A new approach for project scheduling with a limited resource

Khattab, M.; Choobineh, F.

doi:10.1080/00207549108930056

Cited by 25 publications

(10 citation statements)

References 8 publications

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“…Different computing models for the priorities by taking into account these factors will characterize the corresponding priority rules and may lead to different performances. Because there is no systematic measure to select a heuristic rule or to decide which is better than others [9], a multiple-pass heuristic method that considers different priority rules for each pass is proposed so as to select the best one among multiple solutions [7,18,19]. Moreover, a local search way for an improved solution by adjusting start times of some activities based on the current multiple-solutions from the multiple-pass heuristic is recommended [20].…”

Section: Particle-represented Priorities Of Activitiesmentioning

confidence: 98%

Particle swarm optimization for resource-constrained project scheduling

Zhang

Tam

2006

International Journal of Project Management

139

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Section: Particle-represented Priorities Of Activitiesmentioning

confidence: 98%

Particle swarm optimization for resource-constrained project scheduling

Zhang

Tam

2006

International Journal of Project Management

139

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“…Although many individual priority rules have been devised, it is reported that there is no priority rule that dominates others or performs consistently better than other rules. 5,6 To cope with such weakness of individual rules, Boctor 7 and Khattab and Choobineh 6 suggest composite heuristics that include several priority rules and select the best solution among those from the individual rules as the ®nal solution.…”

Section: Introductionmentioning

confidence: 99%

A simulated annealing algorithm for resource constrained project scheduling problems

Cho

Kim

1997

Journal of the Operational Research Society

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This paper presents a simulated annealing algorithm for resource constrained project scheduling problems with the objective of minimising makespan. In the search algorithm, a solution is represented with a priority list, a vector of numbers each of which denotes the priority of each activity. In the algorithm, a priority scheduling method is used for making a complete schedule from a given priority list (and hence a project schedule is de®ned by a priority list). The search algorithm is applied to ®nd a priority list which corresponds to a good project schedule. Unlike most of priority scheduling methods, in the suggested algorithm some activities are delayed on purpose so as to extend search space. Solutions can be further improved by delaying certain activities, since non-delay schedules are not dominant in the problem (the set of non-delay schedules does not always include an optimal solution). The suggested algorithm is exible in that it can be easily applied to problems with an objective function of a general form and/or complex constraints. The performance of the simulated annealing algorithm is compared with existing heuristics on problems prepared by Patterson and randomly generated test problems. Computational results showed that the suggested algorithm outperformed existing ones.

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“…There are some recent studies [1,13] that are wmrthy to mention here because of their different approaches. Bell et.al.…”

Section: 3 1 Heui'istic Approachesmentioning

confidence: 99%

“…They were given by Wiest [29] and Fendly [10]. There are several attempts including comparative studies in between the e.xisting heuristics and between the heuristics and the optimal solution procedures [1,2,4,7,9,10,12,13,15,18,19,28,29].…”

Section: 3 1 Heui'istic Approachesmentioning

confidence: 99%