Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.     

Flowshop scheduling problem with a batching machine and task compatibilities

This paper deals with the problem of task scheduling in a flowshop with two (discrete and batching) machines. Each task has to be processed by both machines. All tasks visit the machines in the same order. The first machine is a discrete machine that can process no more than one task at a time, and the second machine is a batching machine that can process several tasks per batch with the additional feature that the tasks of the same batch have to be compatible. A compatibility relation is defined between each pair of tasks, so that an undirected compatibility graph is obtained which turns out to be an interval graph. The batch processing time is equal to the maximal processing time of the tasks in this batch and all tasks of the same batch start and finish together. The aim is to make batching and sequencing decisions and minimize the makespan.     

Throughput gain with parallel flow in automated flow lines

Circuit board assembly is mostly done in conveyorized flow lines in which each board visits each machine in a fixed sequence. It is common knowledge that balanced flow lines provide high throughput and low cost in high volume and low mix manufacturing. However, in flow lines, a single machine interruption stops the entire line when the buffer capacity is low. As a result, the throughput of circuit board assembly lines is often much lower than bottleneck machine throughput capacity. Lines with parallel machines can potentially reduce the effect of individual machine interruptions. This research is to quantify the throughput gain of parallel segments with analytical models. Simulation and a case study were also conducted to compare with the analytical results. Note to Practitioners-In theory, transfer lines or flow lines generate high throughput with low work-in-process. In reality, the benefit can be achieved only if the line is free of interruptions and is balanced. In most manufacturing systems, interruptions are common due to setups, material replenishments, errors and occasional machine breakdowns. An interrupted station can stop the entire line quickly in a transfer or flow line. In addition, in most modern manufacturing systems, the batch sizes become smaller and smaller. When the batches are very small (in extreme case, batch size of 1), it is almost impossible to achieve perfect balance when different jobs are processed in the same line. In both cases, the parallel segments in the line can increase the throughput without increasing the work-in-process. This paper presented the evidence via analytical models and simulation.     

Makespan minimization in a no-wait flow shop problem with two batching machines

This paper deals with the problem of task scheduling in a no-wait flowshop with two batching machines. Each task has to be processed by both machines. All tasks visit the machines in the same order. Batching machines can process several tasks per batch so that all tasks of the same batch start and complete together. The batch processing time for the first machine is equal to the maximal processing time of the tasks in this batch, and for the second machine is equal to the sum of the processing times of the tasks in this batch. We assume that the capacity of any batch on the first machine is bounded, and that when a batch is completed on the first machine it is immediately transferred to the second machine. The aim is to make batching and sequencing decisions that allow the makespan to be minimized.     

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.     

Performance analysis of manufacturing systems composed of modular machines using the universal generating function

The modularity and reconfigurability of the building blocks of modern manufacturing systems have to be considered when evaluating their performance. This paper proposes a model for evaluating system availability and expected production rates for manufacturing systems that are composed of unreliable modular machines with multiple functionally parallel production units. These units are treated as independent modules, where the breakdown/stoppage of one unit does not necessitate the failure/stoppage of the whole machine and its production. The considered systems are multi-state manufacturing systems (MSMS) that can handle multiple parts simultaneously, and their structure is that of nonbuffered flow lines allowing paralleling of identical multi-state modular machines (MSMM) in each production stage. In spite of the inherent computational complexity of the proposed analysis, due to the large number of system states, it was made possible by the use of the universal generating function (UGF) technique, which proved efficient for large MSMS. The proposed model was applied to a number of case studies for demonstration and verification. The case studies were based on a family of engine front covers. The results show that machines with a larger number of modules, usually thought of as having lower availability, provided higher overall system availability in the case of machines with multiple spindles. Based on the new analysis and results, it is recommended that system designers favorably consider machines with multiple spindles rather than increasing the number of machines in parallel. These results provide an important support for the use of modular/reconfigurable equipment compared with traditional equipment, in spite of the higher cost.     

