Advanced Planning And Scheduling Research Articles

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

Application of operations scheduling algorithms for the allocation of forensic science services

The activities of preparing and issuing Forensic Science Services reports are a public service of great importance for society and for the executive and judicial branches. However, notably, it has difficulties in meeting due dates, causing damage to all involved and generating additional costs for governments. In practice, it is observed that knowledge in the area of operations management is not used to determine the allocations of Forensic Science services at Criminalistics Technical Sections. Therefore, the objective of this research is to develop and apply knowledge related to the operations scheduling of Forensic Science reports. To fulfill the objective, the case study research method was applied in the Regional Technical Section of Criminalistics (STRC) of Três Corações - Minas Gerais in Brazil. Real data from that Forensic Unity carried out in the past were implemented in an APS (Advanced Planning and Scheduling) software and new allocations, based on the software's internal algorithms, were carried out. The results of allocations from the software have been compared with the previous results. The comparisons show that scheduling from the software's algorithms can bring better results.

To digitalize or not? Navigating and merging human- and technology perspectives in production planning and control

Contemporary manufacturing companies are navigating industrial digitalization anticipating increased production efficiency and competitiveness in a volatile environment. This study focuses on the implementation processes of digital tools for production planning and control (PPC), i.e., advanced planning and scheduling (APS) software, in relation to the application of analog planning with physical flow boards. Digital tools can support understanding the consequences of production changes and variations, hence facilitating adaptable and resilient manufacturing. However, technological changes can be daunting, and effective implementations require dynamic capabilities to remain competitive in elusive environments. The aim is to study the implementation processes of an APS software to understand the requirements of fruitfully moving from analog planning to next-generation digital tools for decision support in PPC. The paper presents an explorative case study, at a manufacturing company within the energy sector. The interview study took place over 9 months during 2020–2021, investigating current and retrospective aspects of the case across 2019–2021. The case study comprises 17 in-depth interviews with a range of company employees, e.g., logistics managers and functions responsible for digitalization development. The results highlight the challenges of implementing and especially trusting digital tools for PPC. To realize the value of digital tools for PPC, it is argued that it is imperative to simultaneously apply a human-centric perspective in decision making to ensure trustworthy, sustainable, and resilient human-data-technology nexus implementations towards smart manufacturing.

Advanced Process Control Modeling from an Advanced Planning and Scheduling Perspective

Further to basic process control strategies, advanced process control (APC) aims to improve the performance of the control by actuating towards input moves against effects of process disturbances. Typically addressing economic-based goals as objectives, APC includes a wide range of techniques embedded in process control systems generally found in continuous-process industries such as processing of food, fuels, bulk chemicals, metals, minerals, pulp and paper, among others. Different from the well-known APC techniques such as feedforward, decoupling, inferential, constraint control, etc., we address a novel approach to the dependent-independent or controlled-manipulated pairing of variables of the model predictive control (MPC), also known as moving horizon control (MHC). We introduce manipulated variables as batch-processes into the MPC formulation to build the dynamic model with their paired controlled variables. The manipulated-controlled MPC formulation is configured as an advanced planning and scheduling (APS) problem as opposed to a traditional APC problem whereby the manipulated-batch-process variables are considered with relative-time yields representing their finite impulse-responses or differenced step-responses. As we shall show, APC problems can be equally formulated as a batch-process in APS perspective. To eliminate the steady-state offset between the actual controlled value and its target or setpoint, it is well-known to apply bias-updating though other forms of parameter-feedback are possible. Typically, APS applications only employ variable-feedback, i.e., opening or starting inventories, properties, etc., but this alone will not alleviate the steady-state offset. A small and representative example highlights the APC as an APS modeling with parameter feedback.

An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks

In today’s manufacturing industry, enterprise-resource-planning (ERP) systems reach their limit when planning and scheduling production subject to multiple objectives and constraints. Advanced planning and scheduling (APS) systems provide these capabilities and are an extension for ERP systems. However, when integrating an APS and ERP system, the ERP data frequently lacks quality, hindering the APS system from working as required. This paper introduces a data quality (DQ) assessment framework that employs a Bayesian Network (BN) to perform quick DQ assessments based on expert interviews and DQ measurements with actual ERP data. We explain the BN’s functionality, design, and validation and show how using the perceived DQ of experts and a semi-supervised learning algorithm improves the BN’s predictions over time. We discuss applying our framework in an APS system implementation project involving an APS system provider and a medium-sized manufacturer of hydraulic cylinders. Despite considering the DQ assessment framework in such a specific context, it is not restricted to a particular domain. We close by discussing the framework’s limits, particularly the BN as a DQ assessment methodology and future works to improve its performance.

ALGORITHM AUTOMATION METHODS FOR WORKING WITH NETWORK EQUIPMENT

Relevance of the topic: methods of automating algorithms for the operation of network equipment are currently carried out using various tools and solutions for faster and more efficient execution of routine tasks, as well as for obtaining deterministic results. Production processes need algorithmization of ACS as a tool for saving time resource for management, coordinating production schedules, technical and technological limitations. You can form an alternative to quickly respond to precedents in production that have not predicted to change the course of technological processes. Most automation options today have disadvantages and advantages. The most popular way is queuing theory. Optimization of global search, additive method of criteria convolution, APS (Advanced Planning and Scheduling) systems for the most perfect planning are relevant. The specified software systematically synchronizes the planning process deployed for optimization and combines actions on the links that make up the supply chain, accentuates the framework and specifics determined by production and the industry. Most often, the network equipment is controlled by a software module that loads the machines and mechanisms needed by the enterprise for the production of products. Developers design systems to control timeslots and production lines. Known for the high efficiency of algorithms, the execution of which occurs through the automated control system, and the installation is carried out at an industrial enterprise to control loading operations. In the structure of external software, an algorithm is required. The program module contains commands for loading operations. They are distributed for technological lines, divided along the time axis. We see a rational solution to simulate a complex using simulation and analytics based on an automation product within the network equipment configured by the program. This will allow, under the conditions of the experiment, to determine how most likely traffic will be distributed in time, to prepare methods for automatic collection of information. Input traffic parameters, settings or the current load arriving at the equipment are set arbitrarily.

Order release in production planning and control systems: challenges and opportunities

Production planning and control (PPC) systems are important to the competitiveness of manufacturing firms and their ability to benefit from recent technological advances. We give an overview of research and unsolved research questions in PPC systems related to the order release function that sets output targets for autonomous production units that manage production at the shop-floor level. We describe the hierarchical Manufacturing Planning and Control (MPC) and Advanced Planning and Scheduling (APS) architectures for PPC systems prevalent in practice and the positioning of the order release task within these frameworks. We then describe the research streams relevant to order release, optimisation models for order release planning, and the relation of these models to the overall PPC system. Modelling the dynamic response of production units to time-varying work input, which represents an extension of classical production theory by incorporating the time dimension, is identified as the major modelling challenge. We conclude by suggesting several unsolved research questions that should encourage researchers to work on this topic which is far from mature.

Development of production planning system for shipbuilding using component-based development framework

Production planning is a key part of production management of manufacturing enterprises. Since computerization began, modern production planning has been developed starting with Material Requirement Planning (MRP), and today Enterprise Resource Planning (ERP), Advanced Planning and Scheduling (APS), Supply Chain Management (SCM) has been spreading and advanced. However, in the shipbuilding field, rather than applying these general-purpose production planning methodologies, in most cases, each shipyard has developed its own production planning system. This is because the applications of general-purpose production planning methods are limited due to the order-taking industry such as shipbuilding with highly complicated construction process consisting of millions of parts per ship. This study introduces the design and development of the production planning system reflecting the production environment of heavy shipyards in Korea. Since Korean shipyards such as Hyundai, Daewoo and Samsung build more than 10 ships per year (50–70 ships in the case of large shipyards), a planning system for the mixed production with complex construction processes is required. This study draws requirements using PI/BPR (process innovation and business process reengineering) methodology to develop a production planning system for shipyards that simultaneously build several ships. Then, CBD software development methodology was applied for the design and implementation of planning system with drawn requirements. It is expected that the systematic development procedure as well as the requirements and functional elements for the development of the shipyard production planning system introduced in this study will be able to present important guidelines in the related research field of shipbuilding management.

Framework and deployment of a cloud-based advanced planning and scheduling system

• This paper presented a cloud-based and advanced planning and scheduling (C-APS) system framework, it can be used as a reference for system developers to develop a cloud-based and object-oriented APS with simulation-based scheduling engine that can be applied in practice. • This paper proposed the service structure and approach of deploying the C-APS system in the public cloud infrastructure, platform, and service provider (e.g., AWS TM ) and hybrid cloud platforms. Readers may reference this approach to deploy a server-based enterprise information application system to a cloud-based system. • This paper presented the package diagram to collect the necessary data for building the case company's virtual factory model required by the C-APS system; this feature can significantly improve modeling efficiency and data stability. Besides, the C-APS's objected-oriented, simulation-based scheduling engine has the characteristics of quickly expanding and adjusting to the virtual plant model. • The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain; the scheduling quality is high; the computing time is short and acceptable for practical application. Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.

Development of a Cloud-based Advanced Planning and Scheduling System for Automotive Parts Manufacturing Industry

Facing the global trend of smart manufacturing, many countries around the world proposed its industry policy of smart factory, in which, cyber-physical production system (CPPS) plays the critical role. However, most of the manufacturing industries in Taiwan still heavily rely on the excel-based production planning and scheduling approach although ERP systems are widely used today. Besides, few small and medium-sized manufacturing enterprises (SMEs) have implemented the advanced planning and scheduling (APS) system due to the high implementation cost for satisfying the specific production characteristics and planning constraints. On the contrary, the cloud-based information system is conducive to practice smart manufacturing for SMEs, it does not require high-end devices to have high-speed computing ability, as long as they are in the network environment.Therefore, this research considered the characteristics of manufacturing process, automation and digitalization of automotive parts manufacturing industry to develop a cloud-based advanced planning and scheduling (CAPS) system which is implemented in the Amazon Web Service (AWSTM) platform. The planning engine of CAPS is an intelligent dynamic planning & scheduling (IDPS) simulation system, which can dynamically and effectively generate production and operations schedule. Production planners only need to maintain and upload the planning data to CAPS system and download the timely operations schedule and visualized analysis generated from CAPS’s web page. Applications of CAPS in a well-known automotive parts manufacturing company in Taiwan has been demonstrated in this paper.

An APS software selection methodology integrating experts and decisions-maker’s opinions on selection criteria: A case study

With important advancements achieved in information technology, wide varieties of advanced planning and scheduling (APS) software has emerged in recent decades. Each of those APS software uses thei...

Development of APS(advanced planning and scheduling) System for Small and Medium-sized Manufacturing Enterprise

Development of APS(advanced planning and scheduling) System for Small and Medium-sized Manufacturing Enterprise

A Hybrid Discrete Bat Algorithm with Krill Herd-based advanced planning and scheduling tool for the capital goods industry

Capital goods companies produce high value products such as power plant or ships, which have deep and complex product structures, with components having long process routings. Contracts usually include substantial penalties for late delivery. The high value of items can lead to substantial holding costs. Efficient schedules minimise earliness and tardiness costs and need to satisfy assembly and operation precedence constraints as well as finite capacity. This paper presents the first advanced planning and scheduling (APS) tool for the capital goods industry that uses a Discrete Bat Algorithm (DBA), modified DBA (MDBA) and hybrid DBA with Krill Herd algorithm (HDBK) to optimise schedules. The tool was validated using four data-sets obtained from a collaborating capital goods company. A sequential experimental strategy was adopted. The first experiment identified appropriate parameter settings for the DBA. The second experiment evaluated and compared the performance of the proposed HDBK algorithm with an Artificial Bee Colony, Krill Herd (KH), Modified KH, DBA and MDBA metaheuristics. The experimental results revealed that the HDBK performed best in terms of the minimum penalty cost for all problem sizes and achieved up to a 47.837% reduction in mean total penalty costs of extra-large problem size.

A framework for development of advanced planning and scheduling (APS) systems in glass container industry

PurposeThe purpose of this paper is to present a new framework for designing and implementing simple but effective advanced planning and scheduling (APS) systems in glass container industry by means of spreadsheets.Design/methodology/approachThe conceptual framework for APS system design is developed by integrating principles of end-user computing and mixed-integer programming (MIP). This framework comprises the APS implementation and its integration with enterprise resource planning systems. Such a proposal is applied in a real-world glass bottle manufacturer and its performance assessment is carried out by using metrics of the well-known supply chain operations reference (SCOR) model.FindingsThe case study shows that the framework application can improve the responsiveness of glass container supply chain (SC) as well as reduce significantly SC planning costs. A further assessment of the end-user computing satisfaction highlights the general acceptance of the framework.Practical implicationsThe proposed framework can assist glass industry managers and practitioners to solve their complex production planning problem since custom solutions are not available for this sector. Another important implication is the possible generalization of the framework given that MIP models for production planning are currently available in the literature for different industries.Originality/valueOn the one hand, the new framework successfully integrates concepts of end-user computing and MIP bridging the gap between theory of aforementioned areas and their practical applications. On the other hand, the paper contributes to the scarce literature on performance evaluation of APS systems and their impact on production system performance. The novel APS gain analysis based on SCOR metrics ensures that the system potential benefits are aligned with the modern concepts of supply chain management.

