Advanced Planning And Scheduling Systems Research Articles

Current Situation and Development of Advanced Planning and Scheduling System Based on Group Optimization Algorithm in Discrete Industry

Discrete industry, especially job shop scheduling, has always been the key industry of Advanced Planning and Scheduling system (APS) system application. Based on Particle Swarm optimization (PSO), this paper introduces the Group swarm optimization algorithm, expounds the relevant theory and development status of APS, introduces the application of Particle Swarm optimization and Artificial Bee Colony optimization algorithm in APS system, and analyzes the performance and efficiency of the two algorithms. Finally, it predicts the future development trend of APS: the core algorithm will adopt a variety of hybrid algorithms, and the data flow will be combined with ERP / MES system.

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.

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 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

Integrated Production and Logistics Planning

<p class="0abstract">Industrial planning systems have undergone considerable evolution in recent decades, starting from MRP, then ERP, and finally advanced planning and scheduling systems (APS). This evolution took place by dint of the evolution of information and communication technologies, and also due to industrial stakeholder integration tendency forming global supply chains in order to optimize costs and lead times. The purpose of this article is to present a new integrated short and medium term planning approach for upstream logistics (necessary for procurement) and production, this new approach is the MLRP "Manufacturing and Logistic Requirement Planning ". We introduce in this paper a new bill of materials structure that is adapted to the MLRP, and then we present the proposed algorithm which allows determining production and logistics needs for all bills of materials components. Finally, we present the proposed information system that implements the MLRP.</p>

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.

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.

Scheduling heuristic resourced-based on task time windows for APS (Advanced planning and scheduling) Systems

APS Systems (Advanced planning and scheduling) can provide production plans that enable a detailed analysis on the impact and possibility of analyzing different scenarios for a plant. The present work proposes an allocation heuristics of the type used in resource-based (bottleneck) APS Systems and uses task time windows in situations where product delivery time is important. The heuristics uses loading information from the equipment to dynamically assign (at each step) the equipment and the bottleneck time interval. Based on this information, it allocates the lots assigned to this equipment in the manner with the lowest influence on the bottleneck, so as to reduce its importance in the bottleneck problem without increasing its gravity. This heuristics enables successive and correct handling of all existing bottlenecks by updating the information at each step via constrained propagation mechanisms. The problem addressed is the scheduling of production in lot plants. Short-term problems with constraints on shared intermediate product storage resources (equipment) and plant operation in on-demand mode are considered.

A Constraint Satisfaction Coordination Approach for Distributed Supply Chain Production Planning

Currently, distributed supply chain production planning (SCPP) is a practical problem affecting global supply chain production. Distributed SCPP is a complex constraint satisfaction problem (CSP) comprising a series of constraints intra- and inter-production members on orders, capacity, and materials. Previous studies have proposed various advanced planning and scheduling (APS) algorithms for companies to solve internal CSPs related to production planning. Distributed SCPP problems additionally require a coordination approach to resolve conflicts among the production plans of individual production members. Several distributed CSP (DCSP) algorithms have been proposed to guide the coordination for various DCSP conditions and objectives. Based on these techniques, we propose a coordination approach for achieving consistency among individual production plans to develop an integral supply chain production plan. First, we employed an APS system to resolve the internal production planning constraints and to develop a feasible production plan for each production member. Next, we developed a hybrid DCSP coordination algorithm based on agent-ordering asynchronous weak-commitment search (AWS) algorithm and asynchronous incremental relaxation (IR) algorithm concepts to facilitate production planning coordination and resolve constraint conflicts among individual production plans. Finally, a thin-film transistor liquid crystal display (TFT-LCD) supply chain was used to illustrate and verify the proposed coordination approach.

When should advanced planning and scheduling systems be used in sales and operations planning?

Purpose – The purpose of this paper is to explore how the context affects successful use of advanced planning and scheduling (APS) systems in sales and operations planning (S&OP) processes, and how individual, technological, and organizational (ITO) dimensions affect this procedure. Design/methodology/approach – This is a qualitative case study of two APS system-supported S&OP processes. The work aims to generate propositions concerning the relationships among the use of APS system, the context, ITO dimensions, and fulfillment of S&OP aims. Findings – Use of APS systems was especially appropriate in support of S&OP processes in complex planning environments and when S&OP aims were ambitious. ITO dimensions were important influences on successful APS system use in most contexts. APS systems were not considered appropriate when having S&OP processes with ambitious aims and low individual and organizational maturities. Use of APS systems was also inappropriate when the extent of technological maturity was minimal. S&OP processes with ambitious aims, operating within a complex planning environment, are difficult if not impossible to implement without the support of APS systems. Practical implications – The suggestions on when APS systems should be used in different S&OP environments will be useful to companies implementing or about to implement APS systems. Originality/value – APS systems offer great potential if they are effectively used to support S&OP, still the use of APS system in S&OP is unexplored. The paper shows how the context and the ITO dimensions affect the successful use of APS systems in S&OP processes.