Scheduling hybrid flowshop with parallel batching machines and compatibilities

This paper considers a two-stage hybrid flowshop problem in which the first stage contains several identical discrete machines, and the second stage contains several identical batching machines. Each discrete machine can process no more than one task at time, and each batching machine can process several tasks simultaneously in a batch with the additional feature that the tasks of the same batch have to be compatible. A compatibility relation is defined between each pair of tasks, so that an undirected compatibility graph is obtained which turns out to be an interval graph. The batch processing time is equal to the maximal processing time of the tasks in this batch, and all tasks of the same batch start and finish together. The goal is to make batching and sequencing decisions in order to minimize the makespan. Since the problem is NP-hard, we develop several heuristics along with their worst cases analysis. We also consider the case in which tasks have the same processing time on the first stage, for which a polynomial time approximation scheme (PTAS) algorithm is presented.     

A modeling technique for loading and scheduling problems in FMS

In recent years, due to highly competitive market conditions, it has become necessary for manufacturing systems to have quick response times and high flexibility. Flexible manufacturing systems (FMS's) have gained attention in response to this challenge. FMS has the ability to produce a variety of parts using the same system. However this flexibility comes at the price, which is the development of efficient and effective methods for integrated production planning, and control.In this paper, we analyze the production planning problem in flexible manufacturing systems. We address the problems of part loading, tool loading, and part scheduling. We assume that there is a set of tools with known life and a set of machines that can produce a variety of parts. A batch of various part types is routed through this system with the assumption that the processing time and cost vary with the assignment of parts to different machines and assignment of various tool sets to machines. We developed a mathematical model to select machines and assign operations and the required tools to machines in order to minimize the summation of maximum completion time, material handling time, and total processing time.We first integrate and formulate loading, and routing, two of the most important FMS planning problems, as a 0–1 mixed integer programming problem. We then take the output from the integrated planning model and generate a detailed operations schedule. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as production rate and utilization.     

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.     

A method for aligning serial batching decisions in a two-level organization of factory and distributed workstations

Large factories that manufacture high mixes of complex products are usually composed of a number of workstations and the manufacturing control function is divided between a factory and a workstation level. While the management of individual workstations tends to focus on efficient machine utilization, the top-level factory management is usually concerned with job flow control. Integration of operation decisions between the two organization levels can recover productivity loss stemming from disparate objectives. This paper presents a method for aligning the job batching decision for serial-batch machines that require machine setup to serve stochastic arrivals of multiple job types. The effect of batching on flow time is first analyzed and closed-form formulas for the probability of setup are derived for a time-based batching policy. The misalignment in batching decisions at the two organization levels is next illustrated. Finally, a state-based performance measure is designed for decision integration. Numerical simulation and regression are used to test the proposed method. The main contribution of this paper is on developing a distributed vertical alignment method which compliments the approaches of horizontal coordinated scheduling and vertical functional decomposition in architecture design of distributed manufacturing control.     

Solving a bi-objective unrelated parallel batch processing machines scheduling problem: A comparison study

Nowadays in competitive markets, production organizations are looking to increase their efficiency and optimize manufacturing operations. In addition, batch processor machines (BPMs) are faster and cheaper to carry out operations; thus the performance of manufacturing systems is increased. This paper studies a production scheduling problem on unrelated parallel BPMs with considering the release time and ready time for jobs as well as batch capacity constraints. In unrelated parallel BPMs, modern machines are used in a production line side by side with older machines that have different purchasing costs; so this factor is introduced as a novel objective to calculate the optimum cost for purchasing various machines due to the budget. Thus, a new bi-objective mathematical model is presented to minimize the makespan (i.e., C_max), tardiness/earliness penalties and the purchasing cost of machines simultaneously. The presented model is first coded and solved by the ε-constraint‌ method. Because of the complexity of the NP-hard problem, exact methods are not able to optimally solve large-sized problems in a reasonable time. Therefore, we propose a multi-objective harmony search (MOHS) algorithm. the results are compared with the multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective ant colony optimization algorithm (MOACO). To tune their parameters, the Taguchi method is used. The results are compared by five metrics that show the effectiveness of the proposed MOHS algorithm compared with the MOPSO, NSGA-II and MOACO. At last, the sensitivity of the model is analyzed on new parameters and impacts of each parameter are illustrated on bi- objective functions.     

