Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine

Preventive maintenance (PM) planning and production scheduling are among the most important problems in the manufacturing industries. Researchers have begun to investigate the integrated optimization problem of PM and production scheduling with a single objective. However, many industries have trade-offs in their scheduling problems where multiple objectives must be considered in order to optimize the overall performance of the system. In this paper, five objectives, including minimizing maintenance cost, makespan, total weighted completion time of jobs, total weighted tardiness, and maximizing machine availability are simultaneously considered to optimize the integrated problem of PM and production scheduling introduced by Cassady and Kutanoglu. Multi-objective genetic algorithm (MOGA) is used to solve the integrated optimization problem. To illuminate the conflicting nature of the objective functions, decision-makers’ preferences of the multiple objectives are not integrated into the MOGA. The total weighted percent deviation, which represents not only the preferences of the objectives but also the deviations of the solutions, is proposed to help decision-makers select the best solution among the near-Pareto optimal solutions obtained by the MOGA. A numerical example reveals the necessity and significance of integrating optimization of PM and production scheduling considering multiple objectives.     

Scheduling sequence-dependent setup time job shops with preventive maintenance

Although many researchers have proposed different techniques to integrate production scheduling and preventive maintenance, these techniques have some drawbacks. For example, some of them are so intricate that one cannot easily implement them, or some strongly exploit specific features of the original studied problem that one cannot apply them to other problems. We hereby propose two techniques that are easy to understand and code, yet simplistically adaptable to any other machine-scheduling problems. This paper investigates job shop scheduling with sequence-dependent setup times and preventive maintenance policies. The optimization criterion is to minimize makespan. Four metaheuristics based on simulated annealing and genetic algorithms as well as adaptations of two metaheuristics in the literature are employed to solve the problem. The performances of the proposed algorithms are evaluated by comparing their solutions through two benchmarks based on Taillard’s instances.     

Intelligent aircraft maintenance support system using genetic algorithms and case-based reasoning

The maintenance of aircraft components is crucial for avoiding aircraft accidents and aviation fatalities. To provide reliable and effective maintenance support, it is important for the airline companies to utilise previous repair experience with the aid of advanced decision support technology. Case-based reasoning (CBR) is a machine learning method that adapts previous similar cases to solve current problems. To effectively retrieve similar aircraft maintenance cases, this research proposes using a CBR system to aid electronic ballast fault diagnosis of Boeing 747-400 airplanes. By employing genetic algorithms (GA) to enhance dynamic weighting and the design of non-similarity functions, the proposed CBR system is able to achieve superior learning performance as compared to those with either equal/varied weights or linear similarity functions.     

Simultaneous minimization of total tardiness and waiting time variance on a single machine by genetic algorithms

This paper considers a single machine scheduling problem, with the objective of minimizing a linear combination of total tardiness and waiting time variance in which the idle time is not allowed. Minimizing total tardiness is always regarded as one of the most significant performance criteria in practical systems to avoid penalty costs of tardiness, and waiting time variance is an important criterion in establishing quality of service (QoS) in many systems. Each of these criteria is known to be non-deterministic polynomial-time hard (NP-hard); therefore, the linear combination of them is NP-hard too. For this problem, we developed a genetic algorithm (GA) by applying its general structure that further improves the initial population, utilizing some of heuristic algorithms. The GA is shown experimentally to perform well by testing on various instances.     

Scheduling with past-sequence-dependent setup times and learning effects on a single machine

In this paper, we study the single-machine scheduling problems with learning effect and setup time considerations. The setup times are proportional to the length of the already-processed jobs, i.e., the setup times are past-sequence-dependent (p-s-d). The objective functions are to minimize the sum of the quadratic job completion times, the total waiting time, the total weighted completion time, the maximum lateness, the total absolute differences in waiting times, and the sum of earliness penalties subject to no tardy jobs, respectively. We show that the sum of the quadratic job completion times minimization problem, the total waiting time minimization problem, the total absolute differences in waiting times minimization problem, and the sum of earliness penalties minimization problem subject to no tardy jobs can be solved in polynomial time, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under some special cases.     

