Final Schedule Research Articles

A two-stage multi-objective scheduling method for integrated community energy system

In order to determine the optimal day-ahead scheduling schemes of the integrated community energy system (ICES), a two-stage multi-objective scheduling method (TMSM) was proposed, which consists of a multi-objective optimal power flow (MOPF) calculation stage and a multiple attributes decision making (MADM) stage. Firstly, the electric distribution network, the natural gas network and the energy centers (ECs) of the ICES were modelled. Secondly, five typical indices are considered to characterize the operation of ICES, namely the operation cost (OC) and total emission (TE) of ICES, the power loss (PL) and sum of voltage deviation (SVD) of electric distribution network, the sum of pressure deviation (SPD) of natural gas network. In order to tackle the computation problems resulted by the increasing number of objectives, the dimension reduction of objectives is employed. The indices of OC and TE are selected based on the analytic hierarchy process (AHP) method and set as the objectives at the MOPF calculation stage. Thirdly, all the five indices are considered during the MADM stage to determine the final day-ahead scheduling schemes from the alternative solutions obtained in MOPF. Numerical studies demonstrate that the TMSM is able to provide flexibility for the operation of ICES. The determined optimum day-ahead scheduling schemes are capable of satisfying and balancing operational needs in aspects of security, economy and environmental friendliness.

IMOPSE: a library for bicriteria optimization in Multi-Skill Resource-Constrained Project Scheduling Problem

This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE dataset which consists of 36 instances and additional ’small’ 6 instances for educational purpose. In the paper, three test studies are described: (1) educational use of 6 ’small’ instances, (2) optimization of cost or duration of a schedule, and (3) simple bicritieria optimization of cost/duration of a final schedule. All described tools/examples are freely published on iMOPSE homepage.

Standards Week at the Long Beach Symposium – July 30 to August 3

Building on the success of what has now become EMC Standards Week, i.e. a week of standards working group meetings associated with our annual Symposium, I am pleased to invite you to participate in the activities coming up in Long Beach. At the time of writing, the following working groups have confirmed that they will be meeting with the tentative schedule. Please note that all meetings are open to any attendees. The final schedule will be advised in the coming weeks.

A multi-policy adaptive scheduling framework in virtual clouds

With the development of cloud computing technology, many cloudbased data centres are beginning to connect with each other aiming at building large-scale federated cloud platforms. In such a federated cloud platform, the efficiency and effectiveness of traditional schedulers will be significantly degraded due to the unpredictable resource availability and high networking latency. In this paper, we propose a multi-policy adaptive scheduling framework, which is capable of evaluating the scheduling scheme made by existing schedulers. In this way, the proposed scheduling framework can make full use of the advantages of existing scheduling policies and avoid their shortcomings, and therefore makes the final scheduling decisions more efficient and effectiveness. In addition, the proposed scheduling framework is designed as extensible component that can be easily extended by incorporating other scheduling policies. The prototype of this scheduling framework is tested in a real-world federated cloud platform. The experimental results show that it can significantly improve the quality-of-service for the underlying cloud infrastructures as well as cloud user's satisfactory.

Fortune Telling, Estimating & Systems Engineering

AbstractEstimating the cost of a system is an essential aspect of Systems Engineering. Both the recurring and non‐recurring costs will need to be understood before a business can make a commitment. Often systems will be constrained by affordability and when the system will be needed and such constraints will need to be understood by the Systems Engineer. There is little point developing a system that will take too long to build and would likely bankrupt the company or customer. So a Systems Engineer will also need to explore trade options to optimize the system for cost and schedule.Fortune tellers profess to know the future and assure business leaders that the system is a good idea and will be affordable. Evidence presented in this paper shows that fortune telling can be inaccurate and that unless robust estimation practices are applied, most systems will overshoot their budgets and deadlines. Often it is too late when a business finds out the true cost of the system they are committed to. A Systems Engineer must employ proven estimating practices if they are to avoid customer dissatisfaction and business lossesSuccess leaves clues and successful estimates are no accident. Success is defined here as an accurate estimate, one where the final system costs and schedules come in acceptably close to the estimate.When we asked Systems Engineers for their views on the factors that lead to an accurate estimate, we hear answers like; clear requirements, accurate historic data, and robust estimation techniques. But how important are these and is it possible to quantify their value, their contribution, towards a successful estimate?The authors developed a calibrated scoring mechanism to quantify the “maturity” of an estimate to help business leaders understand the “goodness” of an estimate before they make a commitment. The concept is called Estimate Readiness Level and is a score between 1 and 9 representing the maturity of the estimate. This paper summarizes how the authors calibrated the Estimate Readiness Level assessment so that business leaders can now predict the risk and uncertainty in an estimate before the project launches, and therefore be confident that the system being proposed is affordable and that the business case can be believed.