Time windows and constraint programming to deal with strong restriction in the due date of productive systems

The APS (Advanced Planning and Scheduling) systems are widely used by companies; however, the traditional APS systems cannot deal with problems whose the due date is a strong restriction. The problem derives from the way companies use their scheduling heuristics. This paper addresses this problem by using the concept of time windows with the constraint programming mechanism. A procedure is shown to generate the time windows and how they can be used for the APS systems. The APS heuristic approach that uses the concepts of time windows with constraint programming is introduced to solve problems for which the due date is a strong restriction. These heuristics, with tasks allocation either at the beginning or at the end of the task time window, eliminate the need for a priority scheme. To illustrate the advantage of the proposal, some examples are presented.

Operational extended model formulations for Advanced Planning and Scheduling systems

Since the basic reasoning of Manufacturing Resources Planning (MRPII) systems is flawed, a new breed of concepts called Advanced Planning and Scheduling systems (APS) have recently emerged to overcome the problems occurring on the shop floor. In this study, we develop improved and extended mixed integer programming formulations for APS systems at the factory planning level. First, we develop a basic model which explicitly considers capacity constraints, operation sequences, processing times, and due dates in a multi-machine, multi-order, multi-item environment where an item can be processed on a given set of eligible machines. The extensions to the basic model include sequence dependent setups, and transfer times between machines. We also show that our model with a little modification could be used to quote delivery times for customer orders in case due dates are not specified. We provide numerical examples and our conclusions along with future research directions.

Advanced Planning and Scheduling Systems based on Time Windows and Constraint Programming

APS (Advanced Planning and Scheduling) systems are designed for drawing up production plans by employing task allocation strategies based on simple heuristic tools. The task allocation procedures presented here, allow for a detailed analysis of the production results, as well as for the possibility of setting different production planning scenarios. Task allocation heuristics for APS systems here consider the use of tasks “time windows” and constraint programming to meet deadlines and important dates for scheduling delivery. These heuristics, with tasks allocation either at the beginning or at the end of the task time window, eliminates the need for a priority scheme and represent a contribution to intelligence, flexibility and agile manufacturing. To illustrate the advantage of the proposal, a scheduling for batch production plants is presented. The short-term constraints considered were: restrictions on storage for intermediate products on shared resources (equipment), and a method for on-demand rescheduling.

Evolution Algorithm for Job Shop Scheduling Problem Constrained by the Optimization Timespan

Nowadays, production scheduling is a greatly debated field of operation research due its potential benefits for improving manufacturing performance. Production scheduling, however, despite the increasing use of APS (Advanced Planning and scheduling Systems) and MES (Manufacturing Enterprise Systems) is still underestimated and one frequently encounters more or less intuitive scheduling using excel spread sheets at workshop level, mainly in SME (Small and Medium Enterprises). Some of the main reasons for this are the complexity of related algorithms and the timespan of the optimization manufacturing operation sequence. The complexity of the algorithms usually leads to a number of operators which are difficult to set up for a usual workshop foreman or manufacturing planner. That is why dispatching rules are widely used in comparison with advanced heuristics, such as Evolution Algorithms (EA). Therefore, operation research should not focus only on getting the best values of the objective function by problem based operators, but also on industrial practice requirements such as operator simplicity and a low timespan of the optimization. This article briefly introduces key principles of the scheduling system developed for the Job Shop Scheduling Problem (JSSP) type of manufacturing. An implemented EA with random key representation, clone and incest control and chromosome repair algorithm is briefly explained. Further, the test results of the evolution operator (e.g. crossover and selection) are presented with respect to the value of the objective function and timespan of the optimization. The research goal is to develop a principle of automatic optimization using EA, where the single parameter to set is required optimization timespan.