求解具有预防性维护的并行机调度的遗传算法

为了避免设备出现故障对生产造成损失,需对设备进行有效的预防性维护。本文研究了工件带有释放时间的预防性维护的并行机调度问题,以最小化总工期为优化目标。对该问题设计了一个遗传算法进行求解。染色体为工件序列和机器序列,采用最先适配启发式方法确定各工件的最优时间表。数值实验结果表明,本文设计的遗传算法的性能优于简单启发式算法——最短加工时间的最先适配算法,且计算时间是可接受的。     

Joint control of production, overhaul, and preventive maintenance for a production system subject to quality and reliability deteriorations

This research investigates the case of an unreliable manufacturing system subject to quality and reliability deterioration. In particular, we conjecture that the deterioration of the system leads to a continuous increase in the intensity of failures and a decrease on the quality of the parts produced. As such, deterioration implies a twofold effect on the manufacturing system. When the machine fails, minimal repair is conducted, leaving the machine at the same level of deterioration before failure. Hence, the quality of the parts produced and the failure intensity remain unchanged with this repair. Meanwhile, an overhaul refers to a perfect repair that completely restores the quality of the parts and the failure intensity of the machine. This option completely counters all the effects of the deterioration. Preventive maintenance may also be conducted, but it reduces the level of deterioration only partially, improving the quality of the units produced and the failure intensity just in part. This set of characteristics yields to the formulation of a new control model that simultaneously determines the optimal production plan, the overhaul, and preventive maintenance strategies. Such a joint control policy minimizes the total cost including the inventory holding, backlog, overhaul, preventive maintenance, and defective costs over an infinite planning horizon. Since the dynamics of the system change as a function of the level of deterioration, it is necessary to use its history for a proper formulation; therefore, a semi-Markov decision process is used. Numerical methods are applied to determine the control policy, and numerical examples are conducted as illustrations. An extensive sensitivity analysis is presented in order to confirm the structure of the control policy obtained and examine the effect of several parameters.     

Scheduling fuzzy job shop with preventive maintenance through swarm-based neighborhood search

Fuzzy job shop scheduling problem with preventive maintenance (FJSSP-PM) is seldom investigated. In this paper, FJSSP-PM with n resumable jobs processed on m machines are considered and an efficient swarm-based neighborhood search (SNS) is proposed, in which an ordered operation-based representation and the decoding procedure incorporating PM are given. Swap operation and binary tournament selection are applied to update the swarm of SNS for the best solutions of the problem. It is also proved that most of possible actual completion times lie in the cut of fuzzy completion time for each job. SNS is finally compared with some methods from literature and computational results demonstrate that SNS has promising advantage on fuzzy scheduling with PM.     

Joint optimization of spare parts inventory and maintenance policies using genetic algorithms

In general, the maintenance and spare parts inventory policies are treated either separately or sequentially in industry. However, since the stock level of spare parts is often dependent on the maintenance policies, it is a better practice to deal with these problems simultaneously. In this study, a simulation optimization approach using genetic algorithms (GAs) has been proposed for the joint optimization of preventive maintenance (PM) and spare provisioning policies of a manufacturing system operating in the automotive sector. A factorial experiment was carried out to identify the best values for the GA parameters, including the probabilities of crossover and mutation, the population size, and the number of generations. The computational experiments showed that the parameter settings given by the proposed approach achieves a significant cost reduction while increasing the throughput of the manufacturing system.     

Scheduling deteriorating jobs to minimize the makespan on a single machine

Machine scheduling problems with deteriorating jobs have received increasing attention in recent years, mostly focusing on the linear deterioration models. However, if certain maintenance procedures fail to be completed prior to a prespecified deadline, jobs will require extra time for successful accomplishment in some situations. Therefore, this paper addresses a single-machine problem where the objective is to minimize the makespan under the piecewise linear deterioration model. A branch-and-bound algorithm and two heuristic algorithms are provided to search for the optimal solution and near-optimal solutions, respectively. Computational results are also presented to evaluate the performance of the proposed algorithms.     

