scholarly journals Personnel Scheduling Problem under Hierarchical Management Based on Intelligent Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Li Huang ◽  
Chunming Ye ◽  
Jie Gao ◽  
Po-Chou Shih ◽  
Franley Mngumi ◽  
...  
Keyword(s):  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Grey Wolf Optimizer ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Intelligent Algorithm ◽  
Charge Nurses ◽  
Hierarchical Management ◽  
Two Hybrid ◽  
Better Than

This paper studies a special scheduling problem under hierarchical management in nurse staff. This is a more complex rostering problem than traditional nurse scheduling. The first is that the rostering requirements of charge nurses and general nurses are different under hierarchical management. The second is that nurses are preferable for relative fair rather than absolute fair under hierarchical management. The model aims at allocating the required workload to meet the operational requirements, weekend rostering preferences, and relative fairness preferences. Two hybrid heuristic algorithms based on multiobjective grey wolf optimizer (MOGWO) and three corresponding single heuristic algorithms are employed to solve this problem. The experimental results based on real cases from the Third People’s Hospital, Panzhihua, China, show that MOGWO does not as good as it does on other engineering optimization. However, the hybrid algorithms based on MOGWO are better than corresponding single algorithms on generational distance (GD) and spacing (SP) of Pareto solutions. Furthermore, for relative fair rostering objective, NSGAII-MOGWO has more power to find the optimal solution in the dimension of relative fairness.

scholarly journals Nonpreemptive Goal Programing Method in Optimization Nurse Scheduling by Considering Education Level

2021 ◽  
Vol 22 (2) ◽  
pp. 85
Author(s):  
Fitriani Utina ◽  
Lailany Yahya ◽  
Nurwan Nurwan
Keyword(s):  
Goal Programming ◽  
Undergraduate Education ◽  
Optimal Solution ◽  
Education Level ◽  
Programming Method ◽  
Nurse Scheduling ◽  
Optimal Service ◽  
Schedule Design ◽  
Good Nurse ◽  
Better Than

Nurse scheduling is one of the problems that often arise in hospital management systems. Head of ICU room and nurse to cooperate in making good nurse scheduling for the creation of optimal service. In this paper, we study a hospital nurse schedule design by considering the level of nurse education and the provision of holidays. Nurses with undergraduate education (S1) Nurses become leaders on every shift and are accompanied by nurses with diploma education (D3). The scheduling model in this study using the nonpreemptive goal programming method and LINGO 11.0 software. The preparation of the schedule of nurses assigned to this method can optimize the need for efficient nurses per shift based on education level. The data in the research was obtained by collecting administrative data at Aloei Saboe Gorontalo hospital. The data used are the published schedule by the head of the ICU room. In making a nurse schedule, there are limitations to consider such ashospital regulation. The results of the study obtained an optimal solution in the form of meeting all the desired obstacles. Computational results shows that nurse scheduling using the nonpreemptive goal programming method and LINGO 11.0 software better than the schedule created manually. Every shift is a maximum of one leader with an undergraduate education (S1) background and accompanied by a nurse with a diploma education (D3) background. Keywords: scheduling, goal programming, nonpreemptive goal programming.

Optimal design of aerospace structures using recent meta-heuristic algorithms

2021 ◽  
Vol 63 (11) ◽  
pp. 1025-1031
Author(s):  
Faik Fatih Korkmaz ◽  
Mert Subran ◽  
Ali Rıza Yıldız
Keyword(s):  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Von Mises Stress ◽  
Grey Wolf Optimizer ◽  
Local Optima ◽  
Design And Optimization ◽  
Shape Perturbation ◽  
Novel Algorithms ◽  
Von Mises ◽  
Conventional Optimization

