Najoua Dridi

An improved heuristic for two-machine flow shop scheduling with an availability constraint and nonresumable jobs

In this paper, we deal with the two-machine flow shop scheduling problem having an unavailability interval on the first machine, and nonresumable jobs. We first present an enhancement procedure that, once applied to any arbitrary solution, produces a schedule that is at most equal 2 times the optimal makespan. We then develop an improved heuristic, with a relative worst-case error of 3/2.

A survey on human resource allocation problem and its applications

Abstract In this paper, we present the existing literature on the human resource allocation problem. We study the main resolution approaches: exact, heuristic and metaheuristics methods proposed to solve the problem. We also examine the different studies from state-of the-art and classify them according to their real life applications in several areas such as production planning applications, maintenance management, hospital systems, project management, etc. Finally, we discuss and analyze the different contributions examined in this field.

A note on the two-stage hybrid flow shop problem with dedicated machines

In this paper we introduce a new dominance rule for the two-stage hybrid flow shop problem with dedicated machines. The rule is then used to construct a dominating set. The efficiency of the proposed rule is shown through an analysis of the dominating set cardinality.

The Two-stage Assembly Flow Shop Scheduling with an Availability Constraint: Worst Case Analysis

In this paper, we are interested in handling the limited availability of machines in the two-stage assembly flow shop scheduling problems. Emphasis is put on the semiresumable case with respect to the minimization of the makespan. We provide, when possible, heuristics with a tight worst-case ratio bound of 2.

On the two-stage hybrid flow shop with dedicated machines

In this paper we develop new elimination rules and discuss several polynomially solvable cases for the two-stage hybrid flow shop problem with dedicated machines. We also propose a worst case analysis for several heuristics. Furthermore, we point out and correct several errors in the paper of Yang [J. Yang, A two-stage hybrid flow shop with dedicated machines at the first stage. Comput. Oper. Res. 40 (2013) 2836−2843].

Analyses toward a prediction system for a large-scale volunteer computing system

Computing resources in volunteer computing grid represent a big under-used reserve of processing capacity. However, a task scheduler has no guarantees regarding the deliverable computing power of these resources. Predicting CPU availability can help to better exploit these resources and make effective scheduling decisions. In this paper, we draw up the main guidelines to develop a scalable method to predict CPU availability in a large-scale volunteer computing system. To reduce solution time and ensure precision, we use simple prediction techniques precisely Autoregressive models and tendency-based strategy. To address the limitations of autoregressive models, we propose an automated approach to check whether time series satisfy the assumptions of the models and to construct a prediction model by identifying its appropriate order value. At each prediction, we consider autoregressive models over three different past analyses: first over the recent hours, second during the same hours of the previous days and third during the same weekly hours of the previous weeks. We analyze the performance of multivariate vector autoregressive models (VAR) and pure autoregressive models (AR), constructed according to our approach, against the tendency prediction technique using traces of a large-scale Internet-distributed computing system, termed seti@home.

Research on the optimal parameters of ACO algorithm for a human resource allocation problem

In this work, a human resource assignment problem, in an assembly line, is presented and solved. We have to optimize the allocation of workers to tasks and to stations, in order to increase the productivity of the production line. This problem is called Assembly Line Worker Assignment and Balancing Problem (ALWABP). To solve the problem, an Ant Colony Optimization (ACO) algorithm is applied. A Numerical study is carried out to investigate the effect of some parameters of the ACO method on the quality of the obtained solution.

The two-stage hybrid flow shop problem with dedicated machines under release dates and delivery times

The hybrid flow shop scheduling problem has been extensively examined where the main objective has been to improve production efficiency. However, for contemporary manufacturing firms, the due date related performance has gained a significant managerial importance in real life production. Therefore, in this study we consider the integration of both release dates and delivery times for the jobs. We develop several lower bounds and five heuristics. We also implement and test different variants of tabu search. An extensive computational study shows that the proposed methods yield good results within moderate CPU time.

On the optimality conditions of the two-machine flow shop problem

This paper tackles the makespan minimization for the well known two-machine flow shop problem. Several optimality conditions are discussed aiming at the characterization of a large set of optimal solutions. We show that our approach dominates some of the results found in the literature. We also establish a number of necessary optimality conditions and discuss the asymptotic behavior of the optimal makespan when the number of jobs goes to infinity.

Cross-correlation analyses toward a prediction system of CPU availability in volunteer computing system

Computing resources in volunteer computing grid represent a big under-used reserve of processing capacity. However, a task scheduler has no guarantees regarding the deliverable computing power of these resources. Predicting CPU availability can help to better exploit these resources and make effective scheduling decisions. In this paper, we draw up the main guidelines to develop a method to predict CPU availability in a large-scale volunteer computing system. To reduce solution time and ensure precision, we use simple prediction techniques, precisely Autoregressive models and tendency-based strategy. To address the limitations of autoregressive models, we propose an automated approach to check whether time series satisfy the assumptions of the models and to construct the prediction model. At each prediction, we consider autoregressive models over three different past analyses: first over the recent hours, second during the same hours of the previous days and third during the same weekly hours of the previous weeks. We analyze the performance of multivariate vector autoregressive models (VAR) and pure autoregressive models (AR), constructed according to our approach, against the tendency prediction technique. We study the impact of the cross-correlation between the CPU availability indicators on the performance of VAR models. We used traces of a large-scale Internet-distributed computing system, termed seti[at]home.