Optimal project duration for resource leveling

Atan, Tankut; Eren, Elif

doi:10.1016/j.ejor.2017.10.022

Cited by 19 publications

(7 citation statements)

References 14 publications

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“…A mixed-integer linear model in which the decision variable and metric are the PCT and RRH, respectively, is proposed. It provides a heuristic approach for an NP-hard problem in a large network with deterministic activity duration (Atan & Eren, 2018). A meta-heuristic simulated annealing RL model, which hybridizes the LOB and constraint programming, considers limitations in resource availabilities, and maintains resource continuity while minimiz-ing the occurrence of idle time, handling comprehensive constraints, and identifying explicitly new constraints with visualization (Piryonesi, Nasseri, & Ramezani, 2019).…”

Section: Deterministic Resource Leveling Methodsmentioning

confidence: 99%

Stochastic resource leveling optimization method for trading off float consumption and project completion probability

Gwak

Lee

2021

Computer aided Civil Eng

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Existing resource leveling (RL) approaches fall short of analyzing the trade-off relation between the total float consumption and project completion probability in a real-life project RL problem. This article presents a stochastic resource leveling optimization (SOLO) method that minimizes the total float consumption along with maximizing the project completion probability. It initializes the earliest start times of noncritical activities, measures the level of resource fluctuations of each candidate solution, computes the probability of completing a project in a target deadline by executing simulation-based scheduling, and identifies optimal solution(s) (i.e., optimal start times of noncritical activities) by implementing genetic algorithm, thereby identifying an optimal resource-leveled baseline.The study is of value to practitioners because SOLO considers both the amount of total float consumption and project completion probability. This study facilitates experimentation with different computation time-saving options given various constraints (i.e., the residual of project completion probabilities, threshold of release and rehire, ratio of criticality index, and number of critical activities). Test cases verify the validity of the computational method.How to cite this article: Gwak H-S, Lee D-E. Stochastic resource leveling optimization method for trading off float consumption and project completion probability.

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Section: Deterministic Resource Leveling Methodsmentioning

confidence: 99%

Stochastic resource leveling optimization method for trading off float consumption and project completion probability

Gwak

Lee

2021

Computer aided Civil Eng

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“…This aids in reducing repeated hiring and firing of resources, minimizes logistic issues and facilitates cost saving (Tang et al, 2018). If the project duration can be extended, a better resource balancing schedule can be prepared because longer periods have more floating time (Atan and Eren, 2018; contradicted the typical assumption that projects do not allow interruptions during execution and proposed that it is beneficial to consider preemption when allocating resources. Academics have proposed a large number of accurate and imprecise algorithms to solve RLP.…”

Section: Review Of Literaturementioning

confidence: 99%

Labor resource allocation under extremely short construction period based on the inverse optimization method

Peng

Liu

2022

ECAM

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PurposeThis research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.Design/methodology/approachThe method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.FindingsThe case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.Originality/valueThe contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.

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“…Dupont et al [43] used a mixed integer linear programming model to show profit-and-loss targets. Atan and Eren [44] established mixed integer linear methods for several leveling objectives by using a heuristic algorithm.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

A Hybrid Approach for Project Crashing Optimization Strategy with Risk Consideration: A Case Study for an EPC Project

Ouyang

Chen

2019

Mathematical Problems in Engineering

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This study aims to develop and provide a comprehensive evaluation strategy for schedule-related variations and time-cost analysis for an engineering–procurement–construction (EPC) project. Time-cost analysis is an important aspect of project scheduling, particularly in long-term and costly EPC projects. In this study, a hybrid method is proposed for the time-cost optimization strategy evaluation of a project. Monte Carlo simulation is applied to determine contingency plans and realize the effective management of estimated schedule uncertainties. A mathematical integer linear programming optimization model coded using CPLEX is developed to assess appropriate strategies for project execution under time and cost constraints. A set of project evaluation optimization models considering risk and project crash plan and the relationship between crash cost and delay penalty is also developed for assessing project feasibility. The correlation between project risk and crashing strategy has seldom been evaluated simultaneously in previous research. This work fills this research gap by quantifying the feasibility of a project, with combined data on risk, schedule, and cost as evaluation indicators. It allows project managers to consider management issues and strategies before they implement projects. A practical example with numerical applications is presented to illustrate the contribution of the decision-making support mechanism, and several managerial insights are provided.

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Optimal project duration for resource leveling

Cited by 19 publications

References 14 publications

Stochastic resource leveling optimization method for trading off float consumption and project completion probability

Stochastic resource leveling optimization method for trading off float consumption and project completion probability

Labor resource allocation under extremely short construction period based on the inverse optimization method

A Hybrid Approach for Project Crashing Optimization Strategy with Risk Consideration: A Case Study for an EPC Project

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