place and route methods. The work also introduces the first online schedulers of quantum circuits protected by surface codes. The work also highlights scheduling efficiency by comparing the new methods with state of the art static scheduling of surface code protected fault-tolerant circuits.Comment: accepted in QI

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Optimize class time tabling by using genetic algorithm technique in UTHM

Timetable scheduling in academic institutions is a major challenge for the institutions, especially with a large number of students and courses offered. This becomes more challenging when classrooms are limited and needs to consider the meeting time of students with the lecturers. These academic institutions such as schools, colleges and universities need timetables to make sure that the students have enough time for each subject in a week without clashing with other subjects or other classes. There are elements that need to be considered in order to make a timetable. These elements include students, teachers or lecturers, rooms, period and also the subjects involved. A new branch of university which is Universiti Tun Hussein Onn Malaysia (UTHM) Pagoh will also have a problem to schedule timetables. Since the branch is new, therefore the problem of lacking in facilities, the number of classrooms and the number of students or classes will arise. In order to schedule timetables, reshuffling and arranging classrooms need to be done and may lead to the complexity of classrooms scheduling. In existing research, many problems involving scheduling have been solved by using genetic algorithm method. There are many other methods that were also being used such as linear programming, integer linear programming, tabu search, ant colony optimization (ACO) algorithm and goal programming. This research is about optimization problem and it proposes a heuristic approach for timetabling optimization, in order to improve and enhance the efficiency of classroom planning. A new algorithm was produced to handle the timetabling problem in the university. This research used genetic algorithm (GA) that was applied to java programming languages with a goal of reducing conflict and optimizing the fitness. Therefore, the general problem was being solved and the best solutions were obtained with lower number of conflicts and maximum fitness value. The timetables for the FAST firstyear students from the Mathematics Department and Statistics Department were also being solved with less conflict and maximum fitness value. A further analysis was done and the results provided the best solutions as well. This research gives an idea about timetable scheduling and also about the optimization method of GA. This research can also become a reference for other timetable scheduling

A hybrid scatter search. Electromagnetism meta-heuristic for project scheduling.

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory, hereafter referred to as the electromagnetism meta-heuristic. We present computational experiments on standard benchmark datasets, compare the results with current state-ofthe-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics.Algorithms; Effectiveness; Electromagnetism; Functions; Heuristic; Project scheduling; Scatter; Scatter search; Scheduling; Theory;