A genetic algorithm for resource investment problem with discounted cash flows

Najafi, Amir Abbas; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.amc.2006.05.118

Cited by 39 publications

(29 citation statements)

References 23 publications

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“…One of the specific problems in project scheduling is resource investment problem, in which the accessibility level to resources is regarded as decision variable, and a scheduling timetable for activities along with an accessibility level to resources are obtained according to the optimization of a specific objective. Therefore, Najafi and Niaki (2005) considered a resource investment problem, aiming to maximize the project NVP. In addition, they proposed a solution method for this type of problems, which are recognized as resource investment problem with discounted cash flows.…”

Section: Net Present Value (Npv)mentioning

confidence: 99%

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

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

105

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The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the interesting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Resource-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four aspects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

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Section: Net Present Value (Npv)mentioning

confidence: 99%

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

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

105

View full text Add to dashboard Cite

show abstract

“…Vanhoucke et al [183] assume the cash flow of an activity to be a linear non-increasing function of the activity's completion time. Najafi and Niaki [131] consider a cash flow related to a subset of activities, that is, a cash flow is initiated when the last activity of the subset is finished. Furthermore, the cash flow associated with activity j might depend on the mode chosen for j as lined out in Icmeli and Erenguc [91].…”

Section: Objectives Based On the Net Present Valuementioning

confidence: 99%

“…In the notation of Brucker et al [31], the objective to optimize the NPV is represented by ∑ c F j β C j for γ, where c F j is the cash flow of activity j and β is the discount factor. Furthermore, NPV objectives have been investigated for the multi-mode RCPSP (see Ulusoy et al [176], Varma et al [187], and Waligóra [190]), the resource investment problem (see Najafi and Niaki [131]), and the RCPSP with minimal and maximal time lags (see Neumann and Zimmermann [135,137]). Icmeli and Rom [92] employ the NPV objective in a problem with continuous activity durations and time-dependent resource capacities.…”

Section: Objectives Based On the Net Present Valuementioning

confidence: 99%

A survey of variants and extensions of the resource-constrained project scheduling problem

Hartmann

Briskorn

2010

European Journal of Operational Research

738

356

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“…In [66], at specific time periods cash inflows related to activity i occur and they are proportional to the fraction of i being completed at that moment. Najafi and Niaki [67] considered a cash flow that is initiated when the last activity of the subset is finished.…”

Section: Financial Indicatorsmentioning

confidence: 99%

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

Chen¹,

Liang

Padilla

2014

Entropy

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Abstract:Projects are an important part of our activities and regardless of their magnitude, scheduling is at the very core of every project. In an ideal world makespan minimization, which is the most commonly sought objective, would give us an advantage. However, every time we execute a project we have to deal with uncertainty; part of it coming from known sources and part remaining unknown until it affects us. For this reason, it is much more practical to focus on making our schedules robust, capable of handling uncertainty, and even to determine a range in which the project could be completed. In this paper we focus on an approach to determine such a range for the Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP), a widely researched, NP-complete problem, but without adding any subjective considerations to its estimation. We do this by using a concept well known in the domain of thermodynamics, entropy and a three-stage approach. First we use Artificial Bee Colony (ABC)-an effective and powerful meta-heuristic-to determine a schedule with minimized makespan which serves as a lower bound. The second stage defines buffer times and creates an upper bound makespan using an entropy function, with the advantage over other methods that it only considers elements which are inherent to the schedule itself and does not introduce any subjectivity to the buffer time generation. In the last stage, we use the ABC algorithm with an objective function that seeks to maximize robustness while staying within the makespan boundaries defined OPEN ACCESSEntropy 2014, 16 5033 previously and in some cases even below the lower boundary. We evaluate our approach with two different benchmarks sets: when using the PSPLIB for the MRCPSP benchmark set, the computational results indicate that it is possible to generate robust schedules which generally result in an increase of less than 10% of the best known solutions while increasing the robustness in at least 20% for practically every benchmark set. And, in an attempt to solve larger instances with 50 or 100 activities, we also used the MRCPSP/max benchmark sets, where the increase of the makespan is approximately 35% with respect to the best known solutions at the same time as with a 20% increase in robustness.

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A genetic algorithm for resource investment problem with discounted cash flows

Cited by 39 publications

References 23 publications

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

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

A survey of variants and extensions of the resource-constrained project scheduling problem

An Entropy-Based Upper Bound Methodology for Robust Predictive Multi-Mode RCPSP Schedules

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