The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to a nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, we convert the probabilistic state constraints to deterministic constraints, and obtain a tractable deterministic receding horizon control problem. We consider the receding control law to have a linear state-feedback and an admissible offset term. We ensure mean square boundedness of the state variable via solving linear matrix inequalities off-line, and solve the receding horizon control problem on-line with control offset terms. We illustrate the overall approach applied on a macroeconomic system

Spatial model for the management of a system of infrastructure facilities

Infrastructure asset management is an important tool that provides decision makers with optimized plans for the maintenance, repair and rehabilitation of systems of infrastructures facilities. The optimization is performed in terms of a limited budget and a pre-defined duration. The first step of this thesis was to create three optimization models. First, a standard model that fragments the asset into fractions distributed over the possible conditions to which actions are applied until the assets reach the targeted conditions over the plan\u27s horizon and within budget. Second, was a robust model that assessed the worst case scenario by the integration of uncertainty of the deterioration of the asset. Finally, a Hurwics Criterion model that enhanced the previous models by integrating a level of optimisms to reflect realistic scenarios in which the worst case would not necessarily occur. These models were implemented in a linear and nonlinear integer technique. These models assumed that the asset can be segmented, and then grouped by percentage and assigned to a certain condition. However, in the case of continuous stretches of assets such as pavements, it was noted that this technique does not take into account the distances between the segments of the asset from one another, which was the main challenge this thesis focused on. In order to overcome this gap, a Spatial Model was developed, upgrading the available models to account for the distances. All seven models were then applied on a real case study, which is the Ring Road surrounding Greater Cairo. It was found that the linear integer models have an impact on both the duration of the optimization exercise and the goodness of the final results. Moreover, the Robust Model always gave higher expenses as opposed to the others as it customized itself for the worst case scenario. Furthermore, the Hurwics criterion model once assigned an optimism level different than zero allowed the overall expenses to decrease. Finally, the Spatial model was tested. It included a simulation that was set to give the minimum score and in order to verify the model, the mean and maximum simulations were carried out and as expected they gave higher costs. Finally all seven models were validated through 10 experts that have tested the models

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems