A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

Steel-making process scheduling using Lagrangian relaxation

The steel-making process, including steel-making and continuous casting, is usually the bottleneck in iron and steel production. Effective scheduling of this process is thus critical to improve productivity of the entire production system. Unlike the production scheduling in the machinery industry, steel-making process scheduling is characterized by the following features: job grouping and precedence constraints, set-up and removal times on the machines, and high job waiting costs. These features add extra difficulties to the scheduling problem. The objective is to ensure continuity of the production process and just-in-time delivery of final products. In this paper, a novel integer programming formulation with a 'separable' structure is constructed considering all the above-mentioned features. A solution methodology is developed combining Lagrangian relaxation, dynamic programming and heuristics. After relaxing two sets of 'coupling constraints', the relaxed problem is decomposed into smaller subproblems, each involving one job only. These subproblems are solved efficiently by using dynamic programming at the low level while the Lagrangian multipliers are iteratively updated at the high level by using a subgradient method. At the termination of such iterations, a two-stage heuristic is then used to adjust subproblem solutions to obtain a feasible schedule. A numerical experiment demonstrates that the method generates high quality schedules in a timely fashion.     

物料需求计划不稳定性的模拟研究

介绍物料需求计划不稳定性的基本概念和模拟研究方法。在不确定性需求的流动式计划环境下,研究冻结参数和计划算法在不同生产条件下对物料需求计划不稳定性的影响。通过设计模拟实验和大量模拟计算及统计分析表现：费用结构、预测模式、冻结比例、计划周期和计划算法对物料需求计划不稳定性有较大影响,且交互作用显著。研究结果对减小物料需求计划的不稳定性有一定指导意义。     

Robust and reactive project scheduling: a review and classification of procedures

The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of critical chain scheduling and indicate in which environments it is useful.     

A discrete time reactive scheduling model for new order insertion in job shop,make-to-order industries

This research presents a new reactive scheduling methodology for job shop, make-to-order industries. An integer linear programming formulation previously developed by the authors to schedule these types of industries is extended to address the problem of inserting new orders in a predetermined schedule, which is important in order-driven industries. A reactive scheduling algorithm is introduced to iteratively update the schedules. Numerical results on realistic examples of job shops of different sizes illustrate the effectiveness of the approach. In each case, different alternatives for inserting a set of new orders in an initial schedule are optimally generated, enabling the user to choose the most convenient one. Solutions are characterised by measures of scheduling efficiency as well as stability measures that assess the impact of rescheduling operations in a previously defined scheduling solution.     

Open-pit coal mine production sequencing incorporating grade blending and stockpiling options: An application from an Indian mine

Production scheduling is a crucial aspect of the mining industry. An optimal and efficient production schedule can increase the profits manifold and reduce the amount of waste to be handled. Production scheduling for coal mines is necessary to maintain consistency in the quality and quantity parameters of coal supplied to power plants. Irregularity in the quality parameters of the coal can lead to heavy losses in coal-fired power plants. Moreover, the stockpiling of coal poses environmental and fire problems owing to low incubation periods. This article proposes a production scheduling formulation for open-pit coal mines including stockpiling and blending opportunities, which play a major role in maintaining the quality and quantity of supplied coal. The proposed formulation was applied to a large open-pit coal mine in India. This contribution provides an efficient production scheduling formulation for coal mines after utilizing the stockpile coal within the incubation periods with the maximization of discounted cash flows. At the same time, consistency is maintained in the quality and quantity of coal to power plants through blending and stockpiling options to ensure smooth functioning.     

A genetic algorithm approach for production scheduling with mould maintenance consideration

The traditional approach for maintenance scheduling concerns single-resource (machine) maintenance during production which may not be sufficient to improve production system reliability as a whole. Besides, in the literature many researchers schedule maintenance activities periodically with fixed maintenance duration. However, in a real manufacturing system maintenance activities can be executed earlier and the maintenance duration will become shorter since less time and effort are required. A practical example is that in a plastic production system, the proportion of machine-related downtime is even lower than mould-related downtime. The planned production operations are usually interrupted seriously because of the mismatch among the maintenance periods between injection machine and mould. In this connection, this paper proposes to jointly schedule production and maintenance tasks of multi-resources in order to improve production system reliability by reducing the mismatch among various processes. To integrate machine and mould maintenance tasks in production, this paper attempts to model the production scheduling with mould scheduling (PS-MS) problem with time-dependent deteriorating maintenance schemes. The objective of this paper is to propose a genetic algorithm approach to schedule maintenance tasks jointly with production jobs for the PS-MS problem, so as to minimise the makespan of production jobs.     

