Solving the bi-objective personnel assignment problem using particle swarm optimization

Lin, Shih-Ying; Horng, Shi–Jinn; Kao, Tzong-Wann; Fahn, Chin-Shyurng; Huang, Deng-Kui; Run, Ray-Shine; Wang, Yuh-Rau; Kuo, I-Hong

doi:10.1016/j.asoc.2012.03.031

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…The concept of bilateral matching originated in the 1960s during studies on the coexistence of stable marriages and university admissions (Gale and Shapley, 1962). The concept was later applied to other two-dimensional constructs, including supply and demand (Wu et al, 2020), personnel allocations (Lin et al, 2012), and technical knowledge applications to tasks (Han et al, 2018). Notably, it is of great theoretical and practical significance to conduct in-depth research on the bilateral matching problem (Morizumi et al, 2011;Fan and Yue, 2014;Yue and Fan, 2013).…”

Section: Two-sided Matching Decision Modelmentioning

confidence: 99%

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Extended belief rule-based method for the assessment of matching between rescue tasks and volunteers

Chen

Chen²,

Yang³

2022

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PurposeThis paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.Design/methodology/approachAn extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.FindingsDisasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.Originality/valueBased on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

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Section: Two-sided Matching Decision Modelmentioning

confidence: 99%

“…The concept was later applied to other two-dimensional constructs, including supply and demand (Wu et al. , 2020), personnel allocations (Lin et al. , 2012), and technical knowledge applications to tasks (Han et al., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Extended belief rule-based method for the assessment of matching between rescue tasks and volunteers

Chen

Chen²,

Yang³

2022

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“…In order to ful ll the assignment problem we use the result of above model to nd an e cient assignment based on observed data. \begin{gathered} Min\,\,\,\sum\limits_{{i=1}}^{n} {\sum\limits_{{j=1}}^{n} { -{\theta _{ij}}{x_{ij}}} } \h ll \\ s.t., \h ll \\ \sum\limits_{{j=1}}^{n} {{x_{ij}}=1\,\,\,\,\,\,\,\,\,\,\,\,\,1 \leqslant i \leqslant n} \,\,\,\,\,\,\,\,\,\,\, (7) \h ll \\ \sum\limits_{{i=1}}^{n} {{x_{ij}}=1\,\,\,\,\,\,\,\,\,\,\,\,\,1 \leqslant j \leqslant n} \,\,\,\,\,\,\, \h ll \\ {x_{ij}} \in \{ 0,1\} \,\,\,\,\,\,\,\,\,\,\,1 \leqslant i,j \leqslant n \h ll \\ \end{gathered} Note that we use the e ciency of each assignment for the coe cient of objective function, that is, higher is better. That is why a minus is multiplied to the coe cient in contrast with cost coe cient of generic assignment problem of (4).…”

Section: Extended Assignment Problemmentioning

confidence: 99%

Input and output consideration in the assignment and transportation problems

Ghiyasi

rouyendegh

Taspinar

et al. 2022

Preprint

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Transportation and assignment problems are subject of extensive research focusing on the optimization of shipping routes and utilization of resources to decrease costs or increase profits. In this work, these approaches are generalized to cases with multiple inputs/outputs, while improving the discrimination power and reducing the computational complexity compared to the existing models. The applicability and strength of our model is also demonstrated by applying it to practical scenarios alongside these conventional approaches.

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“…The motivation of using HPSO in this work is threefold. First, the PSO, a swarm-based meta-heuristic algorithm, has provided good solutions for many combinatorial optimization problems (e.g., multidimensional knapsack problem [29], data allocation problem [30] and personnel assignment problem [31]) within a reasonable amount of computing time, especially many different variants of the FLP, LRP and VRP have been solved using PSO-based approaches in the literature (e.g. VRP with stochastic demands [32], VRP with dynamic requests [2], multi-workshop facility layout problem [33] and LRP with stochastic demands [19]).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Heuristic Based on a Particle Swarm Algorithm to Solve the Capacitated Location-Routing Problem With Fuzzy Demands

et al. 2020

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In this paper, the capacitated location-routing problem with fuzzy demands (CLRP-FD) is considered, which simultaneously solves two problems: locating the facilities and designing the vehicle routes among the established facilities and customers. In the CLRP-FD, the capacities of the employed vehicles and established facilities cannot exceed, and the demands of the customers are assumed to be triangular fuzzy variables. To model the CLRP-FD, a fuzzy chance constrained programming approach is designed using fuzzy credibility theory. To solve this problem, a hybrid particle swarm optimization (HPSO) algorithm, which includes a stochastic simulation and a local search strategy based on the variable neighborhood search algorithm, is proposed. Finally, the influence of the value of the dispatcher preference index (DPI) on the total distribution cost is analyzed by conducting numerical experiments. To evaluate the efficiency of the proposed HPSO and the performance of the CLRP-FD model, the results obtained using the HPSO were compared with their corresponding lower bound provided using the CPLEX solver. Moreover, we also evaluated the performance of HPSO through computational experiments on some well-known benchmark CLRP instances. The numerical results show that the proposed HPSO is competitive, and can give a satisfactory solution in a reasonable amount of time.

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Solving the bi-objective personnel assignment problem using particle swarm optimization

Cited by 15 publications

References 15 publications

Extended belief rule-based method for the assessment of matching between rescue tasks and volunteers

Extended belief rule-based method for the assessment of matching between rescue tasks and volunteers

Input and output consideration in the assignment and transportation problems

A Hybrid Heuristic Based on a Particle Swarm Algorithm to Solve the Capacitated Location-Routing Problem With Fuzzy Demands

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