Abstract Most conventional optimization approaches are deterministic and based on the derivative information of a problem’s function. On the other hand, nature-inspired and evolution-based algorithms have a stochastic method for finding the optimal solution. They have become a more popular design and optimization tool, with a continually growing development of novel algorithms and new applications. Flexibility, easy implementation, and the capability to avoid local optima are significant advantages of these algorithms. In this study, shapes, and shape perturbation limits of a bracket part, which is used in aviation, have been set using the hypermorph tool. The objective function of the optimization problem is minimizing the volume, and the constraint is maximum von Mises stress on the structure. The grey wolf optimizer (GWO) and the moth-flame Optimizer (MFO) have been selected as nature-inspired evolution-based optimizers.

scholarly journals A hybrid case-based reasoning approach to detecting the optimal solution in nurse scheduling problem

2018 ◽  
Author(s):  
Svetlana Simić ◽  
Dragana Milutinović ◽  
Slobodan Sekulić ◽  
Dragan Simić ◽  
Svetislav D Simić ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Case Based Reasoning ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Case Based

Orthogonal Optimization Algorithm of Electronic Circuit Parameters

2013 ◽  
Vol 427-429 ◽  
pp. 671-674
Author(s):  
Yong Xian Li ◽  
Yu Zi Lin ◽  
Jia Zhong Li
Keyword(s):  
Optimization Design ◽  
Electronic Circuit ◽  
Orthogonal Design ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Parameter Design ◽  
Variance Ratio ◽  
Intelligent Algorithm ◽  
Orthogonal Optimization ◽  
Better Than

A novel intelligent algorithm of orthogonal optimization is introduced for electronic circuit parameters. The orthogonal optimization design develops from conventional orthogonal design. According to the results of variance and variance ratio analysis in the orthogonal design, the next searching direction and range of each variable are determined, which is able to be circulating in the optimization of searching. The orthogonal optimization solution is performed intelligently until error value of the variance ratio for each variable is approximately equal. Since the tolerance of an optimal solution is obtained when the parameter design is completed, this method does not need special tolerance design. The authors take a stabilized power supply circuit as an example to optimize the circuit parameters. This method has less calculation amount, shorter searching time, more rapid speed and higher accuracy of optimization searching. Optimization results show that this algorithm is much better than other current algorithms of intelligent optimization methods.

scholarly journals Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammed Al-Salem ◽  
Leonardo Bedoya-Valencia ◽  
Ghaith Rabadi
Keyword(s):  
Job Shop ◽  
Heuristic Algorithms ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Exact Algorithms ◽  
Just In Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD) for a set of jobs when their weights equal 1 (unweighted problem). This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithm is proposed to find approximate solutions. Through computational experiments, the heuristic algorithms’ performance is evaluated with problems up to 500 jobs.

scholarly journals Fairness Time-Slot Allocation and Scheduling with QoS Guarantees in Multihop WiMAX Mesh Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chien-Yu Wu ◽  
Hann-Jang Ho ◽  
Sing-Ling Lee ◽  
Liang Lung Chen
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Mesh Networks ◽  
Time Slot ◽  
Optimal Solution ◽  
Small Scale ◽  
Scheduling Problem ◽  
Slot Allocation ◽  
Long Distance ◽  
Time Slot Allocation

The WiMAX technology has been defined to provide high throughput over long distance communications and support the quality of service (QoS) control applied on different applications. This paper studies the fairness time-slot allocation and scheduling problem for enhancing throughput and guaranteeing QoS in multihop WiMAX mesh networks. For allocating time slots to multiple subscribe stations (SSs), fairness is a key concern. The notion of max-min fairness is applied as our metric to define the QoS-based max-min fair scheduling problem for maximizing the minimum satisfaction ratio of each SS. We formulate an integer linear programming (ILP) model to provide an optimal solution on small-scale networks. For large-scale networks, several heuristic algorithms are proposed for better running time and scalability. The performance of heuristic algorithms is compared with previous methods in the literatures. Experimental results show that the proposed algorithms are better in terms of QoS satisfaction ratio and throughput.

scholarly journals Optimal solving of the pump scheduling problem by using a Harmony Search optimization algorithm