Advanced planning and scheduling (APS) and enterprise planning system (ERP) planning reference models for Personal Computer assembly industry: A process perspective

Faced with fierce market competition, the life cycle of computer products is shorter than other products which caused the supply and demand changing quickly. If the PC assembly industries still adopted ERP for material planning, it cannot response to customer’s demands swiftly. Therefore, it is necessary to adopt the advanced planning and scheduling (APS) system. The goal of APS is to solve a complicated supply chain planning and scheduling question. This study discussed the related issues of APS system implementation for Personal Computer (PC) assembly industry and developed an implementation process of APS system. The research applied ARIS’s EPC model to establish the reference planning models of APS and ERP from the process perspective. Finally, we proposed the integration of APS and ERP reference model to information system implementation for the PC assembly industry. Key words: Advanced planning scheduling system (APS), enterprise planning system (ERP), architecture of integrated information system (ARIS), event-driven process chain (EPC).

A two-stage sequential planning scheme for integrated operations planning and scheduling system using MILP: the case of an engine assembler

This paper presents an operations planning scheme based on mathematical programming models (specifically, Mixed-Integer Linear Programming (MILP) models) integrated into a web-enabled Advanced Planning and Scheduling System (APS), developed for and implemented in an engine assembler that supplies the car industry. One objective of this paper is to provide empirical insights into some operations planning characteristics in the automotive industry. The other main objective is to show MILP models and their use to create plans that enable the coordination of different planning levels (mid-term and short-term) and planning domains (procurement, production and distribution). The APS fulfills the requirements of an engine assembler in the automotive sector (namely lean-type constraints and objectives). The system is based on two MILP models, which have been purposely developed together along with their relations. The models presented herein provide a solution that considers supply chain objectives and constraints, and are integrated by means of data and constraints which have proven sufficient to fulfill users’ and stakeholders’ requirements. This case study presents the models’ most relevant aspects and their implementation.

Shop floor characteristics influencing the use of advanced planning and scheduling systems

The purpose of this article is to investigate how the manufacturing process, the shop type and the data quality, i.e. the shop floor characteristics, influence the use of advanced planning and scheduling (APS) systems in production activity and control (PAC). The methodology implemented is a multiple case study at three case companies. Each company has different shop floor characteristics, but all use a scheduling module in an APS system, which supports production scheduling. A theoretical framework is developed suggesting how APS system are used in the PAC activities, and which major aspect to consider. The case analysis shows that the scheduling module in APS system, foremost supports sequencing and dispatching. In particular, the shop type is influenced by the decision of how often the APS runs and what freedom is given to the shop floor. The manufacturing process influences how the dispatch list is created. Contrary to the literature presuming that APS systems are most suitable in job shop processes, it is found that the manufacturing process is not a crucial factor when deciding whether APS systems are an appropriate investment. It is found that the level of data quality needed in the APS system depends to a large extent on how the dispatch list is used. For example, is the dispatch list used as a guideline, not a regulation, the need for accurate data in the module is reduced. This article extends the previous literature concerning APS systems by analysing how APS systems influence PAC as a whole and increase the understanding of the challenges of using APS systems in PAC.

Problems in the onward and upward phase of APS system implementation

Purpose – Studies conducted on advanced planning and scheduling (APS) systems have found problems in the marginal or negative returns from APS systems when they are implemented in manufacturing planning and control processes. The purpose of this study is to examine what problems exist in the onward and upward phase of the APS system implementation and how the individual, technical and organizational (ITO) dimensions in the implementation phases influence the problems in the onward and upward phase. Design/methodology/approach – Three different manufacturing companies using a supply chain planning module to support their tactical manufacturing planning processes were chosen and their APS system implementation phases were studied. Interviews with the project members and the end-users, and on-site visits, were conducted. Internal company data and presentations were collected and analyzed according to four implementation phases and the ITO dimensions. Findings – Three types of problems were identified in the onward and upward phase: process-related problems concerning difficulties to move forward; dependency on a consultancy firm; and too much time spent in the system. System-related problems include the usage of parallel systems and inadequate usage of the appropriate potential of the APS system. Plan-related problems regard an incorrect production plan. Different relationships between the ITO dimensions in the implementation process and the problem type were proposed. Practical implications – The relationships identified in this paper are of important knowledge for companies who are implementing, or are in the process of implementing, APS systems. Originality/value – There has been little written about the implementation issues of APS systems. The practical use of APS systems in the tactical planning is also relatively low. It is not known what problems to expect and how the ITO dimensions influence the problems during implementation. The findings this paper discusses fill some of these gaps.