The impact of OPT and TPM on the economic production quantity

In manufacturing systems, the material flow is influenced by a number of factors, such as batching policies, capacity of machines, machine breakdowns, etc. Realizing the role of batching policies and reliability of machines in production systems, a mathematical model is presented here for determining optimal batching policies with the objective of improving the speed of material flow considering machine breakdowns and batch splitting and forming. This model is employed for studying (i) the significance of total preventive maintenance (TPM); (ii) the use of the optimized production technology (OPT) concept in batching policies; and (iii) the influence of a set-up cost reduction programme on the performance of manufacturing systems. The basic criterion considered for optimizing the batch sizes is the minimization of total system cost (TSC). An example problem is solved to explain the application of the model.     

Estimation of active maintenance opportunity windows in Bernoulli production lines

Effective and timely maintenance actions can sustain and improve both system availability and product quality in automated manufacturing systems. However, arbitrarily stopping machines for maintenance will occupy their production time and may introduce system-level production losses. There may exist hidden opportunities during production, such that specific machines can be actively shut down for preventive maintenance without penalizing the system throughput. In this paper, the time intervals for such opportunities are defined as active maintenance opportunity windows (AMOWs). A Bernoulli model is developed to analytically estimate AMOWs in two-machine-one-buffer production lines. A recursive algorithm based on an aggregation method is used to estimate AMOWs in long lines. For balanced production lines, a heuristic algorithm is proposed to estimate AMOWs in real time. The effectiveness of the methods has been validated through numerical studies.     

Use of (max, +) algebra for scheduling and optimization of HVLV systems subject to preventive maintenance

High-Variety, Low-Volume (HVLV) manufacturing systems are built to produce parts of several types in small quantities and under multiple production objectives. They relate to job-shop systems well known by researchers. One of the most studied assumptions of HVLV systems scheduling is considering that machines may be periodically unavailable during the production scheduling. This article deals with an analytical integrating method using (max, +) algebra to model HVLV scheduling problems subject to preventive maintenance (PM) while considering machines availability constraints. Each machine is subject to PM while maintaining flexibility for the start time of the maintenance activities during the planning period. The proposed model controls the placement of maintenance activities along the production operations. Indeed, the sequencing of maintenance activities on the machines depends on the criteria to minimize and may be different for each criteria value. For preventive maintenance, the proposed model aims to generate the best sequencing between activities while respecting the planning program that satisfy the optimal criteria values. In order to illustrate the performance of the proposed methodology, a simulation example is given.     

Integrated production planning and preventive maintenance in deteriorating production systems

This paper discusses the issue of integrating production planning and preventive maintenance in manufacturing production systems. In particular, it tackles the problem of integrating production and preventive maintenance in a system composed of parallel failure-prone production lines. It is assumed that when a production line fails, a minimal repair is carried out to restore it to an ‘as-bad-as-old’ status. Preventive maintenance is carried out, periodically at the discretion of the decision maker, to restore the production line to an ‘as-good-as-new’ status. It is also assumed that any maintenance action, performed on a production line in a given period, reduces the available production capacity on the line during that period. The resulting integrated production and maintenance planning problem is modeled as a nonlinear mixed-integer program when each production line implements a cyclic preventive maintenance policy. When noncyclical preventive maintenance policies are allowed, the problem is modeled as a linear mixed-integer program. A Lagrangian-based heuristic procedure for the solution of the first planning model is proposed and discussed. Computational experiments are carried out to analyze the performance of the method for different failure rate distributions, and the obtained results are discussed in detail.     

MIP formulation and heuristics for multi-stage capacitated lot-sizing and scheduling problem with availability constraints

In this paper, the problem of lot-sizing and scheduling of multiple product types in a capacitated flow shop with availability constraints for multi-period planning horizon is considered. In many real production systems, machines may be unavailable due to breakdowns or preventive maintenance activities, thus integrating lot-sizing and scheduling with maintenance planning is necessary to model real manufacturing conditions. Two variants are considered to deal with the maintenance activities. In the first, the starting times of maintenance tasks are fixed, whereas in the second one, maintenance must be carried out in a given time window. A new mixed-integer programming (MIP) model is proposed to formulate the problem with sequence-dependent setups and availability constraints. The objective is to find a production and preventive maintenance schedule that minimizes production, holding and setup costs. Three MIP-based heuristics with rolling horizon framework are developed to generate the integrated plan. Computational experiments are performed on randomly generated instances to show the efficiency of the heuristics. To evaluate the validity of the solution methods, problems with different scales have been studied and the results are compared with the lower bound. Computational experiments demonstrate that the performed methods have good-quality results for the test problems.     