2017 ◽  
Vol 19 (6) ◽  
pp. 879-889 ◽  
Author(s):  
F. De Paola ◽  
N. Fontana ◽  
M. Giugni ◽  
G. Marini ◽  
F. Pugliese
Keyword(s):  
Optimization Algorithm ◽  
Water Distribution ◽  
Heuristic Algorithms ◽  
Water Pressure ◽  
Harmony Search ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Scheduling Problem ◽  
Pump Operation ◽  
Pump Scheduling

Abstract Pumps are installed in water distribution networks (WDNs) to ensure adequate service levels in the case of poor water pressure (e.g. because of low elevation of reservoirs or high head losses within the WDN). In such cases optimal pump scheduling is often required for the opportunity of significant energy saving. Optimizing the pump operation also allows a reduction in damage and maintenance times. Among the approaches available in the literature to solve the problem, meta-heuristic algorithms ensure reduced computational times, although they are not able to guarantee the optimal solution can be found. In this paper, a modified Harmony Search Multi-Objective optimization algorithm is developed to solve the pump scheduling problem in WDNs. The hydraulic solver EPANET 2.0 is coupled with the algorithm to assess the feasibility of the achieved solutions. Hydraulic constraints are introduced and penalties are set in case of violation of the set constraints to reduce the space of feasible solutions. Results show the high performances of the proposed approach for pumping optimization, guaranteeing optimal (or near optimal) solutions with short computational times.

scholarly journals A mathematical model for teamwork scheduling problem in available time-windows

2020 ◽  
Vol 3 (SI1) ◽  
pp. First
Author(s):  
Sơn Hồng Trang ◽  
Lăng Văn Trần ◽  
Nguyên Tường Huỳnh
Keyword(s):  
Mathematical Model ◽  
Heuristic Algorithms ◽  
Time Windows ◽  
Computing Time ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Feasible Schedule ◽  
Maximum Completion Time ◽  
Simple Heuristics ◽  
Assignment Approach

This paper deals with teamwork scheduling problem in available time windows. This problem has been posed by combining the three constraints are the jobs can split into some sub-jobs which should not be less than a threshold called splitmin, the jobs are only assigned into available time windows and the jobs can be assigned into many people in the organization. Since then the four properties of this problem considered are everyone handles any jobs; a job can be handled by some person at the same time; jobs can be broken down into some sub-jobs; the size of the job/sub-job should not be less than splitmin. The goal aims to determine a feasible schedule that minimizes makespan. And a numerical example is presented to demonstrate the essential constraint with given input data to well define this scheduling problem. Besides the authors proposed a mathematical model to determine the optimal solution by using solvers to solve it and some simple heuristics with computing time less than one second to find the good solutions such as Assignment approach, SPT/LPT rules. All experiments were evaluated on two criteria are the maximum completion time for all jobs and runtime in seconds to determine the solution. These experiments were conducted by the comparison of the lower bound, the exact method based on using CPLEX solver to solve the MILP model, and proposed heuristics. The experimental results show it is very time consuming to determine the optimal solution by CPLEX solver, while the solution found by heuristic algorithms is only good enough.

Scheduling Trucks in Multi-Door Cross Docking Systems: An Adaptive Genetic Algorithm with a Dispatching Rule

2015 ◽  
Vol 32 (03) ◽  
pp. 1550016 ◽  
Author(s):  
Byung Soo Kim ◽  
Cheol Min Joo
Keyword(s):  
Genetic Algorithms ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Operation Time ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problem ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Total Operation Time ◽  
Management Concept

One of the most important operational management problems of a cross docking system is the truck scheduling problem. Cross docking is a logistics management concept in which products delivered to a distribution center by inbound trucks are immediately sorted out, routed and loaded into outbound trucks for delivery to customers. The truck scheduling problem in a multi-door cross docking system considered in this paper comprises the assignment of trucks to dock doors and the determination of docking sequences for all inbound and outbound trucks in order to minimize the total operation time. A mathematical model for optimal solution is derived, and the genetic algorithms (GAs) and the adaptive genetic algorithms (AGAs) as solution approaches with different types of chromosomes are proposed. The performance of the meta-heuristic algorithms are evaluated using randomly generated several examples.

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