Fuzzy Scheduling Model Research Articles

Economic benefits of flexible production in green manufacturing enterprises based on fuzzy scheduling model algorithm

Economic benefits of flexible production in green manufacturing enterprises based on fuzzy scheduling model algorithm

Adopting a hierarchical diagnosis and treatment system to optimize elective surgery scheduling

The COVID-19 outbreak has changed the hospital demand dynamics by increasing the ICU/ward demand and uncertainty. While traditional standalone hospitals have struggled to manage their elective surgery scheduling during the pandemic, hierarchical diagnosis and treatment systems (HDTS) with the high- and low-level hospitals have had some advantages as the high-level hospitals can transfer postoperative recovery patients to their low-level hospital in the network to mitigate the resource shortages. However, in practice, this task is challenging as patient transfers could be costly depending on the patient’s condition, transfer time, and transfer hospital. Thus, there exists an interesting tradeoff between the costs and benefits associated with the patient transfer process within an HDTS. Since data is usually not available in this context, we develop a fuzzy (based on executive opinion) scheduling model for elective surgeries considering the tradeoffs in the patient transfer process. An efficient hybrid algorithm based on the genetic algorithm, variable neighborhood search, and heuristic rules is proposed to cope with the computational complexity of the problem, and the adaptability of the fuzzy model in an uncertain environment is validated. Our paper highlights how hospitals can maximize their profits by transferring patients in an HDTS when the demand for ICUs/wards is uncertain, and thus, this framework also applies to elective patient scheduling during epidemic outbreaks such as COVID-19.

Elective surgery scheduling considering transfer risk in hierarchical diagnosis and treatment system

With the aggravation of the shortage of staffing hospital beds, elective surgeries have to be postponed or even cancelled, which directly affects hospital income and patient health. Therefore, we propose a fuzzy scheduling model based on a patient transfer strategy in the hierarchical diagnosis and treatment system to ensure timely surgery. We propose a risk estimation method based on health failure mode and effect analysis to reduce the transfer risk and represent the surgery duration and the length of stay as fuzzy numbers to deal with the uncertainty. Given that solving the fuzzy model is challenging, we propose an equivalent formulation to transform the fuzzy model into a two-stage mixed integer linear programming (TMIP) model, which can reduce the loss of decision-making information. Finally, the column and constraint generation algorithm is used to solve the TMIP model that is suited for the structure of the main problem and subproblem. The experiment shows that the patient transfer strategy can effectively relieve the hospital bed shortage, and its potential can be further released if the transfer risk can be properly assessed. The proposed method for solving the fuzzy model can reduce the information loss comparing traditional methods.

Real-Time Data Scheduling of Flexible Job in Papermaking Workshop Based on Deep Learning and Improved Fuzzy Algorithm

The traditional real-time data scheduling method ignores the optimization process of job data that leads to delayed delivery, high inventory cost, and low utilization rate of equipment. This paper proposes a novel real-time data scheduling method based on deep learning and an improved fuzzy algorithm for flexible operations in the papermaking workshop. The algorithm is divided into three parts: the first part describes the flexible job shop scheduling problem; the second part constructs the fuzzy scheduling model of flexible job data in papermaking workshop; and finally the third part uses a genetic algorithm to obtain the optimal solution of fuzzy scheduling of flexible job data in papermaking workshop. The results show that the optimal solution is obtained in 48 seconds at the 23rd attempt (iteration) under the application of the proposed method. This result is much better than the three traditional scheduling methods with which we compared our results. Hence, this paper improves the work efficiency and quality of papermaking workshop and reduces the operating cost of the papermaking enterprise.

Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set

Resource allocation via project scheduling plays an important role to balance project time and cost. In this paper, we are designing a framework to handle scheduling problems using neutrosophic activity duration times. We consider important aspects of time-cost tradeoffs while taking into account the real and uncertain situation surrounded by the projects. In real situations, there are many considerable aspects which should be considered by managers, for instance, the tradeoffs between the project completion time and cost and the uncertain conditions of the environment. Since the concept of the deterministic project scheduling and time-cost tradeoffs conflict with the real situation, where in many cases, some data on the activities durations of the project changes during the implementation process. Fuzzy scheduling and time-cost tradeoffs models assume only truth-membership functions dealing with uncertainties surrounded by the projects and their activities duration which are unable to handle indeterminacy and inconsistency. Therefore, neutrosophic theory is a better framework that takes into account the dynamic features of all parameters. Trapezoidal neutrosophic numbers are used to estimate the activities durations in this paper. The crisp model for activities time obtained by applying score and accuracy functions. The goal here is to minimize the cost of projects under uncertain environmental conditions.

