Multiobjective optimization for manpower assignment in consulting engineering firms (original) (raw)
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The personnel assignment problem involva the assignment of people to tasks, projects, etc., in a manner that maximizes a measure of aggregate utility for the assignees subject to several assignment constraints. In addition to computationally efficient solution algorithms, the effective implementation of mathematical models to solve the personnel assignment problem must include data processing systems with comprehensive error checking/resolution procedures, end user reports, documentation, and training. This paper summarizes two successful computer-based systems for solving large-scale personnel assignment problems that frequently occur in university settings. SCAT, the Student-Company Assignment Technique, assigns students to job interview slots. SPAT, the Student-Project Assignment Technique, assigns students to project teams in an MBA field project course. The SCAT and SPAT approaches can be generalized to a wide variety of personnel assignment problems in university, industry, government, and military settings.
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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
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The research in this paper was done in CV. Rooesman, a small scale shoes and bag manufacturer company located in Yogyakarta Indonesia. Their strategy to response the demand is make-to-order. This company receives many purchase orders from several countries such as Japan, South Korea, and USA. Each purchase order is unique for example in term of design, complexity of the production process and raw material needed. Currently CV. Rooesman has difficulty to select which purchase order should be accepted. Ideally, all purchase orders from the customers have to be accepted. However due to the some factors such as limitation of resources that the company have i.e. human resources (number of workers and skill of workers needed) and other factor such as characteristic of the purchase order the company have to select which purchase order should be accepted. It happened in the past that the company accepted the purchase order without considering those factors then the order was not be able to ...
PARTICLE SWARM OPTIMIZATION APPLIED TO TASK ASSIGNMENT PROBLEM
– Particle Swarm Optimization (PSO) is a metaheuristic inspired on the emerging social behavior found in nature. PSO has shown good results in some recent works of discrete optimization, even though it was originally designed for continuous optimization problems. This paper models the problem of allocating a set of cabs to some customers as a combinatorial problem whose optimization goal is to minimize the distance traveled by the fleet. This problem can be categorized as a Task Assignment Problem, being optimized in this paper by two versions of the discrete PSO: the first approach that is based on a binary codifi-cation and the second one which uses permutations to encode the solution. The obtained results show that the second approach (permutation encoding) is superior than the first one (binary encoding) in terms of quality of the solutions and computational time, and it is capable of achieving the known optimal values whenever they are available.