A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty

Shadrokh, Shahram; Kianfar, Farhad

doi:10.1016/j.ejor.2006.03.056

Cited by 108 publications

(53 citation statements)

References 12 publications

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“…Задача ресурсных инвестиция были успешно решена в ряде работ [ [97], [98], [129], [130], [131]]. Стоит отметить работу [ [132]] в которой представлена расширенная задача ресурсных инвестиций: вместо крайнего срока завершения проекта (deadline) в ней используется директивный срок, что допускает появления задержки завершения всего проекта. Целевой функцией является минимизация общей суммы стоимостных затрат на поддержание ресурсов и на штраф за запаздывание.…”

Section: минимизация затрат на возобновляемые ресурсыunclassified

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2014

Proceedings of ISP RAS

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Section: минимизация затрат на возобновляемые ресурсыunclassified

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2014

Proceedings of ISP RAS

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“…Since RIP belongs to the family of scheduling problem it has proven to be NP-hard by Möhring (1984). Hence, heuristic algorithms are very commonly applied in solving such problems (Shadrokh & Kianfar, 2007).…”

Section: Deterministic Resource Investment Problemmentioning

confidence: 99%

“…( Shadrokh & Kianfar, 2007), developed a genetic algorithm (GA) to solve RIP. Their algorithm permitted the project delay subject to a predefined tardiness penalty.…”

Section: Deterministic Resource Investment Problemmentioning

confidence: 99%

Robust Resource Investment Problem with Time-Dependent Resource Cost and Tardiness Penalty

Hattab¹,

Haouari

2016

2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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The Resource Investment Problem (RIP) is a variant of the well-known Resource Constraint Project Scheduling Problem (RCPSP) that requires finding the optimal resource allocation, given a preset completion date, with the objective of minimizing the total cost.The practical relevance of RIP is very obvious; since the decision maker (the project manager for example) wants to know what resources are required to achieve the targeted project completion date. RIP helps to decide the amount of investment in resources that yield the optimal solution, in addition to the optimal tradeoff between completion time and resource investment.In practice, most of the projects are associated with due dates beyond which a tardiness penalty may be applied. To avoid the tardiness penalty, project managers sometimes decide to add more resources, thereby increasing resource investment cost, to the project to finish earlier. The experiments revealed the usefulness of the algorithm in finding relatively robust solution for small problem sizes.

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“…Shadrokh and Kianfar (2007) used the branch-and-bound procedure proposed by Demeulemeester and Herroelen (1992) to solve 690 problems. The proposed DE method was used on the single mode test cases in the PSPLIB library.…”

Section: Testing and Comparison Of De Algorithmmentioning

confidence: 99%

A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

Eshraghi¹

2016

10.5267/j.ijiec

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One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

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A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty

Cited by 108 publications

References 12 publications

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Robust Resource Investment Problem with Time-Dependent Resource Cost and Tardiness Penalty

A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

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