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

NOVA METODA ZA PROJEKTIRANJE FAZNOGA RAZVOJA KOPA PRI GEOLOŠKIM NESIGURNOSTIMA BAZIRANA NA ALGORITMU OPTIMIZACIJE KOLONIJOM MRAVA

An essential task in the open-pit mine optimizing process is determining the extraction time of material located in the ultimate pit, considering some operational and economic constraints. The proper design of pushbacks has a significant impact on the optimum production planning. On the other hand, some uncertainty sources such as in-situ grade cause both deviations from production and financial goals. This paper presents an extension of a multi-stage formulation for risk-based pushback designing that utilizes the ant colony optimization (ACO) algorithm to solve it. For more detailed studies, two different strategies were developed according to statistical and probabilistic issues. The data of Songun copper mine located in NW Iran was used to evaluate the ability of the proposed approach in controlling the risk of deviation from production targets and increasing the project value. The results indicated the effectiveness of the proposed approach in pushback designing based on geological uncertainty. Examining different strategies showed that the technique based on multiple probability produces better solutions.Uzimajući u obzir neka operativna i ekonomska ograničenja, u procesu optimizacije površinskoga kopa bitan je zadatak određivanje vremena eksploatacije materijala koji se nalazi na najdubljoj etaži. Pravilan dizajn veličine zahvata etaže ima znatan utjecaj na optimalno planiranje proizvodnje. S druge strane, neki izvori nesigurnosti, kao što su terenske nepoznanice, uzrokuju odstupanja od proizvodnih i financijskih ciljeva. Ovaj članak predstavlja proširenje višesegmentnoga modeliranja za projektiranje faznoga razvoja kopa temeljenoga na riziku koji se za rješavanje koristi algoritmom optimizacije kolonijom mrava (eng. ant colony optimization, ACO). Za detaljnije proučavanje razvijene su dvije različite strategije prema statističkim i probabilističkim načelima. Za procjenu sposobnosti predloženoga pristupa u kontroli rizika odstupanja od proizvodnih ciljeva i povećanja troškova projekta korišteni su podatci iz rudnika bakra Songun koji se nalazi u sjeverozapadnome Iranu. Rezultati su pokazali učinkovitost predloženoga pristupa u projektiranju faznoga razvoja kopa kod geološke nesigurnosti. Ispitivanje različitih strategija pokazalo je kako metoda višestruke vjerojatnosti daje bolje rezultate

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases

Comparative study of heuristics algorithms in solving flexible job shop scheduling problem with condition based maintenance

Purpose: This paper focuses on a classic optimization problem in operations research, the flexible job shop scheduling problem (FJSP), to discuss the method to deal with uncertainty in a manufacturing system. Design/methodology/approach: In this paper, condition based maintenance (CBM), a kind of preventive maintenance, is suggested to reduce unavailability of machines. Different to the simultaneous scheduling algorithm (SSA) used in the previous article (Neale & Cameron,1979), an inserting algorithm (IA) is applied, in which firstly a pre-schedule is obtained through heuristic algorithm and then maintenance tasks are inserted into the pre-schedule scheme. Findings: It is encouraging that a new better solution for an instance in benchmark of FJSP is obtained in this research. Moreover, factually SSA used in literature for solving normal FJSPPM (FJSP with PM) is not suitable for the dynamic FJSPPM. Through application in the benchmark of normal FJSPPM, it is found that although IA obtains inferior results compared to SSA used in literature, it performs much better in executing speed. Originality/value: Different to traditional scheduling of FJSP, uncertainty of machines is taken into account, which increases the complexity of the problem. An inserting algorithm (IA) is proposed to solve the dynamic scheduling problem. It is stated that the quality of the final result depends much on the quality of the pre-schedule obtained during the procedure of solving a normal FJSP. In order to find the best solution of FJSP, a comparative study of three heuristics is carried out, the integrated GA, ACO and ABC. In the comparative study, we find that GA performs best in the three heuristic algorithms. Meanwhile, a new better solution for an instance in benchmark of FJSP is obtained in this research.Peer Reviewe

A survey on metaheuristics for stochastic combinatorial optimization

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel

Reactive scheduling to treat disruptive events in the MRCPSP

Esta tesis se centra en diseñar y desarrollar una metodología para abordar el MRCPSP con diversas funciones objetivo y diferentes tipos de interrupciones. En esta tesis se exploran el MRCPSP con dos funciones objetivo, a saber: (1) minimizar la duración del proyecto y (2) maximizar el valor presente neto del proyecto. Luego, se tiene en cuenta dos tipos diferentes de interrupciones, (a) interrupción de duración, e (b) interrupción de recurso renovable. Para resolver el MRCPSP, en esta tesis se proponen tres estrategias metaheurísticas: (1) algoritmo memético para minimizar la duración del proyecto, (2) algoritmo adaptativo de forrajeo bacteriano para maximizar el valor presente neto del proyecto y (3) algoritmo de optimización multiobjetivo de forrajeo bacteriano (MBFO) para resolver el MRCPSP con eventos de interrupción. Para juzgar el rendimiento del algoritmo memético y de forrajeo bacteriano propuestos, se ha llevado a cabo un extenso análisis basado en diseño factorial y diseño Taguchi para controlar y optimizar los parámetros del algoritmo. Además se han puesto a prueba resolviendo las instancias de los conjuntos más importantes en la literatura: PSPLIB (10,12,14,16,18,20 y 30 actividades) y MMLIB (50 y 100 actividades). También se ha demostrado la superioridad de los algoritmos metaheurísticos propuestos sobre otros enfoques heurísticos y metaheurísticos del estado del arte. A partir de los estudios experimentales se ha ajustado la MBFO, utilizando un caso de estudio.DoctoradoDoctor en Ingeniería Industria

Resource Schedule of Concrete Fish Pond Construction Using Network Analysis

In the construction of building, preparation of bid, maintenance and planning of oil refinery and preparation for agricultural activities, there is a need to know the completion days of the project without delay and the earliest time and the latest time for which each activity will take. It was based on this that we decide to analyze the construction of concrete fish pond using Network Analysis through the use of Critical Path Method (CPM) and Program Evaluation Review Technique (PERT). Sixty-four days was arrived at for the completion of the construction using CPM while sixty-eight days with 99% probability was arrived at using PERT method. In deciding which of the method is best suitable for the construction of the fish pond, PERT serve as the best method due to the fact that it considers the Pessimistic Time (longest time possible and can be seen as usual delay) and Optimistic Time (shortest time possible if things go perfectly) as well as the probability [which is 99%] of completing the task within a specific time. The result established some useful facts for researchers in this area as well as managers of industry in carrying out their study from the feasibility stage to the other stages so as to have a good practical target towards the completion of the project as planned. Keyword: Network Analysis, Critical Path Method, Program Evaluation Review Technique, Pessimistic Time, Optimistic Time and Probability DOI: 10.7176/JMCR/57-04 Publication date:June 30th 201

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities

Overview of Multi-Objective Optimization Approaches in Construction Project Management

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios