Technology-driven startups often experience fluctuations in business volume and their delivery peak exceeds the conventional functional department's human resource staffing. resource allocation is crucial for ensu...
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Technology-driven startups often experience fluctuations in business volume and their delivery peak exceeds the conventional functional department's human resource staffing. resource allocation is crucial for ensuring timely completion of project deliverables within resource constraints. This study tackles the scheduling challenge under the condition of composite skills, with the goal of efficiently allocating diverse human resources while adhering to constraints such as external labor coordination and skill compatibility. Initially, an integer programming model was formulated to minimize the overall project duration and equalize the workload among employees. Subsequently, an enhanced fast and elitist non-dominated sorting genetic algorithm (NSGA-II) was developed by integrating heuristic-based population initialization and adaptive genetic strategies. A case study was then developed based on the R program of technology-driven company S. The allocation model was solved using both the classic and improved versions of the algorithm. The outcomes indicate that the fitness of the final generation significantly exceeds that of the initial generation, and the refined algorithm outperforms the conventional one in reducing the project duration and enhancing balance, with the potential to reduce project duration by 9% and enhance the balance by 40%. Cluster analysis and statistical methods were applied to extract three pivotal traits of the optimal allocation scheme, providing a scientific reference and decision-making foundation for managerial resource-allocation strategies.
Many companies operating in an Engineering-to-Order environment are under constant pressure to manage the complexity of production planning. This paper deals with the case study of a company offering highly customized...
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Many companies operating in an Engineering-to-Order environment are under constant pressure to manage the complexity of production planning. This paper deals with the case study of a company offering highly customized products that are modular buildings. To support tactical production planning and capacity allocation, this paper formulates the production planning problem as a Preemptive resource constrained multi-project scheduling problem and proposes two mathematical formulations to solve it. The first presented formulation performs preemption using continuous activity splitting whilst the second permits discontinuous energy distribution. With the presented formulations we aimed to curtail resultant makespans and delays of all set projects. resource capacities are defined as accumulated weekly working hours of available and similar skills. We evaluated the performance of the proposed formulations, in terms of solving time and makespan, using three real-world datasets. Our findings indicate that the formulation using discontinuous energy allocation outperforms the formulation offering more control on the sub-activities.
In multi-project environment, multiple projects share and compete for the limited resources to achieve their own goals. Besides resource constraints, there exist precedence constraints among activities within each pro...
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ISBN:
(纸本)0791847403
In multi-project environment, multiple projects share and compete for the limited resources to achieve their own goals. Besides resource constraints, there exist precedence constraints among activities within each project. This paper presents a hybrid genetic algorithm to solve the resource-constrainedmulti-projectschedulingproblem (RCMPSP), which is well known NP-hard problem. Objectives described in this paper are to minimize total project time of multiple projects. The chromosome representation of the problem is based on activity lists. The proposed algorithm was operated in two phases. In the first phase, the feasible schedules are constructed as the initialization of the algorithm by permutation based simulation and priority rules. In the second phase, this feasible schedule was optimized by genetic algorithm, thus a better approximate solution was obtained. Finally, after comparing several different algorithms, the validity of proposed algorithm is shown by a practical example.
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