University Scheduling using Genetic Algorithm

The automated timetabling and scheduling is one of the hardest problem areas. This isbecause of constraints and satisfying those constraints to get the feasible and optimizedschedule, and it is already proved as an NP Complete (1) [1]. The basic idea behind this studyis to investigate the performance of Genetic Algorithm on general scheduling problem underpredefined constraints and check the validity of results, and then having comparative analysiswith other available approaches like Tabu search, simulated annealing, direct and indirectheuristics [2] and expert system. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems and later analysis will prove this argument. The programis written in C++ and analysis is done by using variation in various parameters.

University Timetabling using Genetic Algorithm

The field of automated timetabling and scheduling meeting all the requirementsthat we call constraints is always difficult task and already proved as NPComplete. The idea behind my research is to implement Genetic Algorithm ongeneral scheduling problem under predefined constraints and check the validityof results, and then I will explain the possible usage of other approaches likeexpert systems, direct heuristics, network flows, simulated annealing and someother approaches. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems. The program written in C++ and analysisis done with using various tools explained in details later.

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

Scheduling aircraft’s engines repair process: a mathematical model

In this talk, we discuss a scheduling problem that originated at TAP - Maintenance & Engineering - the maintenance, repair and overhaul organization of Portugal’s leading airline. In the repair process of aircrafts’ engines, the operations to be scheduled may be executed on a certain workstation by any processor of a given set, and the objective is to minimize the total weighted tardiness. A mixed integer linear programming formulation, based on the flexible job shop scheduling, is presented here, along with computational experiment on a real instance, provided by TAP-ME, from a regular working week. The model was also tested using benchmarking instances available in literature.

Resource Scheduling in Residential Microgrids Considering Energy Selling to External Players

The operation of distribution networks has been facing changes with the implementation of smart grids and microgrids, and the increasing use of distributed generation. The specific case of distribution networks that accommodate residential buildings, small commerce, and distributed generation as the case of storage and PV generation lead to the concept of microgrids, in the cases that the network is able to operate in islanding mode. The microgrid operator in this context is able to manage the consumption and generation resources, also including demand response programs, obtaining profits from selling electricity to the main network. The present paper proposes a methodology for the energy resource scheduling considering power flow issues and the energy buying and selling from/to the main network in each bus of the microgrid. The case study uses a real distribution network with 25 bus, residential and commercial consumers, PV generation, and storage.

Experience with the first Internet-based course at the Faculty of Medicine, University of São Paulo

PURPOSE: The Internet expands the range and flexibility of teaching options and enhances the ability to process the ever-increasing volume of medical knowledge. The aim of this study is to describe and discuss our experience with transforming a traditional medical training course into an Internet-based course. METHOD: Sixty-nine students were enrolled for a one-month course. They answered pre- and post-course questionnaires and took a multiple-choice test to evaluate the acquired knowledge. RESULTS: Students reported that the primary value for them of this Internet-based course was that they could choose the time of their class attendance (67%). The vast majority (94%) had a private computer and were used to visiting the Internet (75%) before the course. During the course, visits were mainly during the weekends (35%) and on the last week before the test (29%). Thirty-one percent reported that they could learn by reading only from the computer screen, without the necessity of printed material. Students were satisfied with this teaching method as evidenced by the 89% who reported enjoying the experience and the 88% who said they would enroll for another course via the Internet. The most positive aspect was freedom of scheduling, and the most negative was the lack of personal contact with the teacher. From the 80 multiple-choice questions, the mean of correct answers was 45.5, and of incorrect, 34.5. CONCLUSIONS: This study demonstrates that students can successfully learn with distance learning. It provides useful information for developing other Internet-based courses. The importance of this new tool for education in a large country like Brazil seems clear.

