Analysis on Software Project Staffing and Scheduling Using Ant Colony Optimization

Developing computer compatible and efficient techniques for software project planning are very much required and challenging in various multinational software companies. Most of the software projects are based on mainly human resources and it consists of people intensive activities. Thus proper scheduling of projects and human resource allocation is very much needed for rapid growth and development of software companies in a competitive world. In this paper a prototype based on project management system for project scheduling and human allocation is discussed. The paper discusses about the Ant Colony Optimization Algorithm (ACO) that takes into account both project scheduling and human allocation. For this the algorithm uses a task list and an employee allocation matrix. The paper consists of use of Gantt chart which displays the project schedule thus providing the project manager with the necessary project details (e.g. start date, end date, resource allocated).Thus providing the project managers the ease in functioning. The purpose of developing such a system is that it provides the software companies the flexibility in software planning. DOI: 10.17762/ijritcc2321-8169.150318

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

U radu se daje prijedlog dizajna paradigme algoritma za optimizaciju kolonije mrava primjenom Hyper-Cube sustava za rješenje problema programiranja računarskog projekta (Software Project Scheduling Problem). Taj se NP-hard problem sastoji od davanja zaduženja zaposlenicima u svrhu smanjenja trajanja projekta i njegovih ukupnih troškova. To zaduženje mora zadovoljiti ograničenja problema i pitanje prvenstva među zadacima. Pristup prikazan ovdje koristi Hyper-Cube sustav za uspostavljanje eksplicitno multidimenzionalnog prostora za kontrolu ponašanja mravi. Time nam se omogućava autonomno vođenje istraživanja u cilju pronalaženja ohrabrujućih rješenja.This paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions

A risk-based approach applied to system engineering projects: a new learning based multi-criteria decision support tool based on an ant colony algorithm

This article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system’s conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies

An improved multiple classifier combination scheme for pattern classification

Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification

Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

Dynamic vehicle routing with time windows in theory and practice

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon’s benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment

Algoritma Ant Colony Optimization untuk Optimasi Penjadwalan Mata Kuliah

The purpose of this study is to optimize the the lecturing schedule using the Ant Colony Optimization (ACO) algorithm. The method developed will find an optimal solution of lecturing schedule where there are some limitations that must be considered. These limitations include being a lecturer or a class that can only be scheduled to lecture a maximum of two times in a row. Another limitation is that two adjacent level may not be scheduled at the same time, because there is a possibility that students will repeat a lecture. The third limitation is that there should be no lecturers or classes that conduct lectures with too high a frequency one day. And the fourth limitation is the alternative time where lecturers can teach will be limited due to other activities that must be carried out by the lecturer. So that the course scheduling case can be solved using the ACO algorithm, a graph is made where each node is the name of the course that must be scheduled. The path created by the ants from the initial node to the end node will contain the order of courses that must be carried out one week. Based on the test results, the ACO algorithm has succeeded in scheduling courses involving 38 subjects, 4 class forces, 6 recovery locations and 12 lecturers supporting the courses. The scheduling solution obtained has a fitness value of 0.0092. Where there are no lecturers who have a high teaching frequency one day, but there are 12 class schedules that cause a class to follow a high frequency of lectures. And there are 4 courses scheduled to be close together. This final performance is considered quite good and shows that ACO has been successfully used to optimize course scheduling

Pengembangan Jadwal Shift Staf Editor Video pada Stasiun Televisi Nasional Trans7 berbasis Android menggunakan Algoritma Ant Colony dengan Firebase

Perusahaan bisnis bidang broadcasting atau tepatnya stasiun televisi memiliki strategi untuk menayangkan tayangan acara yang paling ditunggu oleh penonton setianya, yaitu berupa tayangan acara yang up to date dan cepat dalam penyiarannya. Oleh sebab itu, diperlukan staf pada bagian pasca produksi untuk bekerja secara cepat dan sesuai dengan prosedur tayangan. Untuk mempersiapkan tayangan terbaru secara cepat, diperlukan sistem penjadwalan staf editor video yang jumlahnya besar secara cepat, tepat, dan dapat dilihat dengan instan oleh para editor video sehingga memudahkan editing video setiap harinya. Algoritma yang cocok untuk menghasilkan jadwal secara cepat dengan jumlah yang besar dengan tidak ada data yang bentrok adalah ant colony atau algoritma koloni semut. Algoritma ant colony ini mengacu pada cara hidup semut yang berkelompok dalam mencari makanan sehingga dapat kembali lagi ke tempat semula dengan jalur yang sama dan cepat. Data masukan yang digunakan dalam penelitian ini adalah nama editor dan ruangan, serta luaran berupa jadwal per shift untuk setiap ruangan untuk suatu periode tertentu. Penelitian ini menggunakan basis data MySQL dan firebase. Jadwal editor yang telah diolah pada aplikasi back-end kemudian diubah ke dalam bentuk aplikasi android, dengan demikian jadwal tersebut dapat dilihat oleh seluruh staf editor video melalui smartphone masing-masing. Pengujian dilakukan terhadap hasil perhitungan algoritma, fungsional sistem, integrasi, dan penerimaan yang dikembalikan pada sistem dengan mencari kesalahan pada interface perangkat lunak, dan pengujian penggunaan langsung kepada pengguna. Hasil pengujian menunjukkan bahwa algoritma ant colony dapat digunakan untuk menyusun jadwal editor video dengan cepat dan tepat, semua fitur berjalan dengan baik, dan tingkat kepuasan pengguna cukup tinggi. AbstractBroadcasting or television business companies have a strategy to broadcast programs up to date and quickly. Therefore, staff in the post-production section are required to work quickly and in accordance with the show procedures. To prepare the latest shows quickly, a video editor staff scheduling system is needed. That scheduling system should be fast, precise, and can be seen instantly by video editors so that the editing processes can be done easily every day. The suitable algorithm to generate a schedule quickly in large numbers with no data clashing is ant colony.Ant colony algorithm refers to the way of ants when looking for food in a group, then they can return again to their original place with the same path and fast. The input data used in this study is the name of the editor and the room, and output in the form of a schedule per shift for each room for a certain period. This research uses MySQL and firebase databases. The editor's schedule that has been processed in the back-end application is then converted into an android application, therefore the schedule can be seen by all video editor staff via their own smartphones. Tests are carried out on the results of algorithm calculations, functional systems, integration, and feedback to the system by looking for errors on the software interface, and direct use testing to users. The test results show that the Ant Colony algorithm can be used to compile a schedule of video editors quickly and precisely, all features run well, and the level of user satisfaction is quite high

Optimal Vaccine Distribution Strategy for Different Age Groups of Population: A Differential Evolution Algorithm Approach

Vaccination is one of the effective ways for protecting susceptible individuals from infectious diseases. Different age groups of population have different vulnerability to the disease and different contact frequencies. In order to achieve the maximum effects, the distribution of vaccine doses to the groups of individuals needs to be optimized. In this paper, a differential evolution (DE) algorithm is proposed to address the problem. The performance of the proposed algorithm has been tested by a classical infectious disease transmission model and a series of simulations have been made. The results show that the proposed algorithm can always obtain the best vaccine distribution strategy which can minimize the number of infectious individuals during the epidemic outbreak. Furthermore, the effects of vaccination on different days and the vaccine coverage percentages have also been discussed