连铸-轧制生产中带有批决策的排序问题的建模与优化方法

以长材产线为背景, 研究了炉次在连铸及轧制阶段的组批及批排序问题. 与以往将连铸、轧制分开研究不同, 本文同时考虑连铸和轧制阶段对组批及批排序的要求, 还考虑了下游工序精整机组负荷均衡生产的要求. 为该问题建立了新的混合整数规划(Mixed integer programming, MIP) 模型. 由于问题的NP-hard 属性和模型的大规模特征, 以及工业应用的实际要求, 本文提出了改进的分散搜索(Scatter search, SS) 算法用于求解该问题. 在改进的SS 算法中, 利用解的相关性质来限制搜索空间, 并将变邻域搜索策略引入, 从而结合解的多样性及邻域互补性特点, 充分发挥算法混合的优势. 实际数据的计算结果验证了改进SS 算法的有效性.     

Dynamic programming solution to the batching problem in just-in-time flow-shops

Mixed-model manufacturing systems are widely used by companies, in order to meet the customers’ demand for a variety of products, in an efficient way. This paper is concerned with a special class of mixed-model manufacturing systems: flow-shops. In a flow-shop, each product has to be processed by a number of machines, following a common route. We study the production smoothing problem under presence of non-zero setup and processing times which also vary among the products. We split the master problem into two sub-problems which are concerned with determining the batch sizes and production sequences, respectively. We develop a dynamic programming procedure to solve the batching problem, and suggest using an existing method from the current literature to solve the sequencing problem. We conduct a computational study and show that our solution approach is effective in meeting the JIT goals and efficient in its computational requirements.     

Production planning in additive manufacturing and 3D printing

Additive manufacturing is a new and emerging technology and has been shown to be the future of manufacturing systems. Because of the high purchasing and processing costs of additive manufacturing machines, the planning and scheduling of parts to be processed on these machines play a vital role in reducing operational costs, providing service to customers with less price and increasing the profitability of companies which provide such services. However, this topic has not yet been studied in the literature, although cost functions have been developed to calculate the average production cost per volume of material for additive manufacturing machines.In an environment where there are machines with different specifications (i.e. production time and cost per volume of material, processing time per unit height, set-up time, maximum supported area and height, etc.) and parts in different heights, areas and volumes, allocation of parts to machines in different sets or groups to minimize the average production cost per volume of material constitutes an interesting and challenging research problem. This paper defines the problem for the first time in the literature and proposes a mathematical model to formulate it. The mathematical model is coded in CPLEX and two different heuristic procedures, namely ‘best-fit’ and ‘adapted best-fit’ rules, are developed in JavaScript. Solution-building mechanisms of the proposed heuristics are explained stepwise through examples. A numerical example is also given, for which an optimum solution and heuristic solutions are provided in detail, for illustration. Test problems are created and a comprehensive experimental study is conducted to test the performance of the heuristics. Experimental tests indicate that both heuristics provide promising results. The necessity of planning additive manufacturing machines in reducing processing costs is also verified.     

A vector space model approach to production planning for a multi-product,multi-cell and multi-stage manufacturing system

This paper develops a formal mathematical approach to aggregate production planning for a multi-product, multi-cell and multi-stage manufacturing system. The model, based upon a vector space approach, includes all the important variables relating to the demand for individual items, inventory levels, the availability of machines taking into account any breakdowns, subcontracting of orders and overtime working. The computational procedure for determining the production planning strategies, in terms of overtime/undertime working and increase/decrease in the number of orders subcontracted, are presented. Three numerical examples are presented showing the use of the model developed. This approach makes it possible to develop realistic models of practical manufacturing systems. It is particularly applicable to flexible manufacturing systems.     