Scheduling of straddle carriers in a container terminal

Report for the scientific sojourn at the University of California at Berkeley between September 2007 to February 2008. The globalization combined with the success of containerization has brought about tremendous increases in the transportation of containers across the world. This leads to an increasing size of container ships which causes higher demands on seaport container terminals and their equipment. In this situation, the success of container terminals resides in a fast transhipment process with reduced costs. For these reasons it is necessary to optimize the terminal’s processes. There are three main logistic processes in a seaport container terminal: loading and unloading of containerships, storage, and reception/deliver of containers from/to the hinterland. Moreover there is an additional process that ensures the interconnection between previous logistic activities: the internal transport subsystem. The aim of this paper is to optimize the internal transport cycle in a marine container terminal managed by straddle carriers, one of the most used container transfer technologies. Three sub-systems are analyzed in detail: the landside transportation, the storage of containers in the yard, and the quayside transportation. The conflicts and decisions that arise from these three subsystems are analytically investigated, and optimization algorithms are proposed. Moreover, simulation has been applied to TCB (Barcelona Container Terminal) to test these algorithms and compare different straddle carrier’s operation strategies, such as single cycle versus double cycle, and different sizes of the handling equipment fleet. The simulation model is explained in detail and the main decision-making algorithms from the model are presented and formulated.

Scheduling of guarded command based models

Formal methods provide a means of reasoning about computer programs in order to prove correctness criteria. One subtype of formal methods is based on the weakest precondition predicate transformer semantics and uses guarded commands as the basic modelling construct. Examples of such formalisms are Action Systems and Event-B. Guarded commands can intuitively be understood as actions that may be triggered when an associated guard condition holds. Guarded commands whose guards hold are nondeterministically chosen for execution, but no further control flow is present by default. Such a modelling approach is convenient for proving correctness, and the Refinement Calculus allows for a stepwise development method. It also has a parallel interpretation facilitating development of concurrent software, and it is suitable for describing event-driven scenarios. However, for many application areas, the execution paradigm traditionally used comprises more explicit control flow, which constitutes an obstacle for using the above mentioned formal methods. In this thesis, we study how guarded command based modelling approaches can be conveniently and efficiently scheduled in different scenarios. We first focus on the modelling of trust for transactions in a social networking setting. Due to the event-based nature of the scenario, the use of guarded commands turns out to be relatively straightforward. We continue by studying modelling of concurrent software, with particular focus on compute-intensive scenarios. We go from theoretical considerations to the feasibility of implementation by evaluating the performance and scalability of executing a case study model in parallel using automatic scheduling performed by a dedicated scheduler. Finally, we propose a more explicit and non-centralised approach in which the flow of each task is controlled by a schedule of its own. The schedules are expressed in a dedicated scheduling language, and patterns assist the developer in proving correctness of the scheduled model with respect to the original one.

Irrigation scheduling for Sovereign Coronation grapevine based upon evapotranspiration calculations and crop coefficients /