Robust production and maintenance planning in stochasticmanufacturing systems

In this paper the authors consider a preventive maintenance and production model of a flexible manufacturing system with machines that are subject to breakdown and repair. The preventive maintenance can be used to reduce the machine failure rates and improve the productivity of the system. The control variables are the rate of maintenance and the rate of production; the objective is to choose a control process that optimizes a robust cost of inventory/shortage, production, and maintenance. The value function is shown to be locally Lipschitz and to satisfy a Hamilton-Jacobi-Isaacs equation. A sufficient condition for optimal control is obtained. Finally, an algorithm is given for solving the optimal control problem numerically     

An ant colony-based algorithm for integrated scheduling on batch machines with non-identical capacities

In this paper, a production–distribution scheduling problem with non-identical batch machines and multiple vehicles is considered. In the production stage, n jobs are grouped into batches, which are processed on m parallel non-identical batch machines. In the distribution stage, there are multiple vehicles with identical capacities to deliver jobs to customers after the jobs are processed. The objective is to minimize the total weighted tardiness of the jobs. Considering the NP-hardness of the studied problem, an algorithm based on ant colony optimization is presented. A new local optimization strategy called LOC is proposed to improve the local exploitation ability of the algorithm and further search the neighborhood solution to improve the quality of the solution. Moreover, two interval candidate lists are proposed to reduce the search for the feasible solution space and improve the search speed. Furthermore, three objective-oriented heuristics are developed to accelerate the convergence of the algorithm. To verify the performance of the proposed algorithm, extensive experiments are carried out. The experimental results demonstrate that the proposed algorithm can provide better solutions than the state-of-the-art algorithms within a reasonable time.

     

Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system

This paper investigates the maintenance problem for a flow line system consisting of two series machines with an intermediate finite buffer in between. Both machines independently deteriorate as they operate, resulting in multiple yield levels. Resource constrained imperfect preventive maintenance actions may bring the machine back to a better state. The problem is modeled as a semi-Markov decision process. A distributed multi-agent reinforcement learning algorithm is proposed to solve the problem and to obtain the control-limit maintenance policy for each machine associated with the observed state represented by yield level and buffer level. An asynchronous updating rule is used in the learning process since the state transitions of both machines are not synchronous. Experimental study is conducted to evaluate the efficiency of the proposed algorithm.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Buffer allocation and preventive maintenance optimization in unreliable production lines

In this paper, we consider a serial production line consisting of \(n\) unreliable machines with \(n-1\) buffers. The objective is to determine the optimal preventive maintenance policy and the optimal buffer allocation that will minimize the total system cost subject to a given system throughput level. We assume that the mean time between failure of all machines will be increased after performing periodic preventive maintenance. An analytical decomposition-type approximation is used to estimate the production line throughput. The optimal design problem is formulated as a combinatorial optimization one where the decision variables are buffer levels and times between preventive maintenance. To solve this problem, the extended great deluge algorithm is proposed. Illustrative numerical examples are presented to illustrate the model.     

基于退化机器模型的分布式柔性生产系统:性能分析、任务调度及预测性维护

在近些年的制造环境中,由于市场对多品种、小批量定制产品需求的增加,生产制造更加深入地向着柔性方向发展.如何利用现有资源,提高生产效率,实时地对系统性能进行评估与预测,并对基于小批量生产的实时调度进行优化改进,在分布式柔性生产系统中具有重要的研究意义.因此,基于退化机器模型的多批次串行生产线的性能进行分析,并对分布式生产系统进行任务调度及预测性维护.具体地说,对于具有退化机器模型及有限容量缓冲区的生产系统,首先采用马尔科夫分析方法建立数学模型;随后,提出精确方法来计算此生产系统模型实时的性能指标,并针对该模型下的调度问题,设计最优完成时间指标优化算法;此外,提出基于退化机器模型的预测性维护策略以减少完成时间;最后,通过数值实验验证该算法的可行性和有效性.     

一种考虑多台同种机床分配的有效的单元构成方法

在实际的制造系统中，由于生产能力的需求，某些种类的机床具有多台，但现有的单元构成方法无法描述这一问题，从而产生不合理的成组，本文给出的网络模型成功地描述了这一问题，从而得出了一个有产的、更加实用的单元化制造系统的设计方法。     

A Tractable Class of Maximal Hedging Policies in Multi-Part Manufacturing Systems

The flow control problem in multi-part failure prone manufacturing systems is considered. While computationnaly attractive, the near optimal controllers of Caramanis and Sharifnia, suffer from the drawback that the production capacity set must be approximated via a very restricted set of inscribed hypercubes, namely those for which a componentwise feasibility requirement is satisfied. Also, due to the completely decoupled nature of production along each component, utilization of the restricted capacity set is suboptimal. A class of capacity set incribed hypercube policies called simple maximal hedging (SMH) policies is introduced. In SMH policies production levels along the various components of the capacity set are coupled, the componentwise feasibility requirement is lifted, and there is no underutilization of production capacity if needed. In a p part types manufacturing system, for partwise additive cost functionals, it is shown that performance evaluation of a given SMH policy reduces to the analysis of p decoupled (fictitious) semi-Markovian machines. The machines are Markovianized via first passage-time analysis and a Padé approximants technique. Numerical optimization over the class of SMH policies in a sample manufacturing system indicates that their performance can come close to that of the optimal control.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