Multi-stage lot sizing in a serial production system

This paper summarizes the development and implementation of a large-scale, multi-objective, lot-sizing model for scheduling tablet pharmaceuticals in a serial production system. The model places multiple resource capacity constraints on production at various stages of the manufacturing process and explicitly considers resource set-up times to ensure the generation of a feasible schedule. The multi-period model uniquely allows for variable lot sizes by stage and by period, subject to integrality restrictions arising from the technological nature of the multi-stage process. An integer goal programming approach is used for determining non-dominated solutions to the multi-item problem. The implementation and utilization of the model by an international manufacturer of pharmaceuticals is described, and model results are presented and discussed.     

Finite capacity scheduling method for MRP with lot size restrictions

Materials requirements planning (MRP) is a widely used production scheduling technique in the manufacturing industry. Based on the projected demand and the corresponding product structure, MRP prescribes the periodic production quantities for every end-item, subassembly and component. The goal of MRP is to reduce the inventory cost while simultaneously ensuring that dependent demand relationships are met. In its basic form, the dependent demand explosion used by MRP to schedule production does not consider the availability of resources, consequently the schedule is often capacity infeasible. The MRP progressive capacity analyser (PCA) procedure in which finite capacity planning and lot sizing are performed concurrently with the MRP bill of material (BOM) explosion process is introduced. The PCA procedure is executed in four steps. It models the lot size multiple restriction and can be easily modified for other lot sizing rules. This method has been validated and tested for sensitivity to fluctuations in demand patterns and lot sizes. It has also been compared and shown to outperform a popular methodology used in practice.     

基于流转批量的动态制造提前期算法研究

通过对企业资源计划(ERP)应用中生产过程的流转批量、加工批量与提前期的关系进行分析研究，以离散制造业中的批量生产为应用背景，结合批量因素提出多流转批次下的制造提前期计算方法，以流转批次反映工序间不同生产能力的相互影响，使车间作业中流转批量因素能在生产计划阶段通过提前期反映出来，以降低生产计划与车间作业计划之间的误差。并以实例应用说明该方法在生产计划制定中取得的效果。     

A heuristic algorithm to minimise the control places of a Petri net for the control of shared machines

This paper proposes a design methodology of a controller based on a Petri net for the shared machines of manufacturing systems. A conflict occurs when several manufacturing systems require the same shared machines at the same time. In this case, we have two issues; the scheduling of jobs on shared machines and the construction of a control procedure for scheduling. The scheduling of production on machines has been extensively studied over the past years by researchers. In this paper, our concern is not the scheduling problem but the construction of a control procedure for the production schedule. We propose a design of a Petri net based controller for the shared machines of manufacturing systems such that the number of control places in the Petri net is minimised. The experimental results show that the proposed algorithm performs better than an upper bound in terms of optimality. Also, the proposed algorithm is computationally more efficient than the optimal algorithm. Finally, we present the application of the proposed algorithm to a realistic batch process system shown in the literature.     

Single-machine scheduling for minimizing total cost with identical,asymmetrical earliness and tardiness penalties

Costs of flowtime, earliness and tardiness should be incorporated in real production scheduling. This paper constructs a single-machine scheduling model with a common due date to minimize the total cost including an identical, asymmetric earliness-tardiness cost. Several dominance conditions necessary for an optimal schedule are derived. A branch and bound algorithm exploiting the conditions is proposed to find an optimal schedule for an unconstrained version of the scheduling problem. Numerical experiments are demonstrated to show the effectiveness of the proposed method.     

Process and production planning for circuit board assembly

Process planning output can be post-processed into criteria for job scheduling decisions in printed circuit board assembly using surface-mount technology (SMT). Artificial intelligence-based techniques used in computerized planning and scheduling systems in other industries can be extended to printed circuit board operations. These techniques include blackboard architectures, object-oriented programming systems, and nonmonotonic reasoning systems. These techniques were used to demonstrate a unique architecture of blackboard systems that communicate via object-oriented messages to arrive at a shop-floor process plan and production schedule. The methodology was specialized to the assembly of printed circuit boards using surface-mounted components in a high-variety/low-volume product mix. The assembly facility was represented as a hierarchical object of product, process, and organizational knowledge. The system of working heuristics was integrated within a prototype environment with the practitioners that assisted in its development. The end result is a good working methodology for system development, implementation, and maintenance by knowledge worker involvement.     

Development of integrated production scheduling system for iron and steel works

The primary purpose of production scheduling at an integrated iron and steel works is to establish and execute the optimum production schedule that can improve both productivity and yield at each of the plants such as the steelmaking plants and rolling mills, while giving first consideration to the keeping of the date of delivery

However, it is by no means a simple matter to establish and control an integrated or consistent production, schedule covering about ten production processes for various products with an annual output of 6 million tons or more, or about 26 000 orders monthly

In our system-development efforts, therefore, we made the system under study simple and usable in practical applications, designed a scheduling simulator capable of policy selection, and thereby successfully developed an integrated production scheduling system centring around the scheduling simulator.     

