Johnson's Algorithm Research Articles

Finding an effective classification technique to develop a software team composition model

AbstractIneffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to (1) discover an effective classification technique to solve the problem and (2) develop a model for composition of the software development team. The model developed was composed of 3 predictors: team role, personality types, and gender variables; it also contained 1 outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and rough sets theory (RST). Higher prediction accuracy and reduced pattern complexity were the 2 parameters for selecting the effective technique. Based on the results, the Johnson algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.

Improving the GJK algorithm for faster and more reliable distance queries between convex objects

This article presents a new version of the Gilbert-Johnson-Keerthi (GJK) algorithm that circumvents the shortcomings introduced by degenerate geometries. The original Johnson algorithm and Backup procedure are replaced by a distance subalgorithm that is faster and accurate to machine precision, thus guiding the GJK algorithm toward a shorter search path in less computing time. Numerical tests demonstrate that this effectively is a more robust procedure. In particular, when the objects are found in contact, the newly proposed subalgorithm runs from 15% to 30% times faster than the original one. The improved performance has a significant impact on various applications, such as real-time simulations and collision avoidance systems. Altogether, the main contributions made to the GJK algorithm are faster convergence rate and reduced computational time. These improvements may be easily added into existing implementations; furthermore, engineering applications that require solutions of distance queries to machine precision can now be tackled using the GJK algorithm.

Improving the GJK Algorithm for Faster and More Reliable Distance Queries Between Convex Objects

This article presents a new version of the Gilbert-Johnson-Keerthi (GJK) algorithm that circumvents the shortcomings introduced by degenerate geometries. The original Johnson algorithm and Backup procedure are replaced by a distance subalgorithm that is faster and accurate to machine precision, thus guiding the GJK algorithm toward a shorter search path in less computing time. Numerical tests demonstrate that this effectively is a more robust procedure. In particular, when the objects are found in contact, the newly proposed subalgorithm runs from 15% to 30% times faster than the original one. The improved performance has a significant impact on various applications, such as real-time simulations and collision avoidance systems. Altogether, the main contributions made to the GJK algorithm are faster convergence rate and reduced computational time. These improvements may be easily added into existing implementations; furthermore, engineering applications that require solutions of distance queries to machine precision can now be tackled using the GJK algorithm.

Improving the GJK Algorithm for Faster and More Reliable Distance Queries Between Convex Objects

This article presents a new version of the Gilbert-Johnson-Keerthi (GJK) algorithm that circumvents the shortcomings introduced by degenerate geometries. The original Johnson algorithm and Backup procedure are replaced by a distance subalgorithm that is faster and accurate to machine precision, thus guiding the GJK algorithm toward a shorter search path in less computing time. Numerical tests demonstrate that this effectively is a more robust procedure. In particular, when the objects are found in contact, the newly proposed subalgorithm runs from 15% to 30% times faster than the original one. The improved performance has a significant impact on various applications, such as real-time simulations and collision avoidance systems. Altogether, the main contributions made to the GJK algorithm are faster convergence rate and reduced computational time. These improvements may be easily added into existing implementations; furthermore, engineering applications that require solutions of distance queries to machine precision can now be tackled using the GJK algorithm.

Properties of two-machine no-wait flow-shop scheduling with a non-resumable unavailable interval

The properties of two-machine no-wait flow-shop scheduling problem, with a non-resumable unavailable interval are studied. This research discusses two cases: a non-resumable unavailable interval in the first and second machines respectively. The problems’ complexities are proved to be NP-hard. The Gilmore and Gomory Algorithm (GGA) is applied to minimize the makespan, and the conditions that GGA yields the optimal solutions are presented. Sixty numerical examples with ten-job and two-machine are generated to test the proposed theorems. Compared to the results obtained by the Johnson algorithm, the GGA algorithm yields an 8.15% improvement on average. Moreover, the worst-case performance ratios of GGA are proved to be 2.

Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model

Programming is a cognitive activity which requires logical reasoning to code for abstract presentation. This study aims to find out the personality traits of students who maintain the effective grades in learning programming courses such as structured programming (SP) and object oriented programming (OOP) by gender classification. Data were collected from three universities to develop, validate, and generalize the Rough-Fuzzy model. Genetic and Johnson algorithms were applied under Rough set theory’s (RST) principles to extract the decision rules. In addition, Standard Voting, Naive Bayesian, and Object Tracking procedures were applied on the generated decision rules to find the prediction accuracy of each algorithm. Mamdani’s Fuzzy Inference System (FIS) was used for mapping the decision rules’ condition (input) to decision (output) based on fuzzy set theory (FST) to develop the model. The results highlighted that certain personality compositions can be suitable for scoring good grades in programming subjects. For instance, a female student is capable enough to improve the programming skills if she is composed of introvert and sensing personality traits. Therefore, it is important to investigate an appropriate personality composition for programming learners.

Greedy Algorithms for the Maximum Satisfiability Problem: Simple Algorithms and Inapproximability Bounds

We give a simple, randomized greedy algorithm for the maximum satisfiability problem (MAX SAT) that obtains a $\frac{3}{4}$-approximation in expectation. In contrast to previously known $\frac{3}{4}$-approximation algorithms, our algorithm does not use flows or linear programming. Hence we provide a positive answer to a question posed by Williamson in 1998 on whether such an algorithm exists. Moreover, we show that Johnson's greedy algorithm cannot guarantee a $\frac{3}{4}$-approximation, even if the variables are processed in a random order. Thereby we partially solve a problem posed by Chen, Friesen, and Zheng in 1999. In order to explore the limitations of the greedy paradigm, we use the model of priority algorithms of Borodin, Nielsen, and Rackoff. Since our greedy algorithm works in an online scenario where the variables arrive with their set of undecided clauses, we wonder if a better approximation ratio can be obtained by further fine-tuning its random decisions. For a particular information model we show that no priority algorithm can approximate Online MAX SAT within $\frac{3}{4} + \varepsilon$ (for any $\varepsilon > 0$). We further investigate the strength of deterministic greedy algorithms that may choose the variable ordering. Here we show that no adaptive priority algorithm can achieve approximation ratio $\frac{3}{4}$. We propose two ways in which this inapproximability result can be bypassed. First we show that if our greedy algorithm is additionally given the variable assignments of an optimal solution to the canonical LP relaxation, then we can derandomize its decisions while preserving the overall approximation guarantee. Second we give a simple, deterministic algorithm that performs an additional pass over the input. We show that this 2-pass algorithm satisfies clauses with a total weight of at least $\frac{3}{4} {OPT}_{LP}$, where ${OPT}_{LP}$ is the objective value of the canonical linear program. Moreover, we demonstrate that our analysis is tight and detail how each pass can be implemented in linear time.

Exact Dominance Querying Algorithm on CP-nets

CP-nets (conditional preference networks) is a graphical model for representing qualitative conditional preference statements via ceteris paribus semantics interpretation. Dominance querying, the problem of determining whether an outcome is preferred to another on this graphical model, is of fundamental importance in preference querying. However, to date, the exact dominance querying algorithm with respect to any binaryvalued CP-nets has not been given, hence designing an exact dominance querying algorithm is quite necessary. Fortunately, we discover that dominance querying problem is essentially a single source shortest path problem, and the induced graph of CP-nets is a sparse graph, as a result, dominance querying problem can be solved by Johnson algorithm which is suitable for solving the single source shortest path problem on sparse graph. As a byproduct, this algorithm can determine least flip numbers if an outcome dominates another outcome. At last, we present results of experiments that demonstrate the feasibility of our approach to dominance querying on acyclic CP-nets.

HScheduler: an optimal approach to minimize the makespan of multiple MapReduce jobs

Large-scale MapReduce clusters that routinely process big data bring challenges to the cloud computing. One of the key challenges is to reduce the response time of these MapReduce clusters by minimizing their makespans. It is observed that the order in which these jobs are executed can have a significant impact on their overall makespans and resource utilization. In this work, we consider a scheduling model for multiple MapReduce jobs. The goal is to design a job scheduler that minimizes the makespan of such a set of MapReduce jobs. We exploit classical Johnson model and propose a novel framework HScheduler, which combines features of both classical Johnson's algorithm and MapReduce to minimize the makespan for both offline and online jobs. Our Offline HScheduler reaches the theoretical lower bound (optimum) and Online HScheduler is 2-competitive which is the best-known constant ratio for minimizing the makespan. Through extensive real data tests, we find that HScheduler has better performance than the best-known approach by 10.6---11.7 % on average for offline scheduling and 8---10 % on average for online scheduling. The HScheduler can be applied to improve responsive time, throughput and energy efficiency in cloud computing.