Robust planning in optimization for production system subject to random machine breakdown and failure in rework

This study is concerned with robust planning in optimization, specifically in determining the optimal run time for production system that is subject to random breakdowns under abort/resume (AR) control policy and failure in rework. In most real-life production processes, generation of defective items and breakdowns of manufacturing equipment are inevitable. In this study, random defective rate is assumed and all manufactured items are screened. The perfect quality, reworkable and scrap items are identified and separated; failure-in-rework is assumed. The system is also subject to random machine breakdown; and when it occurs, the AR policy is adopted. Under such policy, the production of the interrupted lot will be immediately resumed when the machine is restored. Mathematical modeling and derivation of the production-inventory cost functions for both systems with/without breakdowns are presented. The renewal reward theorem is used to cope with the variable cycle length when integrating cost functions. The long-run average cost per unit time is obtained. Theorems on convexity and on bounds of production run time are proposed and proved. A recursive searching algorithm is developed for locating the optimal run time that minimizes the expected production-inventory costs. A numerical example with sensitivity analysis is provided to give insight into the optimal operational control of such an unreliable system.     

A computer simulation model for studying the performance of coordinate measuring machines

The increasing trend toward computer-integrated manufacturing (CIM) in today's industry created a need for an effective process control. The objective of the inspection process is not only preventing shipment of defective parts but also providing a feedback to keep the manufacturing process in control. Through data processing capability, speed, and flexibility of operation, coordinate measuring machines (CMMs) play an important role for computer-integrated manufacturing (CIM). This paper introduces coordinate measuring machines and studies their performance. A computer simulation method for studying the performance of such machines working in a production line is developed. In this paper, CMM performance is measured by its speed and flexibility in performing measurements. In flexible manufacturing systems (FMS), CMMs serve as the inspection work station where arrival time of parts to be measured vary according to the flow of operations. The developed simulation model provides information about the machine, scheduled time for parts to be measured, and delay time for the measuring process.     

Batching and scheduling in a multi-machine flow shop

We study the problem of batching and scheduling n jobs in a flow shop comprising m, m≥2, machines. Each job has to be processed on machines 1,…,m in this order. Batches are formed on each machine. A machine dependent setup time precedes the processing of each batch. Jobs of the same batch are processed on each machine sequentially so that the processing time of a batch is equal to the sum of the processing times of the jobs contained in it. Jobs of the same batch formed on machine l become available for a downstream operation on machine l+1 at the same time when the processing of the last job of the batch on machine l has been finished. The objective is to minimize maximum job completion time. We establish several properties of an optimal schedule and develop polynomial time algorithms for important special cases. They are improvements over the existing methods with regard to their generality and time efficiency.     

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.     

Bicriteria robotic operation allocation in a flexible manufacturing cell

Consider a manufacturing cell of two identical CNC machines and a material handling robot. Identical parts requesting the completion of a number of operations are to be produced in a cyclic scheduling environment through a flow shop type setting. The existing studies in the literature overlook the flexibility of the CNC machines by assuming that both the allocation of the operations to the machines as well as their respective processing times are fixed. Consequently, the provided results may be either suboptimal or valid under unnecessarily limiting assumptions for a flexible manufacturing cell. The allocations of the operations to the two machines and the processing time of an operation on a machine can be changed by altering the machining conditions of that machine such as the speed and the feed rate in a CNC turning machine. Such flexibilities constitute the point of origin of the current study. The allocation of the operations to the machines and the machining conditions of the machines affect the processing times which, in turn, affect the cycle time. On the other hand, the machining conditions also affect the manufacturing cost. This study is the first to consider a bicriteria model which determines the allocation of the operations to the machines, the processing times of the operations on the machines, and the robot move sequence that jointly minimize the cycle time and the total manufacturing cost. We provide algorithms for the two 1-unit cycles and test their efficiency in terms of the solution quality and the computation time by a wide range of experiments on varying design parameters.