Modeling and solving multi-skilled resource-constrained project scheduling problem with calendars in fuzzy condition

In this study, we aim to present a new model for the resource-constrained project scheduling problem (RCPSP) considering a working calendar for project members and determined the skill factor of any member using the efficiency concept. For this purpose, the recyclable resources are staff resources where any person with multiple skills can meet the required skills of activities in a given time. Then, considering uncertainty condition for parameters, it provided a fuzzy scheduling model and validated models by solving different examples. The proposed mathematical programming model optimizes the allocation of limited resources to project activities for scheduling purposes in an essential activity in the real condition of scheduling problems. Moreover, the proposed model can decrease the risk of deviation from scheduling by allocating members with higher skill factors to critical activities. Then, considering uncertainty condition for parameters, it provided a fuzzy scheduling model and validated models by solving different examples. Considering fuzzy conditions for the calendar of the project and multi-skill operators are firstly considered in this paper. Also, the recyclable resources are staff resources which are being considered for the model concurrently in response to the risks of availability to resources and delay in completing the project under uncertainty. The results derived from the model solved by CPLEX indicated a decreased need for employment and shortened project completion duration. Assuming the uncertainty of available resource capacity at any time, the results obtained from the fuzzy model for the value of objective function were evaluated under the influence of the resource calendar and showed the benefits. Effect of the multi-skill members with calendar constraints on the model is tested, and the advantages are determined.

Fuzzy Byproduct Gas Scheduling in the Steel Plant Considering Uncertainty and Risk Analysis

In the iron and steel enterprises, efficient utilization of byproduct gas is of great significance for energy conservation and emission reduction. This work presents a fuzzy optimal scheduling model for byproduct gas system. Compared with previous work, uncertainties in byproduct gas systems are taken into consideration. In our model, uncertain factors in byproduct systems are described by fuzzy variables and gasholder level constraints are formulated as fuzzy chance constraints. The economy and reliability of byproduct gas system scheduling are sensitive to different confidence levels. To provide a reference for operators to determine a proper confidence level, the risk cost is defined to quantify the risk of byproduct gas shortage and emission during the scheduling process. The best confidence level is determined through the trade-off between operation cost and risk cost. The experiment results demonstrated that the proposed method can reduce the risk and give a more reasonable optimal scheduling scheme compared with deterministic optimal scheduling.

Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate

Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for batch process is formulated based on a unit-specific event-based continuous-time modeling method. Utility uncertainty and uncertain relationship between production rate and utility supply are described by fuzzy theory. The uncertain scheduling model is converted into deterministic model by mathematical method. Through one numerical example, the accuracy and practicability of the proposed model is proved. Fuzzy scheduling model can supply valuable decision options for enterprise managers to make decision more accurate and practical. The impact and selection of some key parameters of fuzzy scheduling model are elaborated.

Scatter search based particle swarm optimization algorithm for earliness/tardiness flowshop scheduling with uncertainty

Considering the imprecise nature of the data in real-world problems, the earliness/tardiness (E/T) flowshop scheduling problem with uncertain processing time and distinct due windows is concerned in this paper. A fuzzy scheduling model is established and then transformed into a deterministic one by employing the method of maximizing the membership function of middle value. Moreover, an effective scatter search based particle swarm optimization (SSPSO) algorithm is proposed to minimize the sum of total earliness and tardiness penalties. The proposed SSPSO algorithm incorporates the scatter search (SS) algorithm into the frame of particle swarm optimization (PSO) algorithm and gives full play to their characteristics of fast convergence and high diversity. Besides, a differential evolution (DE) scheme is used to generate solutions in the SS. In addition, the dynamic update strategy and critical conditions are adopted to improve the performance of SSPSO. The simulation results indicate the superiority of SSPSO in terms of effectiveness and efficiency.

Research on Jamming Resource Scheduling Based on Multi-Target and Fuzzy Multi-Stage

For radar and electronic warfare integrated system, resource scheduling play a big role in operational effectiveness. At first, introduced the composition and technical performance advantage of radar and electronic warfare integrated system. According to radar jamming equation, the characteristics of array, combined with jamming time-window, jamming efficiency ratio, jamming satisfaction, established multi-target fuzzy multi-stage jamming resource scheduling model with time-window, through the simulation, proved the scientificity and rationality of the method.

Single machine common flow allowance scheduling with a fuzzy rate-modifying activity

The fuzzy scheduling is a new approach presented in this paper. In classic scheduling, it was assumed that the machines are always available. So, no maintenance time was considered for calculations. Also, assumed that all given times are exact and there is no uncertainty, however in the real word accurate calculation is impossible and always faced with uncertainty. Therefore, scheduling calculations would not be accurate and an optimal solution would not be reached. In this study, rate-modifying activity time considered as the fuzzy number and the optimal solution was calculated in uncertainty condition. A novel type of fuzzy scheduling model is presented. A ROG algorithm for ranking fuzzy numbers was introduced and example was solved to show the effectiveness of the proposed approach. Keywords: Fuzzy algorithm; machine scheduling; job scheduling rate-modifying activity; single machine..