Several irrigation treatments were evaluated on Sovereign Coronation table grapes at two sites over a 3-year period in the cool humid Niagara Peninsula of Ontario. Trials were conducted in the Hippie (Beamsville, ON) and the Lambert Vineyards (Niagara-on-the-Lake, ON) in 2003 to 2005 with the objective of assessing the usefulness of the modified Penman-Monteith equation to accurately schedule vine irrigation needs. Data (relative humidity, windspeed, solar radiation, and temperature) required to precisely calculate evapotranspiration (ETq) were downloaded from the Ontario Weather Network. One of two ETq values (either 100 or 150%) were used in combination with one of two crop coefficients (Kc; either fixed at 0.75 or 0.2 to 0.8 based upon increasing canopy volume) to calculate the amount of irrigation water required. Five irrigation treatments were: un irrigated control; (lOOET) X Kc =0.75; 150ET X Kc =0.75; lOOET X Kc =0.2-0.8; 150ET X Kc =0.2-0.8. Transpiration, water potential (v|/), and soil moisture data were collected each growing seasons. Yield component data was collected and berries from each treatment were analyzed for soluble solids (Brix), pH, titratable acidity (TA), anthocyanins, methyl anthranilate (MA), and total volatile esters (TVE). Irrigation showed a substantial positive effect on transpiration rate and soil moisture; the control treatment showed consistently lower transpiration and soil moisture over the 3 seasons. Transpiration appeared accurately reflect Sovereign Coronation grapevines water status. Soil moisture also accurately reflected level of irrigation. Moreover, irrigation showed impact of leaf \|/, which was more negative throughout the 3 seasons for vines that were not irrigated. Irrigation had a substantial positive effect on yield (kg/vine) and its various components (clusters/vine, cluster weight, and berries/cluster) in 2003 and 2005. Berry weights were higher under the irrigated treatments at both sites. Berry weight consistently appeared to be the main factor leading to these increased yields, as inconsistent responses were noted for some yield variables. Soluble solids was highest under the ET150 and ET100 treatments both with Kc at 0.75. Both pH and TA were highest under control treatments in 2003 and 2004, but highest under irrigated treatments in 2005. Anthocyanins and phenols were highest under the control treatments in 2003 and 2004, but highest under irrigated treatments in 2005. MA and TVE were highest under the ET150 treatments. Vine and soil water status measurements (soil moisture, leaf \|/, and transpiration) confirmed that irrigation was required for the summers of 2003 and 2005 due to dry weather in those years. They also partially supported the hypothesis that the Penman-Monteith equation is useful for calculating vineyard water needs. Both ET treatments gave clear evidence that irrigation could be effective in reducing water stress and for improving vine performance, yield and fruit composition. Use of properly scheduled irrigation was beneficial for Sovereign Coronation table grapes in the Niagara region. Findings herein should give growers some strong guidehnes on when, how and how much to irrigate their vineyards.

The impact of scheduling on the implementation of the new Ontario curriculum: teachers' perceptions /

This qualitative study explored secondary teachers' perceptions of scheduling in relation to pedagogy, curriculum, and observation of student learning. Its objective was to determine the best way to organize the scheduling for the delivery of Ontario's new 4-year curriculum. Six participants were chosen. Two were teaching in a semestered timetable, 1 in a traditional timetable, and 3 had experience in both schedules. Participants related a pressure cooker "lived experience" with weaker students in the semester system experiencing a particularly harsh environment. The inadequate amount of time for review in content-heavy courses, gap scheduling problems, catch-up difficulties for students missing classes, and the fast pace of semestering are identified as factors negatively impacting on these students. Government testing adds to the pressure by shifting teachers' time and attention in the classroom from deeper learning to a superficial coverage of material, from curriculum as lived to curriculum as text to be covered. Scheduling choice should be available in public education to accommodate the needs of all students. Curriculum guidelines need to be revamped to reflect the content that teachers believe is necessary for a successful course delivery. Applied level courses need to be developed for students who are not academically inferior but learn differently.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Dynamic Lightpath Scheduling in Next-Generation WDM Optical Networks

Lightpath scheduling is an important capability in next-generation wavelength-division multiplexing (WDM) optical networks to reserve resources in advance for a specified time period while provisioning end-to-end lightpaths. In this study, we propose an approach to support dynamic lightpath scheduling in such networks. To minimize blocking probability in a network that accommodates dynamic scheduled lightpath demands (DSLDs), resource allocation should be optimized in a dynamic manner. However, for the network users who desire deterministic services, resources must be reserved in advance and guaranteed for future use. These two objectives may be mutually incompatible. Therefore, we propose a two-phase dynamic lightpath scheduling approach to tackle this issue. The first phase is the deterministic lightpath scheduling phase. When a lightpath request arrives, the network control plane schedules a path with guaranteed resources so that the user can get a quick response with the deterministic lightpath schedule. The second phase is the lightpath re-optimization phase, in which the network control plane re-provisions some already scheduled lightpaths. Experimental results show that our proposed two-phase dynamic lightpath scheduling approach can greatly reduce WDM network blocking.