A Bilevel Model for Project Scheduling in a Fuzzy Random Environment

Abstract

In this paper, a new bilevel model with multiple decision-makers is proposed for a project scheduling problem (PSP) which considers the interests of both the project owner and contractor. In this model, the project owner is considered to be the leader and the contractor, the follower. The project owner has two objectives: 1) to maximize profit and 2) minimize makespan, while the contractor's objective is to minimize cost only. A fuzzy random simulation-based bilevel global-local-neighbor particle swarm optimization technique is proposed to solve the multiple decision-maker PSP (MDPSP). A case study based on the Wanjiakouzi Hydropower Station is used to demonstrate an application of the developed model and illustrate the scope of the MDPSP.Index Terms-Bilevel programming (BP), fuzzy random variables, multiple decision-makers, particle swarm optimization (PSO), project scheduling. NOMENCLATURE Notations for the Mathematical ModelTo facilitate the mathematical description, the following notations are introduced.

Resource-constrained project scheduling problem: review of past and recent developments

“…In a complicated environment, where there are more than one type of uncertainty, Ke and Ma (2014) presented three time-cost tradeoff models with the titles of expected cost minimization model, ( , )   -cost minimization model and chance maximization model, in which the project environment was described by introducing the random fuzzy theory. Moreover, Xu et al (2015) presented a bi-level model with several decision makers for a project scheduling problem in a random fuzzy environment, where both interests of the project owner (maximization of benefit and minimization of project completion time) and contractor (minimization of costs) were considered. Cheng et al (2016) introduced a new optimization model with the title of Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) for solving Resource Leveling (FCDE-RL), where FCDE was developed by integration of differential evolution with c-mean fuzzy clustering and chaos techniques for solving complex optimization problems.…”

A Practical and Robust Execution Time-Frame Procedure for the Multi-Mode Resource-Constrained Project Scheduling Problem with Minimal and Maximal Time Lags

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