Batch process schedule optimization under parameter volatility

A general strategy to treat the uncertainties in parameters of batch process scheduling has been developed. The strategy consists of three algorithms: flexible planning, flexible scheduling and reactive schedule adaptation. In this paper, we introduce the flexible scheduling and the reactive schedule adaptation algorithms. The flexible scheduling algorithm is based on both a Monte Carlo simulation and a simulated annealing. It can deal with multiple uncertain parameters and any type of probability density function for the uncertain parameter. We seek the flexible schedule that maximizes the expected profit, including net present values of products less raw material and processing costs, as well as due date penalties, inventory costs and setup costs. In reality, the values of uncertain parameters always change after the batch process schedule and plan are set up. Since the flexible schedule has periods of free time that can be used to accommodate uncertainties during the actual production, the reactive schedule adaptation algorithm can modify the flexible schedule in response to any change in an uncertain parameter with little or no penalty. This algorithm finds a new optimal or suboptimal solution under the new condition by using a combination of different local search methods, which are described.     

Modelling and analysis of wafer fabrication scheduling via generalized stochastic Petri net and simulated annealing

Some of the important characteristics of the semiconductor wafer fabrication factories are re-entrant process flows, a dynamic and uncertain environment, stringent production control requirements, etc. that pose a major challenge to the scheduling decisions in integrated circuit wafer fabrication process. Keeping in view the high capital investment and quick response to the market changes, it is essential for the integrated circuit fabrication process to exercise effective control on its production operations so that production resources can be employed in a flexible and efficient manner. The present research has focussed on the development of a generalized stochastic Petri net model that faithfully captures dynamic behaviours such as re-entrant processing, machine failures, loading and unloading, etc., pertaining to wafer fabrication. A simulated annealing-based scheduling strategy using mean cycle time and tardiness as performance measures was also developed to obtain an efficient and robust schedule for a known hard problem. Analysis of variance was applied to examine the interaction effects of various scheduling rules and to identify the main as well as the interaction effects of dispatching rules, dispatching rules and set-up rules, and set-up rules and batching rules. Paired t-tests were applied to assess the performance of rule combinations for lot and batch scheduling. The proposed simulated annealing-based solution methodology was tested on a well-known data set adopted from the literature and its performance reveals that simulated annealing-based scheduling rules work better than existing rules in terms of the two performance measures mean cycle time and tardiness.     

Custom simulator for logistic networks in downstream

Performing process simulations is extremely important for the oil industry. One of the fields of application is to schedule and coordinate the supply, distribution, and storage of raw materials, semi-finished, and finished bulk products in a logistics system. A novel simulation method has been introduced here for scheduling the transport of liquids through a pipeline network to ensure the balance among product availability, sales, and transport in each production and distribution point, to display the actual timetable and its effects, and to identify supply–demand imbalances in a model.     

An implementation of a decomposition-coordination approach to refinery production scheduling

This paper presents an implementation of a decomposition-coordination approach to petroleum refinery production scheduling. In this approach the scheduling problem is not formulated as a single mathematical programming model to be solved through a computerized system, but it is dealt with as a mathematical programming process, in which a number of small problems are practically solved, and their solutions are combined into an overall schedule Our approach does not pursue an exact optimum, but quite satisfactory schedules have been generated from a practical viewpoint and productivity in the scheduling process has been remarkably raised

An interactive computerized scheduling system has been tailored using a commercial MPS and its Extended Control Language (ECL). The man-hours required for making a final schedule has been reduced to one-third as compared with the previous way     

Using Functional Virtual Population as assistance to learn scheduling knowledge in dynamic manufacturing environments

When a scheduling environment is static and system attributes are deterministic, a manufacturing schedule can be obtained by applying analytical tools such as mathematical modelling technology, dynamic programming, the branch- and-bound method or other developed searching algorithms. Unfortunately, a scheduling environment is usually dynamic in a real manufacturing world. A production system may vary with time and require production managers to change schedule repeatedly. Therefore, the main aim here was to find a scheduling method that could reduce the need for rescheduling. An approach called Functional Virtual Population was proposed as assistance to learn robust scheduling knowledge for manufacturing systems under rationally changing environments. The used techniques include machine learning with artificial neural networks and IF-THEN scheduling rules. To illustrate the study in detail, a simulated flexible manufacturing system consisting of four machines, four parts, one automatic guided vehicle and eight buffers was built as the foundation for learning the concept. Also, Pythia software (a back-propagation-based neural networks) was employed as the learning tool in the learning procedure.