Development of a Cloud-based Advanced Planning and Scheduling System

Facing the global trend of Industry 4.0, many countries around the world proposed its industry policy of smart factory, in which, cyber-physical production system (CPPS) plays the critical role. A CPPS has the characteristics of distributed control, real time communication, computation and modularized. In Taiwan, most of the manufacturing industries (e.g., metal manufacturing, machinery and equipment manufacturing etc.) still heavily rely on the excel-based production planning and scheduling approach although ERP systems are widely used today. Furthermore, the advanced planning and scheduling (APS) systems have been developed over 15 years in Taiwan, however, very few of small and medium-sized manufacturing enterprises (SMEs) have implemented APS system due to the high implementation cost for satisfying the specific production characteristics and planning constraints.Therefore, this research will consider the characteristics of manufacturing process, automation and digitalization of metal manufacturing, machinery and equipment manufacturing industry and develop a cloud-based advanced planning and scheduling (CAPS) system, the planning engine is based on an intelligent dynamic planning & scheduling (IDPS) system, which can dynamically and effectively generate production and operations schedule, and provide the timely visualized analysis data for production planners in SMEs. Applications of CAPS in a metal manufacturing company has been demonstrated in this paper.

Synchronisation for smart factory - towards IoT-enabled mechanisms

ABSTRACTTraditionally, production scheduling is optimised using objective functions based on punctuality criteria such as earliness and tardiness. However, synchronisation in complicated manufacturing process is always neglected. Due to the lack of synchronisation, severe problems happen frequently such as long holding time and unreasonable setups. Motivated by the practical challenges, the authors are among the first to present the IoT-enabled real-time synchronisation approaches for an automobile standard part factory with the following objectives. First, the synchronisation paradigm is proposed in terms of two dimensions, namely the Horizontal Synchronisation (HSync) for the consistency of batch production progresses within one customer order, and the Vertical Synchronisation (VSync) for the coordination between adjacent processing stages. Second, the IoT-enabled synchronisation mechanisms using rule-based heuristics and real-time production data is presented. Third, an advanced planning and scheduling (APS) system framework is developed to automatically implement the synchronisation mechanisms. Finally, the effectiveness of the synchronisation mechanisms is verified through a case study.

An Application of Industry 4.0 to the Production of Packaging Films

An application of Industry 4.0 methods to the production of packaging films is presented. The production planning issues that are addressed often include contrasting objectives and strategies between customer's service and production optimization. We present an Advanced Planning and Scheduling (APS) tool that allows the decision maker to automatically generate and chose among a wide range of differently optimized scenarios. The vast amount of different results is analyzed and presented to the decision maker, using advanced data analytics techniques in order to put him/her in the condition to rapidly take an aware and solidly supported decision.

Comparison of simulated annealing and tabu-search algorithms in advanced planning and scheduling systems for TFT-LCD colour filter fabs

Manufacturers of thin-film transistor–liquid crystal displays (TFT–LCDs) have recently faced intense global competition and declining profits. They improve productivity and competitiveness by reducing cycle time, increasing equipment utilisation, integrating supply chain and planning facility setups. This article develops an advanced planning and scheduling (APS) system for 4.5th generation colour filter (CF) fabs via Microsoft Visual Basic for Application (VBA). The APS system considers the difference among orders and automatically assigns orders to production lines based on process characteristics while balancing equipment loading. The tabu-search (TS) and simulated annealing (SA) algorithms are applied to the APS system to optimise scheduling problems. Experimental results show that APS effectively completes the scheduling and saves approximately 75% of working time. These algorithms can also save approximately 15% of changeover waste and decrease the number of over-capacity days more effectively as compared with the original APS.

Dynamic production control system for SME: implementation on plastic blow-moulding machine

Most small and medium-sized enterprises (SME) own less-resources to invest on highly computerised production control system, the productivity can be improved by conserved and effective tool of dynamic production control when dealing with fluctuation business demands of globalisation is essential. This study is based on the principle of advanced planning and scheduling (APS) which is adopted eM-plant with Excel to simulate dynamic production control system that can meet the requirement of APS for SME. The virtual plant is exercised by simulation of eM-plant as to compare with the precedent production control system for the plastic blow-moulding machine plant on their restricted delivery requirement. Obviously, the refine dynamic production system concludes the KPIs are benefited from refine system which shorten order responding time to 15 minutes, shrink the duration of arranging order schedule from one day manually to 20 minutes, and escalate the rate of delivery on time to 80% from 45% previously. [Received 28 September 2015; Revised 24 June 2016; Accepted 1 July 2016]

A Case Study on Application of Dispatching Rule-Based Advanced Planning and Scheduling (APS) System

A Case Study on Application of Dispatching Rule-Based Advanced Planning and Scheduling (APS) System