Flow Shop Production Scheduling under Uncertainty within Infinite Intermediate Storage

As to current situation of research on production scheduling under uncertainties in enterprises, Flow Shop production scheduling model is established based on the theory of fuzzy programming, in which fuzzy processing time is considered and the duration time of intermediate is unlimited. The maximum membership function of mean value has been applied to solve the non-linear fuzzy scheduling model in order to convert the fuzzy optimization problem to the general optimization problem. Finally, a cooperative co-evolutionary particle swarm optimization algorithm based on catastrophe added to improve the diversity of the swarm (CCPSO) is adopted to solve Flow Shop Production Scheduling Under Uncertainty within Infinite Intermediate Storage and results are obtained effectively.

A Cooperative Co-evolution PSO for Flow Shop Scheduling Problem with Uncertainty

Considering current situation of production scheduling with uncertainties in modern manufacturing enviroments, flow shop production scheduling model is established based on the theory of fuzzy programming, in which fuzzy processing time is considered and the duration time of intermediate is unlimited. The maximum membership function of mean value has been applied to solve the non-linear fuzzy scheduling model in order to convert the fuzzy optimization problem to the general optimization problem. Finally, a cooperative co-evolutionary particle swarm optimization algorithm based on catastrophe added to improve the diversity of the swarm (CCPSO) is adopted to solve flow shop production scheduling with uncertainty within infinite intermediate storage and the simulation results obtained are effective and satisfactory.

Fuzzy resource-constrained project scheduling problem for software development

This paper presents a new method to solve the resource- constrained project scheduling problem for software development. In this method, activity duration times are described as fuzzy variables and resource-constrained software project scheduling problems are described as fuzzy programming models. First, how to model the software project scheduling problem under the fuzzy environment conditions is proposed. Second, in order to satisfy the different requirements of decision-making, two novel fuzzy project scheduling models, expected cost model and credibility maximization model, are suggested. Third, a hybrid intelligent algorithm integrated by genetic algorithm and fuzzy simulation is designed to solve the above two fuzzy programming models. Numerical experiments illustrate the effectiveness of the hybrid intelligent algorithm.

A Synthetic Evaluation and Fuzzy Scheduling Model for Autonomous Test Priority

Abstract An autonomous test is one of the key technologies in an autonomous system. Based on the multitask mechanism of an embedded real-time operation system, the software environment for an autonomous test is proposed. The influence factors of task importance are also considered in order to form a comprehensive criterion. A synthetic evaluation and fuzzy scheduling model is established, and the evaluating method for clustering effect is provided afterwards. The experiment shows the validity of the model.

Research on Fuzzy Scheduling Problem of Single-Stage Multi-Parallel Machines

This paper considers single-stage multi-parallel machines scheduling problems. A fuzzy scheduling model is developed with objective to minimize the makespan. In the scheduling model, the uncertainty of processing time is represented by triangular fuzzy number, and the credibility theory is used to assess fuzzy number. In the optimal scheduling searching process, we introduce matching theory to model the scheduling problem consider in this paper, and two heuristic rules are used to improve searching efficiency. At last, genetic algorithm is developed for solving the scheduling problem, and numerical example shows good result.

Scheduling of Uncertain Multi-product Batch Processes Under Finite Intermediate Storage Policy

There are various uncertainties in the production scheduling of the real-world applications. And in multi-product batch processes, the intermediate storage often can be used to increase plant productivity and operational efficiency. In this paper, the scheduling mathematical model for multi-product batch processes under Finite Intermediate Storage (FIS) policy with uncertain processing time has been established based on fuzzy programming theory. And the Maximum Membership Functions of Mean Value method is applied to convert the fuzzy scheduling model to the general optimization model. Furthermore, a fuzzy immune scheduling algorithm combined with the feature of the Immune Algorithm is proposed, which can prevents the possibility of stagnation in the iteration process and achieves fast convergence for global optimization. The effectiveness and efficiency of the fuzzy scheduling model and the proposed algorithm are demonstrated by simulation results.

Parallel machine scheduling models with fuzzy processing times

The purpose of this research is to develop a methodology for modeling parallel machine scheduling problems with fuzzy processing times. Three novel types of fuzzy scheduling models are presented. A hybrid intelligent algorithm is also designed for solving these models. Finally, some numerical examples are provided to demonstrate the computational efficiency of the proposed algorithm.

Fuzzy scheduling with application to real-time systems

Task scheduling is a main activity in the design of real-time systems (RTS). It assures both functionality and safety of such systems. RTS can be modeled as a set of periodic tasks that must be completed before specific deadlines. In this paper, we investigate the fuzzy scheduling models on RTS and the main methodologies that solve these models. Thus, we present general periodic task scheduling models with fuzzy deadlines and fuzzy processing times; scheduling algorithms based on optimal assignment of the priorities; and a more general framework for designing RTS, Rate Monotonic Scheduling Theory, that includes the scheduling algorithms. A case study will illustrate the use of the theory.

FUZZY PROJECT SCHEDULING MODELS UNDER INFLATION CONDITION

The paper defines four fuzzy project scheduling models under inflation condition without or with budget limit. The activity time is described by fuzzy relation or fuzzy variables. The models can be solved in the forms of crisp LP or NLP after using the concept of α-cut.