Two Stage Flow Shop Scheduling Problem with Involving Transportation Time for Job Blocks in Fuzzy Environment

This paper consists of afuzzy flow shop scheduling problem whose objective is to minimize the total elapsed timeof jobs involvingjob block. The processing time setup time and transportation time are taken as triangular fuzzy numbers which are defuzzified into crisp ones by Yager's method. The optimal sequence is found by Johnson's algorithm and the minimum totalelapsed time is found. The approach is illustrated by a numerical example.

The Application of Floyd Algorithm for Returning Path Algorithm with Constraints of Time and Limit of Load

Under ideal condition, there are lots of Shortest Path Algorithms such as Dijkstra, Floyd, Johnson algorithms. However, putting these algorithms into use is not as we expected. The reasons cause the problem may be in the Returning Problem mentioned in the following paper. Shortest Path Algorithms only focus on the shortest path between the points, without paying attention to figure out the direct solution from the Starting Point to the Passing Points and finally to the Starting Point. A Shortest Path Algorithm applied in actual practice will be introduced in here, then another case will be introduced and analyzed to explain the following Algorithm: Under the Constraints of time and limit of Loading capacity, figure out the Shortest Path Algorithm through the Starting Point to the Passing Points and back to the Starting Point. At last , the algorithm is of feasibility and practicability.

Heuristics for solving flow shop scheduling problem under resources constraints

Most of traditional scheduling problems deal with machine as the only resource, however, other resources such as raw materials is often disregards. Considering the second resource makes scheduling problems more realistic and practical to implement in manufacturing industries. Due to the applicability of flow shop environment in different manufacturing, scheduling of these types of shops are extensively studied by several authors. However, introducing an additional resource in this environment is not well studied. The present work deals with makespan minimization in flow shop scheduling problems where no renewable resources constraints are considered. The paper illustrates the importance of Johnson (1954) algorithm for the two machine flow shop under resources constraints. A mathematical model is also presented. Then a well-know heuristic is adapted to propose fast solution for the m machines flow shop problem subject to resource constraint.

Proposed approach of extended Johnson algorithm analysis for load balancing and congestion control in software defined networking

Proposed approach of extended Johnson algorithm analysis for load balancing and congestion control in software defined networking

Comparison Studies for Different Shortest path Algorithms

While technological revolution has active role to the increase of computer information, growing computational capabilities of devices, and raise the level of knowledge abilities, and skills. Increase developments in science and technology. In graph used the shortest path algorithms for solving the shortest path problem. The shortest path can be single pair shortest path problem or all pairs shortest path problem. This paper discuss briefly the shortest path algorithms such as Dijkstra's algorithm, Bellman-Ford algorithm,Floyd- Warshall algorithm, and johnson's algorithm. It describes the previous algorithms for solving the shortest path problem. The goal of this paper is to investigate and comparison the impacts of different shortest path algorithms. The study shows that the efficiency varies among algorithms, helps to suggest which one of them ought to be used to solve a specific variant of the shortest path problem.

A Note on “Orchestrating an Ensemble of MapReduce Jobs for Minimizing Their Makespan”

In paper [1], a scheduling model is considered for multiple MapReduce jobs. The goal in [1] is to design an automatic job scheduler that minimizes the makespan of such a set of MapReduce jobs. In this work, we find that there is a key assumption in [1] which leads to the violation of the conditions for classical Johnson's algorithm and a suboptimal job scheduling for minimizing total makespan. By considering a better strategy and implementation, we can still meet the conditions of classical Johnson's algorithm. Then we can still use Johnson's algorithm for an optimal solution. As for BalancedPools algorithm proposed in paper [1], under our proposed new strategy, it is possible to solve it exactly in linear time, but not NP-hard as suggested in [1], the proof is provided. With the new strategy, results obtained in [1] need reevaluating.

An Evolutionary Approach for Solving the N-Jobs M-Machines Permutation Flow-Shop Scheduling Problem with Break-Down Times

paper a Genetic Algorithm (GA) approach is presented to solve the N-Jobs M-Machines Permutation Flow-Shop Scheduling Problem (PFSP) with Break-down times. In comparison with other methods that start with a solution obtained with the Johnson's Algorithm (or another greedy approach), the presented GA method starts with randomly generated solutions and within 100 iterations is able to obtain a solution better than other methods. Also, while in other works the sequence of jobs to be processed in the machines is obtained prior to the occurrence of break-down times, the GA finds the solution considering from the beginning the occurrence of the break-down times. Thus, the presented GA method considers the effect of the break-down times in the overall process. A selection of standard 20×20 PFSPs was used for validation of the GA, finding that in 86% of the selected PFSPs the GA was able to provide job sequences with better makespans when compared with another method. The makespan improvements were statistically significant at the 0.10 and 0.01 levels. Then, evaluation of the GA was performed on one PFSP case with break-down times. As in the validation cases with standard PFSPs, the GA outperformed the results obtained with another method.