The dynamic vehicle rescheduling problem

The pre-planned schedules of a transportation company are often disrupted by unforeseen events. As a result of a disruption, a new schedule has to be produced as soon as possible. This process is called the vehicle rescheduling problem, which aims to solve a single disruption and restore the order of transportation. However, there are multiple disruptions happening over a “planning unit” (usually a day), and all of them have to be addressed to achieve a final feasible schedule. From an operations management point of view the quality of the final solution has to be measured by the combined quality of every change over the horizon of the “planning unit”, not by evaluating the solution of each disruption as a separate problem. The problem of finding an optimal solution where all disruptions of a “planning unit” are addressed will be introduced as the dynamic vehicle rescheduling problem (DVRSP). The disruptions of the DVRSP arrive in an online manner, but giving an optimal final schedule for the “planning unit” would mean knowing all information in advance. This is not possible in a real-life scenario, which means that heuristic solution methods have to be considered. In this paper, we present a recursive and a local search algorithm to solve the DVRSP. In order to measure the quality of the solutions given by the heuristics, we introduce the so-called quasi-static DVRSP, a theoretical problem where all the disruptions are known in advance. We give two mathematical models for this quasi-static problem, and use their optimal solutions to evaluate the quality of our heuristic results. The heuristic methods for the dynamic problem are tested on different random instances.

A Conference on the Origin (and Evolution) of Baryonic Galaxy Halos

A conference was held in March 2017 in the Galapagos Islands on the topic of The Origin (and Evolution) of Baryonic Galaxy Halos. It attracted some 120 researchers from around the world. They presented 68 talks (nine of which were invited) and 30 posters over five days. A novel element of the talk schedule was that participants were asked which talks they wanted to hear and the schedule was made up based on their votes and those of the Scientific Organizing Committee SOC . The final talk schedule had 34% of the talks given by women. An emphasis was given to discussion time directly after each talk. Combined with limited/no access to the internet, this resulted in high level of engagement and lively discussions. A prize was given to the poster voted the best by participants. A free afternoon included organized excursions to see the local scenery and wildlife of the Galapagos (e.g., the giant tortoises). Four public talks were given, in Spanish, for the local residents of the town. A post-conference survey was conducted, with most participants agreeing that the conference met their scientific needs and helped to initiate new research directions. Although it was challenging to organize such a large international meeting in such an isolated location as the Galapagos Islands (and much credit goes to the Local Organizing Committee LOC and staff of Quito Astronomical Observatory for their logistical efforts, organizing the meeting for over a year), it was very much a successful conference. We hope it will play a small part in further developing astronomy in Ecuador.

Development of an interview schedule for assessing factors influencing educational outcome in students with schizophrenia

IntroductionSchizophrenia is a severe mental disorder with a relatively high toll on the quality of life of the patient and caregiver. It has a high financial, emotional and psychosocial burden. Surprisingly, optimum academic and educational outcomes in individuals with schizophrenia have been a neglected area of research and service provision.ObjectivesDevelopment of an interview schedule assessing the helpful and hindering factors affecting the educational attainment in persons with schizophrenia.MethodsTwenty-one participant were recruited (11 patients and 10 caregivers) from August 2014 to 2015 using purposive sampling and interviewed in a semi-structured qualitative fashion. Patients were between 16–25 years of age. Data collection and interpretation continued iteratively till saturation of factors was achieved. The list of factors (hindering/helping) was compiled and sent to a panel of 14 experts. They rated the schedule and the individual factors on a Likert scale. Reliability and validity parameters were tested and the final schedule was formulated.ResultsThe final schedule contained 17 hindering and 18 helping factors. Detailed instructions to the interviewer for administration of the schedule are included. The factors have been further subdivided into illness related and illness unrelated. Some of the major hindering factors were symptoms of illness, medication side effects, delay in treatment initiation, perceived conflict in parents, lack of motivation. The major helpful factors were adequate symptom control, withholding inpatient care, spirituality, and peer group acceptance.ConclusionsService provisions for ensuring optimal educational achievement can be formulated by assessing the felt needs and hindrances of patients and their caregivers.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Rosetta Lander Batteries Experience During All Operation Phases

