An Intelligent Model for Solving Manpower Scheduling Problems
- URL: http://arxiv.org/abs/2105.03540v1
- Date: Fri, 7 May 2021 23:51:12 GMT
- Title: An Intelligent Model for Solving Manpower Scheduling Problems
- Authors: Lingyu Zhang and Tianyu Liu and Yunhai Wang
- Abstract summary: This paper transforms the manpower scheduling problem into a combinational optimization problem under multi-constraint conditions.
It also uses logical paradigms to build a mathematical model for problem solution and an improved multi-dimensional evolution algorithm for solving the model.
- Score: 22.247926891283537
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The manpower scheduling problem is a critical research field in the resource
management area. Based on the existing studies on scheduling problem solutions,
this paper transforms the manpower scheduling problem into a combinational
optimization problem under multi-constraint conditions from a new perspective.
It also uses logical paradigms to build a mathematical model for problem
solution and an improved multi-dimensional evolution algorithm for solving the
model. Moreover, the constraints discussed in this paper basically cover all
the requirements of human resource coordination in modern society and are
supported by our experiment results. In the discussion part, we compare our
model with other heuristic algorithms or linear programming methods and prove
that the model proposed in this paper makes a 25.7% increase in efficiency and
a 17% increase in accuracy at most. In addition, to the numerical solution of
the manpower scheduling problem, this paper also studies the algorithm for
scheduling task list generation and the method of displaying scheduling
results. As a result, we not only provide various modifications for the basic
algorithm to solve different condition problems but also propose a new
algorithm that increases at least 28.91% in time efficiency by comparing with
different baseline models.
Related papers
- Doubly Stochastic Matrix Models for Estimation of Distribution
Algorithms [2.28438857884398]
We explore the use of Doubly Matrices (DSM) for matching and assignment nature permutation problems.
Specifically, we adopt the framework of estimation of distribution algorithms and compare DSMs to some existing proposals for permutation problems.
Preliminary experiments on instances of the quadratic assignment problem validate this line of research and show that DSMs may obtain very competitive results.
arXiv Detail & Related papers (2023-04-05T14:36:48Z) - Minimalistic Predictions to Schedule Jobs with Online Precedence
Constraints [117.8317521974783]
We consider non-clairvoyant scheduling with online precedence constraints.
An algorithm is oblivious to any job dependencies and learns about a job only if all of its predecessors have been completed.
arXiv Detail & Related papers (2023-01-30T13:17:15Z) - A Hybrid Evolutionary Approach to Solve University Course Allocation
Problem [0.0]
This paper discusses various types of constraints, difficulties and solutions to overcome the challenges regarding university course allocation problem.
A hybrid evolutionary algorithm has been defined combining Local Repair Algorithm and Modified Genetic Algorithm to generate the best course assignment.
arXiv Detail & Related papers (2022-11-15T09:43:02Z) - Learning to Optimize Permutation Flow Shop Scheduling via Graph-based
Imitation Learning [70.65666982566655]
Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems.
We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately.
Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
arXiv Detail & Related papers (2022-10-31T09:46:26Z) - Socio-cognitive Optimization of Time-delay Control Problems using
Evolutionary Metaheuristics [89.24951036534168]
Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches.
In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
arXiv Detail & Related papers (2022-10-23T22:21:10Z) - An Efficient Merge Search Matheuristic for Maximising the Net Present
Value of Project Schedules [5.10800491975164]
Resource constrained project scheduling is an important optimisation problem with many practical applications.
We propose a new math-heuristic algorithm based on Merge Search and parallel computing to solve the resource constrained project scheduling.
arXiv Detail & Related papers (2022-10-20T13:30:23Z) - An Overview and Experimental Study of Learning-based Optimization
Algorithms for Vehicle Routing Problem [49.04543375851723]
Vehicle routing problem (VRP) is a typical discrete optimization problem.
Many studies consider learning-based optimization algorithms to solve VRP.
This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.
arXiv Detail & Related papers (2021-07-15T02:13:03Z) - Apply Artificial Neural Network to Solving Manpower Scheduling Problem [15.848399017432262]
This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem.
We will use the neural network training method based on the time series to solve long-term and long-period scheduling tasks.
Our research shows that neural networks and deep learning strategies have the potential to solve similar problems effectively.
arXiv Detail & Related papers (2021-05-07T23:54:00Z) - A Two-stage Framework and Reinforcement Learning-based Optimization
Algorithms for Complex Scheduling Problems [54.61091936472494]
We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together.
The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively.
Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
arXiv Detail & Related papers (2021-03-10T03:16:12Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.