Genetic algorithms for single machine scheduling with quadratic earliness and tardiness costs

In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose a genetic approach based on a random key alphabet and present several algorithms based on this approach. These versions differ on the generation of both the initial population and the individuals added in the migration step, as well as on the use of local search. The proposed procedures are compared with the best existing heuristics, as well as with optimal solutions for the smaller instance sizes. The computational results show that the proposed algorithms clearly outperform the existing procedures and are quite close to the optimum. The improvement over the existing heuristics increases with both the difficulty and the size of the instances. The performance of the proposed genetic approach is improved by the initialization of the initial population, the generation of greedy randomized solutions, and the addition of the local search procedure. Indeed, the more sophisticated versions can obtain similar or better solutions and are much faster. The genetic version that incorporates all the considered features is the new heuristic of choice for small and medium size instances.     

考虑备件弹性订货的动车组部件分阶段维护决策

为探讨动车组部件预防性维护策略与备件订货、库存策略的联合优化问题,针对两级非完美维护下备件消耗过程的特殊性,以维护策略为驱动提出了备件订货批量可变的弹性订货策略,建立了面向分阶段预防性维护与备件弹性订货的联合优化模型.算例分析表明,弹性订货策略可以通过更低的备件库存量满足维护需求;分阶段预防性维护可以与两级非完美维护力...     

数控机床增加维修用单步控制模式

通过对2台FANUC系统的加工中心改进,分解对装料台、刀库等液压阀控制动作为单步手动执行,并用汉字提示各动作。使解决刀库、装料台等易发故障方便快捷、明了。     

An optimisation approach to the contour error control of CNC machine tools using genetic algorithms

A contour control strategy has been studied in this paper to improve the contour error of CNC machine tools. First, a single axis controller is analysed and then a velocity feedforward controller is added in the velocity loop. To further reduce the contour error, a cross-coupled controller is adopted and an algorithm for an on-line estimation of the contour error in arbitrary curved contouring is proposed. These controller parameters are all optimised by an efficient robust optimisation technique using genetic algorithms. Experimental results are provided to illustrate the proposed methods.     

Scheduling of complex manufacturing systems with Petri nets and genetic algorithms: a case on plastic injection moulds

This paper introduces significant improvements on a previous published work that addresses complex production scheduling problems using Petri nets (PNs) and genetic algorithms (GAs). The PN model allows a formal representation of the manufacturing system and of the special constraints of this kind of system, while the GA generates a near-optimal schedule through the structure provided by the PN. The corresponding manufacturing system is associated with a flexible job shop environment with features such as the fabrication of multiple parts and precedence relationships between such parts and assembly operations, in which the objective is the minimisation of the total weighted tardiness. As part of the modelling stage, a mixed integer linear programming formulation is proposed for this framework. The fabrication of a chess mould in a Colombian company is used in two ways: to introduce a proposed normalisation operator that improves the results by reducing the search space of the GA and to illustrate the use of PN modelling the special aforementioned constraints as well as the encoding of the chromosome used by the GA. The proposed approach was tested on randomly generated instances, and their performance was measure against optimal solutions or solutions provided by algorithms presented in previous work. The results confirm the relevance of this approach to schedule such complex manufacturing systems.     

Modal parameter identification by adaptive parameter domain with multiple genetic algorithms

Journal of Mechanical Science and Technology - The identification of aero-engine dynamic parameters is fundamental to establishing accurate dynamic models, which has a great effect on the accuracy...     

Nonlinear predictive control of a drying process using genetic algorithms

A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control.     

CNC micromilling properties and optimization using genetic algorithms

Micromachining operations are mainly restricted to precision machining of two-dimensional microparts, usually performed on microelectrical discharge machining or microlaser computer numerical control (CNC) machine tools. However, micromilling can fully exploit computer-aided design/computer-aided manufacturing (CAM) software abilities, in order to achieve more complex three-dimensional micropart geometries. After fully defining the micromilling process parameters and related constraints, optimization methodologies, such as genetic algorithms, can be coupled with CAM software, thus obtaining optimal process parameters with very small calculation cost. In this study, CNC micromilling process is systematically presented, along with respective micromilling tools and the necessary industrial equipment for the processes. Genetic algorithm code was developed in Visual Basic, which optimizes the process and ultimately yields optimal parameter values, including all process particulars. Two test cases were presented, and results were discussed in terms of micropart quality, production time, and calculation cost.