A fuzzy random resource-constrained scheduling model with multiple projects and its application to a working procedure in a large-scale water conservancy and hydropower construction project

Abstract

The aim of this paper is to deal with resourceconstrained multiple project scheduling problems (rc-mPSP) under a fuzzy random environment by a hybrid genetic algorithm with fuzzy logic controller (flc-hGA), to a large-scale water conservancy and hydropower construction project in the southwest region of China, whose main project is a dam embankment. The objective functions in this paper are to minimize the total project time (that is the sum of the completion time for all projects) and to minimize the total tardiness penalty of multiple projects, which is the sum of penalty costs for all the projects. After describing the problem of the working procedure in the project and presenting the mathematical formulation model of a resourceconstrained project scheduling problem under a fuzzy random environment, we give some definitions and discuss some properties of fuzzy random variables. Then, a method of solving solution sets of fuzzy random multiple objective programming problems is proposed. Because traditional optimization techniques could not cope with the rc-mPSP under a fuzzy random environment effectively, we present a new approach based on the hybrid genetic algorithm (hGA).In order to improve its efficiency, the proposed method hybridized with the fuzzy logic controller (flc) concept for auto-tuning the GA parameters is presented. For the practical problems in this paper, flc-hGA is proved the most effective and most appropriate compared with other approaches. The computer generated results validate the effectiveness of the proposed model and algorithm in solving large-scale practical problems.

A Two-Stage Resource-Constrained Project Scheduling Model with Proactive and Reactive Strategies Under Uncertainty

“…There we use the conclusion which is proved by Xu and Zhang [17]. These conclusion can be described as follows, ifr ik = (a, ρ, b), with ρ ∼ N (μ, σ 2 ).…”

“…To cope with the hybrid uncertainty in the proactive and reactive scheduling process for RCPSP, we employ the fuzzy random variables in this study. Actually, the fuzzy random variable has been successful used in many areas, such as a large-scale water conservancy and hydropower construction project [17], queuing problems [15], inventory problems [1], portfolio problems [7], renewal reward processes [16], and so on. These studies show the efficiency of fuzzy random variables in handling a uncertain environment where includes fuzziness and randomness.…”

New product development resource forecasting

“…Non-linear models fall into two main groups: artificial neural networks (ANNs) and tree-and rule-based models. More obscure categories exist for example genetic programming (Burgess and Lefley, 2001), fuzzylogic models (Xu and Zhang, 2012) and Bayesian statistics (Chulani et al, 1999). The more obscure methods are usually initiated by or integrated within ANNs and can be described by the same benefits and limitations.…”

Credibility based chance constrained programming for project scheduling with fuzzy activity durations

“…They proposed a multi objective nonlinear mathematical model and an algorithm for solving the problem. Some of the other recent papers in the literature are conducted by Castro-Lacouture et al [25], Afshar and Fathi [26], Shi and Gong [27], Wang and Huang [28] , Bhaskar et al [29], Ponz-Tienda et al [30], Maravas and Pantouvakis [31], Xu and Zhang [32], Masmoudi and Haït [33], Huang et al [34] , Chrysafis and Papadopoulos [35], Huang et al [36], Yu et al [37], Yousefli [38], Zohoori et al [39], Habibi et al [40] Alipouri et al [41] and Birjandi and Mousavi [42]. As seen from the literature review of this paper, fuzzy project scheduling problems have been mostly investigated with fuzzy ranking methods, fuzzy simulation, and heuristic or metaheuristic.…”