Preemptive multi-skilled resource investment project scheduling problem: Mathematical modelling and solution approaches

Javanmard, Shima; Afshar-Nadjafi, Behrouz; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.compchemeng.2016.11.001

Cited by 34 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Overcoverage and undercoverage would lead to different processing time for each activity. Equations (21) and (22) are auxiliary constraints that are used to satisfy Equation (20). Equations (23) and (24) limit maximum possible undercoverage and overcoverage, respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Hematian

Esfahani

Mahdavi

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.

show abstract

Section: Mathematical Modelmentioning

confidence: 99%

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Hematian

Esfahani

Mahdavi

et al. 2019

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…RIP performs project scheduling under the premise of meeting deadline to achieve the minimum total resource cost [9,10], recently, towards multiskilled workers who have different skills at different speeds and costs. Javanmard et al [11] then combined the multiskilled project scheduling problem with the RIP, which aims to obtain a parallel optimization strategy of project scheduling and skill's recruitment. Shahsavar et al [12] combined RIP with the quantity discount problem to reduce the resource costs.…”

Section: Construction Scheduling and Resource Allocationmentioning

confidence: 99%

“…Constraint (10) guarantees that the process of the project should be within the whole planning horizon. Constraints (11) and (12) specify the feasible ranges for the decision variables.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…In fact, [M2] consists of two nested submodels, the first model is to make decision on project scheduling and resource allocation, that is, determine decision variable y, z , and the second one is to decide the worst-case distribution p rs . Suppose that a feasible solution y, z , satisfying constraints (6), (7), (8), (9), (10), (11), and (12), is given. The value of time objective f T can be calculated easily.…”

Section: M2 Minmentioning

confidence: 99%

See 1 more Smart Citation

Robust Optimization for Integrated Construction Scheduling and Multiscale Resource Allocation

Tao

Chong

et al. 2018

Complexity

View full text Add to dashboard Cite

This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi-time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data-Driven Construction Scheduling and Multiscale Resource Allocation Problem (DD-CS&MRAP). Based on likelihood robust optimization, a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double-layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization (MOPSO) and interior point method named MOPSO-interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO-interior point algorithm can reduce resource cost and improve the efficiency of resource utilization.

show abstract

“…It is possible to use manpower with different skills in the implementation of projects that can be considered for the RCPSP as another generalization, namely MSRCPSP [35][36][37][38]. Javanmard et al [39] integrated MSRCPSP with the resource investment problem to obtain concurrent optimal policy of project scheduling and skills' recruitment. Wang and Zheng [40] proposed a multi-objective fruit fly optimization for the MSRCPSP with the criteria of minimizing the makespan and total cost simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

2019

View full text Add to dashboard Cite

Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved. Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion. Keywords: Resource constrained project scheduling problem (RCPSP), flexible activities, multiple routes, particle swarm optimization (PSO), genetic algorithm (GA) 0,1, 2,..., 1 JN  1 J Set of total project fixed activities, 1 JJ  2 J Set of total flexible project activities, 21 J J J    Set of routes of each flexible activity 2 J   h  Set of total sub network for flexible activity

show abstract

Preemptive multi-skilled resource investment project scheduling problem: Mathematical modelling and solution approaches

Cited by 34 publications

References 38 publications

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Robust Optimization for Integrated Construction Scheduling and Multiscale Resource Allocation

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

Contact Info

Product

Resources

About