Earliness–tardiness minimization on a single machine to schedule preventive maintenance tasks: metaheuristic and exact methods

In this paper, we consider the problem of scheduling a set of M preventive maintenance tasks to be performed on M machines. The machines are assigned to execute production tasks. We aim to minimize the total preventive maintenance cost such that the maintenance tasks have to continuously be run during the schedule horizon. Such a constraint holds when the maintenance resources are not sufficient. We solve the problem by two exact methods and meta-heuristic algorithms. As exact procedures we used linear programming and branch and bound methods. As meta-heuristics, we propose a local search approach as well as a genetic algorithm. Computational experiments are performed on randomly generated instances to show that the proposed methods produce appropriate solutions for the problem. The computational results show that the deviation of the meta-heuristics solutions to the optimal one is very small, which confirms the effectiveness of meta-heuristics as new approaches for solving hard scheduling problems.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

An integrated control strategy for simultaneous robot assignment,tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network

There has been a leap in the field of smart manufacturing with the advancement of automation systems, robotic technology, big data analytics, and state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Three very important aspects of smart manufacturing systems are system productivity, product quality, and maintenance of machines and equipment. These three issues are strongly interrelated and collectively determine the performance of a smart manufacturing system. Although there has been significant studies in production control, quality control and maintenance scheduling to address each of these aspects individually, there has been a lack of sufficient studies taking all of them into consideration in one control scheme. In this paper, a mobile multi-skilled robot operated Flexible Manufacturing System (FMS) is considered and a model that integrates robots, individual workstation processes and product quality is developed using a Heterogeneous Graph Structure. Heterogeneous Graph Neural Network (HGNN) is used to aggregate local information from different nodes of the graph model to create node embeddings that represent global information. A control problem is then formulated in the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework to simultaneously consider robot assignment, product quality and maintenance scheduling. The problem is solved using Multi-Agent Reinforcement Learning (MARL). A case study is presented to demonstrate the effectiveness of the HGNN-MARL control strategy by comparing it to three baselines and the naive MARL policy without HGNN.     

Reliability evaluation for a manufacturing network with multiple production lines

This paper focuses on performance evaluation of a manufacturing system with multiple production lines based on the network-analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multi-state). Hence, the manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. This paper intends to measure the probability that the manufacturing network can satisfy customers’ orders. Such a probability is referred to as the system reliability. A graphical representation is first proposed to transform a manufacturing system into a manufacturing network. Thereafter, we decompose the manufacturing network into general processing paths and reworking paths. Three algorithms are subsequently developed for different scenarios and multiple production lines to generate the minimal capacity vectors that machines should provide to satisfy demand. The system reliability can be derived in terms of such capacity vectors afterwards.     

A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem

There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problems in view of its characteristic that has high efficiency and that is fit for practical application [1]. Two different scale numerical examples demonstrate the genetic algorithm proposed is efficient and fit for larger scale identical parallel machine scheduling problem for minimizing the makespan. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this article, a kind of genetic algorithm based on machine code for minimizing the processing times in non-identical machine scheduling problem is presented. Also triangular fuzzy processing times are used in order to adapt the GA to non-identical parallel machine scheduling problem in the paper. Fuzzy systems are excellent tools for representing heuristic, commonsense rules. That is why we try to use fuzzy systems in this study.     

Joint optimization of job scheduling and maintenance planning for a two-machine flow shop considering job-dependent operating condition

Two-machine flow shops are widely adopted in manufacturing systems. To minimize the makespan of a sequence of jobs, joint optimization of job scheduling and preventive maintenance (PM) planning has been extensively studied for such systems. In practice, the operating condition (OC) of the two machines usually varies from one job to another because of different processing covariates, which directly affects the machines’ failure rates, PM plans, and expected job completion times. This fact is common in many real systems, but it is often overlooked in the related literature. In this study, we propose a joint decision-making strategy for a two-machine flow shop with resumable jobs. The objective is to minimize the expected makespan by taking into account job-dependent OC. We consider two situations. In the first situation, where the failure rate of a machine under a fixed OC is constant, a hybrid processing time model is proposed to obtain the optimal job sequence based on the Johnson's law. For the second situation, where the failure rate of a machine is time-varying, the job sequence and PM plan are jointly optimized. An enumeration method is adopted to find the optimal job sequence and PM plan for a small-scale problem, and a genetic algorithm-based method is proposed to solve a large-scale problem. Numerical examples are provided to demonstrate the necessity of considering the effect of job-dependent OC and the effectiveness of the proposed method in handing such joint decision-making problems in manufacturing systems.