Resource Constrained Project Scheduling: a Hybrid Neural Approach

Çolak, Selçuk; Agarwal, Alekh; Erengüç, Ş. Selçuk

doi:10.1007/978-0-387-33768-5_12

Cited by 10 publications

(16 citation statements)

References 52 publications

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“…In the former paradigm, a priority list of activities is determined at time zero, with the order of activities in the priority list independent of the resource constraints. Given the priority list, activities are scheduled at the earliest possible time at which the precedence constraints are satisfied and the resources are available [16][17][18][19][20]. In the latter sche-duling paradigm, the order of activities is not determined at time zero and scheduling decisions are made when activities are planned to begin and resources are available.…”

Section: Mathematical Formulationmentioning

confidence: 99%

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Activity Prioritization Under Resource Constraints Using a Utility Index Method

Aslani¹,

Christodoulou²,

Griffis³

et al. 2009

TOBCTJ

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Abstract:Resource availability constraints are a typical real-life construction scheduling problem; a problem that limits a constructor's ability to execute and deliver a project as originally planned. It is, thus, imperative that developed project schedules should have not only well-thought project logic networks (successor/predecessor information and activity durations) but also resource assignments (including cost) for each activity in the network so that the effects of resource constraints can effectively be accounted for. The paper presents a new approach to resource-constrained scheduling that allows for activity prioritization when a project is subject to limited resources. The methodology proposed is based on a utility index, hereby defined as the ratio of the number of required resources for a specific activity to the total number of required resources among competing activities. A dynamic programming technique is adopted to maximize the utility value for each activity so that the resource allocation among competing activities, as suggested by the method, results in the minimum overall project duration.

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Section: Mathematical Formulationmentioning

confidence: 99%

“…Furthermore, we define x(w) the strategy that attains the maximum in Eq.5 and, x(w) = 0 , to mean that no resource combination can be used ( [8,16]). …”

Section: Mathematical Formulationmentioning

confidence: 99%

Activity Prioritization Under Resource Constraints Using a Utility Index Method

Aslani¹,

Christodoulou²,

Griffis³

et al. 2009

TOBCTJ

View full text Add to dashboard Cite

show abstract

“…The GA approach has shown remarkable success in solving the RCPSP and is one of the most preferred approaches for this problem. Colak et al [2] proposed a NN-based approach which also gave very competitive results for this problem. Although both GA and NN-based approaches give some of the best known results in the literature, the two approaches are very different from each other in terms of search strategies.…”

Section: Introductionmentioning

confidence: 97%

A Neurogenetic approach for the resource-constrained project scheduling problem

Agarwal

Çolak

Erengüç

2011

Computers & Operations Research

Self Cite

111

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“…We call the proposed hybrid metaheuristic the NeuroGenetic approach (NG). Although the AugNN approach is relatively new compared to other long-established approaches such as Tabu Search and Simulated Annealing, the AugNN approach has been shown to outperform Tabu Search and Simulated Annealing for certain scheduling problems (Colak and Agarwal 2005;Colak et al 2006). In the next section we discuss the motivation behind our choice of combining the AugNN and the GA approaches and discuss the challenges involved in hybridizing them.…”

Section: Introductionmentioning

confidence: 98%

NeuroGenetic approach for combinatorial optimization: an exploratory analysis

2009

Self Cite

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Given the NP-Hard nature of many optimization problems, it is often impractical to obtain optimal solutions to large-scale problems in reasonable computing time. For this reason, heuristic and metaheuristic search approaches are used to obtain good solutions fast. However, these techniques often struggle to develop a good balance between local and global search. In this paper we propose a hybrid metaheuristic approach which we call the NeuroGenetic approach to search for good solutions for these large scale optimization problems by at least partially overcoming this challenge. The proposed NeuroGenetic approach combines the Augmented Neural Network (AugNN) and the Genetic Algorithm (GA) search approaches by interleaving the two. We chose these two approaches to hybridize, as they offer complementary advantages and disadvantages; GAs are very good at searching globally, while AugNNs are more proficient at searching locally. The proposed hybrid strategy capitalizes on the strong points of each approach while avoiding their shortcomings. In the paper we discuss the issues associated with the feasibility of hybridizing these two approaches and propose an interleaving algorithm. We also provide empirical evidence demonstrating the effectiveness of the proposed approach.

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Resource Constrained Project Scheduling: a Hybrid Neural Approach

Cited by 10 publications

References 52 publications

Activity Prioritization Under Resource Constraints Using a Utility Index Method

Activity Prioritization Under Resource Constraints Using a Utility Index Method

A Neurogenetic approach for the resource-constrained project scheduling problem

NeuroGenetic approach for combinatorial optimization: an exploratory analysis

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