Orchestrating an Ensemble of MapReduce Jobs for Minimizing Their Makespan

Cloud computing offers an attractive option for businesses to rent a suitable size MapReduce cluster, consume resources as a service, and pay only for resources that were consumed. A key challenge in such environments is to increase the utilization of MapReduce clusters to minimize their cost. One way of achieving this goal is to optimize the execution of Mapreduce jobs on the cluster. For a set of production jobs that are executed periodically on new data, we can perform an offline analysis for evaluating performance benefits of different optimization techniques. In this work, we consider a subset of production workloads that consists of MapReduce jobs with no dependencies. We observe that the order in which these jobs are executed can have a significant impact on their overall completion time and the cluster resource utilization. Our goal is to automate the design of a job schedule that minimizes the completion time (makespan) of such a set of MapReduce jobs. We introduce a simple abstraction where each MapReduce job is represented as a pair of map and reduce stage durations. This representation enables us to apply the classic Johnson algorithm that was designed for building an optimal two-stage job schedule. We evaluate the performance benefits of the constructed schedule through an extensive set of simulations over a variety of realistic workloads. The results are workload and cluster-size dependent, but it is typical to achieve up to 10-25 percent of makespan improvements by simply processing the jobs in the right order. However, in some cases, the simplified abstraction assumed by Johnson's algorithm may lead to a suboptimal job schedule. We design a novel heuristic, called BalancedPools, that significantly improves Johnson's schedule results (up to 15-38 percent), exactly in the situations when it produces suboptimal makespan. Overall, we observe up to 50 percent in the makespan improvements with the new BalancedPools algorithm. The results of our simulation study are validated through experiments on a 66-node Hadoop cluster.

A GENETIC ALGORITHM TO MINIMIZE THE MAKESPAN FOR THREE MACHINE FLOW SHOP SCHEDULING

The aim of this paper is to propose genetic algorithm to finding the optimal schedule with minimum makespan for n jobs in flow shop environment with three machines. In this pape, a new approach for selection, a new crossover operation (3PLOX) and a new stopping criteria based on the lower bound of the makespan (LB) are proposed.Also, a new procedure of calculating the make span for n jobs at processing by three machine in flow shop environment is suggested. In order to examine the effectiveness of the proposed GA, a comparison was made with Johnson's algorithm. After the application on several problems which generated randomly by uniform distribution, the results showed that the proposed GA is better than Johnson's algorithm with rate 94% in finding the optimal sequence for scheduling jobs which gives optimal makespan.

A polynomial time heuristic for the two-machine flowshop scheduling problem with setup times and random processing times

The two-machine flowshop scheduling problem to minimize makespan is addressed. Jobs have random processing times which are bounded within certain intervals. The distributions of job processing times are not known. This problem has been addressed in the literature with the assumption that setup times are included in processing times or are zero. In this paper, we relax this assumption and treat setup times as separate from processing times. We propose a polynomial time heuristic algorithm. Both Johnson algorithm and Yoshida and Hitomi algorithm, both of which developed for the deterministic problem, are special cases of the proposed algorithm. The heuristic algorithm uses a weighted average of lower and upper bounds for processing times. For different weights, the results of the proposed algorithm are compared based on randomly generated data. The computational analysis has shown that the proposed algorithm, with equal weights given to the lower and upper bounds, performs considerably well with an overall average error of 0.36%. The analysis has also shown that the proposed algorithm can safely be used regardless of processing time distributions and the range between lower and upper bounds.

A bi-criteria flow-shop scheduling with preventive maintenance

In this study, a bi-criteria flow-shop scheduling with preventive maintenance tasks is considered. The objective function of the scheduling is the minimization of the makespan and for the preventive maintenance is the minimization of the sum of the earliness and tardiness between theoretical and actual frequency for two machines. The integer programming model is presented and can be solved only small size problems. For large size problem, four meta-heuristics methods are presented. Two genetic algorithms (NSGAII and SPEAII) are implemented. 2 new versions of NSGAII (noted HY-NSGAII) and SPEAII (noted HY-SPEAII) based on Johnson algorithm are presented. To evaluate the proposed approaches several tests and metrics are proposed.