Optimal resource leveling of multi-resource projects

Younis, Mona; Saad, B.

doi:10.1016/0360-8352(96)00116-7

Cited by 47 publications

(22 citation statements)

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“…As with resource constrained scheduling, both algorithms and heuristics have been reported for resource levelling. Methods that aim to produce optimal schedules have been constructed by Easa (1997), Bandelloni, Tucci, and Rinaldi (1994), Demeulemeester (1995), Younis and Saad (1996), and Savin, Alkass, and Fazio (1996). The last technique is based on a neural network model.…”

Section: Minimum and Maximum Time Lags And Resource Levellingmentioning

confidence: 99%

Scheduling operations for the harvesting of renewable resources

Foulds

Wilson

2005

Journal of Food Engineering

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Section: Minimum and Maximum Time Lags And Resource Levellingmentioning

confidence: 99%

Scheduling operations for the harvesting of renewable resources

Foulds

Wilson

2005

Journal of Food Engineering

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“…The exact methods include enumeration, integer programming, and dynamic programming techniques which obtain the global optimal solutions. The literature features research efforts that employ exact methods including Petrovic (1969), Ahuja (1976), Easa (1989), Younis and Saad (1996), Bandelloni et al (1994), and Neumann and Zimmermann (2000). The major drawback of the exact methods is that only networks of small number of activities can be solved (Zhao et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Devising extended-duration schedules of enhanced resource leveling

Rahman

Elazouni

2015

Can. J. Civ. Eng.

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The use of resource management techniques is crucial to resolve conflicts and achieve the efficient utilization of resources. Particularly, resource-leveling techniques schedule activities in unconstrained-resource conditions to minimize fluctuations in resource profiles. According to the literature, resource leveling has typically been performed by considering that the original project duration remains fixed. Virtually, some extension in the project duration might be acceptable should a considerable enhancement in resource leveling be achieved. This paper enhances resource leveling through devising schedules of extended duration that exhibit resource profiles of lower fluctuation. Critical path method (CPM) networks of extended total floats are utilized to provide expanded yet definite spaces to search for schedules of lower resource fluctuation. The modified CPM networks accommodate for employing optimization models and searching optimal or near-optimal solutions. For demonstration, a genetic algorithm model was formulated to solve two case-study networks of 30 and 120 activities. The results indicate that schedules of lower fluctuation in resource profiles were obtained beyond the original networks' duration.Résumé : L'utilisation de techniques de gestion des ressources est primordiale si l'on désire résoudre les conflits et utiliser efficacement les ressources. Plus particulièrement, les techniques de répartition des ressources permettent d'établir l'horaire des activités dans des conditions de ressources non contraintes afin de minimiser les fluctuations des ressources. Selon la littérature, la répartition des ressources était normalement réalisée en considérant que la durée initiale du projet était fixe. Pratiquement, une certaine prolongation de la durée du projet pourrait être acceptable si une amélioration considérable de la répartition des ressources était réalisée. Le présent article porte sur la répartition des ressources en mettant au point des horaires à durée prolongée qui montrent une plus faible fluctuation des ressources. Dans la méthode du chemin critique, les réseaux à marge flottante totale sont utilisés pour fournir des espaces agrandis quoique définis pour trouver des horaires comportant une plus faible fluctuation des ressources. Les réseaux modifiés de la méthode du chemin critique s'adaptent à l'utilisation de modèles d'optimisation et à la recherche de solutions optimales -quasi optimales. À des fins de démonstration, un modèle d'algorithme génétique a été formulé pour résoudre deux études de cas de réseaux comportant respectivement 30 et 120 activités. Les résultats indiquent que les horaires ayant des fluctuations de ressources plus faibles ont été obtenus au-delà de la durée originale des réseaux. [Traduit par la Rédaction]

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“…Simultaneously, increasing competition forces manufacturers to achieve low unit costs by utilizing renewable production resources (e.g. machines and people) and by minimizing the inventory (Leachman et al 1996, Younis and Saad 1996, Neumann and Zimmermann 1999. Both the input inventory of purchased materials waiting for processing in the system and the output inventory of finished products waiting for delivery to the customer should be minimized.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical approach to production scheduling in make-to-order assembly

Sawik

2006

International Journal of Production Research

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This paper presents a hierarchical framework and integer programming formulations for multi-objective production scheduling in a make-to-order manufacturing environment. Integer programming formulations are proposed for the long-term assignment of customer orders to planning periods and the short-term machine assignment and scheduling of production lots in a hybrid flow shop with multi-capacity machines and due-date-related performance measures. The integer programs have been enhanced by the addition of cutting constraints derived for both the long-term customer order assignment and the short-term machine scheduling. Numerical examples modelled on a real-world make-to-order assembly system in the electronics industry are provided and computational results are reported.

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Optimal resource leveling of multi-resource projects

Cited by 47 publications

References 1 publication

Scheduling operations for the harvesting of renewable resources

Scheduling operations for the harvesting of renewable resources

Devising extended-duration schedules of enhanced resource leveling

Hierarchical approach to production scheduling in make-to-order assembly

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