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

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

Resource allocation in multi-class dynamic PERT networks with finite capacity

In this paper, the resource allocation problem in multi-class dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)) is studied. The dynamic PERT network is modeled as a queuing network, where new projects from different classes (types) are generated according to independent Poisson processes with different rates over the time horizon. Each activity of a project is performed at a devoted service station with one server located in a node of the network, whereas activity durations for different classes in each service station are independent and exponentially distributed random variables with different service rates. Indeed, the projects from different classes may be different in their precedence networks and also the durations of the activities. For modeling the multi-class dynamic PERT . networks with CONPIP, we first consider every class separately and convert the queueing network of every class into a proper stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to compute the project completion time distribution for any particular project. The problem is formulated as a multi-objective model with three objectives to optimally control the resources allocated to the service stations. Finally, we develop a simulated annealing (SA) algorithm to solve this multi-objective problem, using the goal attainment formulation.We also compare the SA results against the results of a discrete-time approximation of the original optimal control problem, to show the effectiveness of the proposed solution technique.N/

MULTI-OBJECTIVE STRATEGY FOR OPTIMIZING REPETITIVE CONSTRUCTION PROJECTS USING LINEAR PROGRAMMING MODELS

Decision making has become much more complicated than in the past due to increased decision alternatives, uncertainty, and cost of making errors. As a result, it is very difficult to rely on a trial and error approach in decision making. Nowadays business managers are dealing with different types of projects ranging from implementing a large scale manufacturing plant to a simple sales campaign. While dealing with projects, to become competitive, sometimes it is required to complete a project within the predetermined deadline to keep cost at lowest possible level. Failure to do so ultimately leads to increase in total cost. This would direct managers to encounter a decision situation: which activities of the project will be crashed to minimize the total cost of crashing project. In this paper, we provide a hypothetical example to clarify the framework of how to convert from LOB to CPM and then how to create a model to crash a project time to reach an optimum time-cost solution. Microsoft Excel custom made sheets used to the conversion, also Solver add-in used to solve the model while it implements Linear Programming. As a check, results from Solver and LiPS software are compared

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction

Scheduling Problem under Constrained Resources: A Historical Review of Solution Methods and Computer Application

In construction projects, project execution time is a major concern of the involved stakeholders (client, contractors and consultants). Optimization of project scheduling through time control is considered as a critical factor in project management. Many studies were carried out and many models and software packages were developed since the fifties and till now, but no clear cut methods, to optimize resources while satisfying different constraints were found. The importance of the subject stemmed from the fact that project time completion affects the overall project cost. Considering the most two widely applied scheduling methods: Critical Path method (CPM) and Program Evaluation and Review Technique (PERT), it is found that negligence of handling limitation of resources is evident in most cases. On the other hand, a resource leveling technique which is used to reduce the sharp variations in the resource demand histogram cannot handle the issue of1 minimizing project duration since it is used when there are enough resources. So the leveling process is accomplished by shifting only the non-critical activities within their floats. This paper show a number of heuristics and models to solve scheduling problem of projects subjected to limited resources. Different heuristic methods applied in past studies were examined in order to be tested and applied in a simple example, as a pilot study, so as to be used in real complex projects

Project management techniques for highly integrated programs

The management and control of a representative, highly integrated high-technology project, in the X-29A aircraft flight test project is addressed. The X-29A research aircraft required the development and integration of eight distinct technologies in one aircraft. The project management system developed for the X-29A flight test program focuses on the dynamic interactions and the the intercommunication among components of the system. The insights gained from the new conceptual framework permitted subordination of departments to more functional units of decisionmaking, information processing, and communication networks. These processes were used to develop a project management system for the X-29A around the information flows that minimized the effects inherent in sampled-data systems and exploited the closed-loop multivariable nature of highly integrated projects

Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking

When shortening a project’s duration, activity crashing, fast-tracking and substitution are the three most commonly employed compression techniques. Crashing generally involves allocating extra resources to an activity with the intention of reducing its duration. To date, the activity time-cost relationship has for the most part been assumed to be linear, however, a few studies have suggested that this is not necessarily the case in practice. This paper proposes two non-linear theoretical models which assume either collaborative or non-collaborative resources. These models closely depict the two most common situations occurring during construction projects. The advantages of these models are that they allow for both discrete and continuous, as well as deterministic and stochastic configurations. Additionally, the quantity of resources required for crashing the activity can be quantified. Comparisons between the models and another recent fast-tracking model from the literature are discussed, and a Genetic Algorithm is implemented for a fictitious application example involving both compression techniques