Rosetta is an ambitious ESA mission, launched in March 2004 from Kourou and which performed a rendezvous with comet 67/P Churyumov-Gerasimenko. Its lander, Philae, achieved landing on comet soil on the 12th November 2014 and performed 64 hours of science activities on its batteries before going into hibernation due to lack of solar energy.Philae is operated by the Lander Control Centre (LCC) at DLR Cologne Germany and the Science Operations and Navigation Centre (SONC) at CNES Toulouse France. The Lander system was provided by a European consortium (Germany, France, Italy, Hungary, Finland, UK, Ireland, Switzerland and Austria) and supports a scientific payload of 10 instruments.The Philae battery system was provided by CNES, it is composed of a Saft primary battery (1518Wh) and an ABSL secondary battery (151Wh). The primary is made of non-rechargeable LSH20 (LiSOCl2) Saft cells and the secondary of rechargeable ABSL Li-ion 18650HC. For the Philae mission, the energetic constraint was very important. Indeed, before launch, the operations had to be planned considering variability of several parameters (descent duration, communication slots, comet temperature, solar power availability, etc.). Since Rosetta launch, cells and batteries have been stored and specific ground test plans have been identified in order to follow the battery ageing and to validate the final Philae operation schedule. From ground test results, an electrical model of the batteries was developed to help the operations scheduling.During cruise, the operations consisted of secondary batteries monitoring and tests and primary battery conditioning. During separation and on-comet operations, the behaviour of the batteries system was checked and electric simulations helped with activities scheduling.Firstly, this paper will describe the Philae mission. In a second part, the batteries system will be presented. The ground strategy will be detailed. Finally, the operations of Philae batteries system will be described.

An estimation of distribution algorithm for public transport driver scheduling

Public transport driver scheduling is a process of selecting a set of duties for the drivers of vehicles to form a number of legal driver shifts. The problem usually has two objectives which are minimising both the total number of shifts and the total shift cost, while taking into account some constraints related to labour and company rules. A commonly used approach is firstly to generate a large set of feasible shifts by domain-specific heuristics, and then to select a subset to form the final schedule by an integer programming method. This paper presents an estimation of distribution algorithm (EDA) to deal with the subset selection problem which is NP-hard. To obtain a candidate schedules, the EDA applies a number of rules, with each rule corresponding to a particular way of selecting a shift. Computational results from some real-world instances of drive scheduling demonstrate the availability of this approach.

Perancangan Aplikasi Pengumuman Kampus Berbasis Android Pada STKIP-PGRI Lubuk Linggau

College of Teacher Training and Education (STKIP-PGRI) Lubuklinggau is one of the private universities in Lubuklinggau city, focusing on human resource development in teacher training and education. STKIP-PGRI Lubuklinggau currently has many students, including students who live in Lubuklinggau and others from outside the city of Lubuklinggau. Students who live far from the campus find it difficult to obtain academic-related academic information such as lecture announcement information, announcement of final semester exam schedule, announcement of exam schedule, announcement of thesis examination schedule, announcement of KRS retrieval schedule, and announcement of KHS taking schedule. As a solution of the problem of student difficulties, it must be made an application of Campus Based Announcement Application of Android on STKIP-PGRI Lubuklinggau so that students more quickly in obtaining information campus announcement STKIP-PGRI Lubuklinggau flexibly without boundaries of time and place.

Optimized scheduling and buffering of repetitive construction projects under uncertainty

Purpose Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty. Design/methodology/approach Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated. Findings Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule. Originality/value Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.

An undergraduate uses OR to improve Final Exam Schedules at her university

Final examination scheduling is typically a complex problem that impacts students, faculty, and administrators at every university. In this paper, we describe how an undergraduate student, for her senior project at Kutztown University, analysed the final exam schedules at Kutztown University to see if she could improve them. Specifically, she wanted to see if she could reduce student conflicts defined to be a student having three exams scheduled on the same day. The approach that she developed, based on a balanced bin packing algorithm, was very appealing because it could be implemented manually by a staff member of the Registrar’s office, requiring at most 30 minutes to generate the schedule. Testing this approach using actual data from the Fall 2015 semester resulted in a 42% reduction in student conflicts. This approach, because of its simplicity and intuitive appeal, was widely accepted by the Kutztown University faculty and administrators and is being implemented for the Fall 2016 semester.

Multi-objective demand side scheduling considering the operational safety of appliances

The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling.

GENETIC ALGORITHM-BASED ADMISSION TEST FORVEHICLE-TO-GRID ELECTRICITY TRADE SERVICES

This paper designs and evaluates a vehicle-to-grid (V2G) electricity trader capable of selecting an appropriate subset out of a large number of electric vehicles (EVs) which want to sell their energy to a microgrid. A genetic algorithm, tailored for this trade coordination, reduces the amount of unmet demand forecasted one day advance in the microgrid. Each subset is encoded to an integer r vector whose element has either 1 or 0 according to whether the associated EV is included in the subset or not. The evaluation function estimates the fitness of a feasible solution, employing a fast heuristic-based unit scheduler. Its lightweight-ness allows the genetic algorithm to calculate the fitness of the massive number of feasible subsets, each of which has a fixed number of EVs. This admission test gives a chance for EVs to contact with other microgrids when they are not accepted to the final trade schedule. The performance measurement result obtained from a prototype implementation reveals that the proposed scheme achieves up to 20.8 % performance improvement over the random selection scheme in terms of unmet demand. Moreover, the proposed scheme can efficiently cope with overload condition, that is, many EVs are concentrated in a single microgrid, judging from its stable performance curve.

Treatment of escape-maintained challenging behavior using chained schedules: an evaluation of the effects of thinning positive plus negative reinforcement during functional communication training

Objective: Previous research has supported functional communication training as an effective intervention for reducing challenging behavior. Clinicians often program schedule-thinning procedures to increase the portability of the treatment (i.e. reinforcement is provided less frequently). For individuals with escape-maintained problem behavior, chained schedules have proven effective in increasing task completion and supplemental procedures may ameliorate reemergence of challenging behavior as access to reinforcement is decreased. This study compared the use of a chained schedule-thinning procedure with and without alternative reinforcement (e.g. toys and activities) embedded in an intervention in which escape from the task is provided contingent on a request for a break.Method: Two individuals with escape-maintained challenging behavior participated. We compared two treatment conditions, escape-only and escape-to-tangibles, using a single-subject, alternating treatments design with each treatment implemented in a distinct academic context.Result: With the escape-to-tangibles treatment, we reached the final schedule in both contexts with both participants (4 successes of 4 applications). We did not reach the final schedule with either participant with the escape-only intervention (0 successes of 2 applications).Conclusion: The current results provided preliminary confirmation that providing positive plus negative reinforcement would decrease destructive behavior, increase compliance, and facilitate reinforcer-schedule thinning.

Algorithm of Sources Management in Deploying of Information Support Systems for Complicated Technical Complexes

The paper discusses a task of sources consumption management in the process of deploying information support systems for complicated technical complexes (CTC). Application of CTC as well as the process of appropriate information support are usually limited by the exact prescribed terms, so any delay is not allowable. The delay may be eliminated more often only by involving additional information sources at later stages. The developed algorithm is based on Bellman principle of optimality that allows one to define not the final correction schedule, but to operate a flexible program of control actions, depending on the concrete result at every stage, duration of which exceeds a defined norm. This program can be used in the appropriate decision support systems and can be included in the simulation models of CTC deploying and applying. The paper describes a detailed algorithm for optimal correction, corresponding to the normal distribution of stages durations.

Final Fee Schedule Includes Payment for Advance Care Planning

Final Fee Schedule Includes Payment for Advance Care Planning

Single-machine rework rescheduling to minimize maximum waiting-times with fixed sequence of jobs and ready times

This paper studies a single machine rescheduling problem in which a set of newly arrived rework jobs must be inserted into an existing schedule. The original jobs have ready times, and the objective is to minimize the maximum waiting time of any job in the final schedule. The problem is demonstrated to be NP-hard. Properties of the optimal solution are developed, enabling the development of a pseudo-polynomial algorithm for a special case and a heuristic algorithm for a general problem. A heuristic procedure is developed, and its worst case performance is derived. Experimental results suggest that the heuristic yields optimal and close-to-optimal solutions to problems similar to those present in practice.

An evolutionary implicit enumeration procedure for solving the resource‐constrained project scheduling problem

AbstractThis paper presents a procedure for solving the resource‐constrained project scheduling problem. It consists of an implicit enumeration module and a genetic algorithm. If the procedure is provided access to all of its required computational resources of the problem at hand, it guarantees the optimality of the produced solution. In contrast, and in the case of limited access to computational resources, the procedure gradually moves the root of the search‐tree downward, and consequently prunes part of the search space, trading speed with precision effectively. In the cases where speed has been traded with precision, and the guarantee of optimality has been lost, the final schedule created by the implicit enumeration module is used as a template whose modified instances fill an initial pool of a genetic algorithm to improve the proposed solution. The procedure has been applied to 2040 benchmark instances. The results are promising; whereas for all small‐ and some medium‐sized instances, the procedure has found and guaranteed optimal solutions, for other instances, whose optimal solutions cannot be guaranteed within the limit of computational resources, it has produced high‐quality solutions.