Design of a suitable production management system for a manufacturing company

This paper reports on the design of a suitable production management system (PMS) for a manufacturing company, located in Western Australia. The company was experiencing problems in scheduling and plant layout leading to further problems in materials flow, labour control, inventory and purchasing, material handling system, and production space.

Group technology (GT) was used to design a new plant layout. A G.T. algorithm was developed to minimise machine duplications. A Kanban system was designed to assist execution of scheduling based on a pull system. The design was tested by simulation using actual data collected on existing operations.     

The relationship between work organization and workforce agility in small manufacturing enterprises

Organizational agility requires development of an adaptable workforce that is able to deal with unexpected and dynamic changes in the business environment. However, little research has been done on the attributes and characteristics of workforce agility. Even less is known about what organization characteristics are conducive to the agile performance of employees. The main goal of this study was to explore the effect of agile strategies on work organization and employees' performance. A total of 41 managers, 82 office workers, and 52 production workers from six small manufacturing companies participated in the study. Three questionnaires, including Agility Strategy Scale, Work Organization Scale, and Workforce Agility Scale, were used for the purpose of data collection. The results support a hypothesized relationship between management strategies focused on agility development, work characteristics, and workforce agility. The results also reveal that autonomy at work is one of the most important predictors of workforce agility. A combination of job demands and job uncertainty had a significant effect on workforce agility. The study also suggests that developing strong cooperative relationships within an organization and with customers and suppliers promotes workforce agility in small manufacturing enterprises.     

Toward developing agility evaluation of mass customization systems using 2-tuple linguistic computing

Mass customization (MC) relates to the ability to provide individually designed products and services to every customer through high process flexibility and integration. For responding to the mass customization trend it is necessary to develop an agility-based manufacturing system to catch on the traits involved in MC. An MC manufacturing agility evaluation approach based on concepts of TOPSIS is proposed through analyzing the agility of organization management, product design, processing manufacture, partnership formation capability and integration of information system. The 2-tuple fuzzy linguistic computing manner to transform the heterogeneous information assessed by multiple experts into an identical decision domain is inherent in the proposed method. It is expected to aggregate experts’ heterogeneous information, and offer sufficient and conclusive information for evaluating the agile manufacturing alternatives. And then a suitable agile system for implementing MC can be established.     

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

Approaches to uncertainties in facility layout problems: Perspectives at the beginning of the 21<Superscript>st</Superscript> Century

Production uncertainty is one of the most challenging aspects in manufacturing environments in the 21st century. The next generation of intelligent manufacturing is dynamically depending on the production requirements, and success in designing agile facilities is closely related to what extent these requirements are satisfied. This paper presents the most recent advancements in designing robust and flexible facilities under uncertainty. The focus is on exploring the way uncertainty is incorporated in facility design, namely dynamic and stochastic facility layout problems. Recent approaches are explored and categorized in detail, and previous approaches are briefly reviewed in the related categories. Furthermore, research avenues warranting exploration in the emerging field of facility design are also discussed.     

Designing an assembly line for modular products

To respond to the challenge of agile manufacturing, companies are striving to provide a large variety of products at low cost. Product modularity has become an important issue. It allows to produce different products through combination of standard components. One of the characteristics of modular products is that they share the same assembly structure for many assembly operations. The special structure of modular products provides challenges and opportunities for operational design of assembly lines. In this paper, an approach for design of assembly lines for modular products is proposed. This approach divides the assembly line into two subassembly lines: a subassembly line for basic assembly operations and a subassembly line for variant assembly operations. The design of the subassembly line for basic operations can be viewed as a single product assembly line balancing problem and be solved by existing line balancing methods. The subassembly line for the variant operations is designed as a two-station flowshop line and is balanced by a two-machine flowshop scheduling method. A three-station flowshop line for a special structure of modular products is proposed and illustrated with an example.     

A particle swarm optimization algorithm for the multiple-level warehouse layout design problem

Warehouse operation and management is one of the essential parts of manufacturing and service operations. The warehouse layout problem is a key to warehouse operations. Generally, warehouse layout design models attempt to optimize different objectives such as the orientation of storage racks, the allocation of space among competing uses, the number of cranes, the overall configuration of the facility, etc. The warehousing strategies can be classified as distribution-type, production-type and contract-type warehouse strategies. In this study, a distribution-type warehouse considered that various type products are collected from different suppliers for storing in the warehouse for a determined period and for delivery to different customers. The aim of the study is to design a multiple-level warehouse shelf configuration which minimizes the annual carrying costs. The turnover rates of the products are classified and they are considered while putting/picking them to/from shelves regarding the distances between the shelves and docks. Since proposed mathematical model was shown to be NP-hard, a particle swarm optimization algorithm (PSO) as a novel heuristic was developed for determining the optimal layout.     

A multi-agent based agile manufacturing planning and control system

In today’s manufacturing enterprise, the performance of customer service level (e.g., short ordering-to-delivery time, low price) is highly dependent on the effectiveness of its manufacturing planning and control system (MPCS). However, most of the current MPCS, employed the hierarchical planning approach, may have some drawbacks, such as structural rigidity, difficulty of designing a control system, and lack of flexibility. Currently, RFID (Radio Frequency Identification) technology has been applied to enhance the visibility, accountability, track ability and traceability of manufacturing system whenever the accurate and detailed manufacturing information (e.g., raw material, WIP, products in factory and products in the down streams) of products will be followed in real-time basis by RFID technique. In addition, a multi-agent approach may be applied in a distributed and autonomous system which allows negotiation-based decision making. Therefore, the objective of this research is to study the application of RFID technique and multi-agent system (MAS) in developing an agent-based agile manufacturing planning and control system (AMPCS) to respond to the dynamically changing manufacturing activities and exceptions.In AMPCS, RFID-based manufacturing control (R-MC) module plays the role of controlling the manufacturing system in which production items and manufacturing resources attached with RFID tag may actively feedback production status to and receive production and operations schedule from advanced manufacturing planning (AMP) module. In addition, a bidding process and algorithm is developed to generate operations schedule by using the characteristics of MAS. Performance analysis (PA) module is responsible not only for evaluating the scheduling results but also for evaluating the performance of production execution. The development of an AMPCS for an automated manufacturing cell demonstrates that the integration of RFID technique and MAS in developing an agile manufacturing planning and control system can really possess the characteristics of visibility, accountability, track ability, responsiveness, and flexibility in a distributed and dynamic manufacturing system.     

DimensionSlice: A main-memory data layout for fast scans of multidimensional data

Multidimensional data are exploited in many application areas such as scientific data analysis, business intelligence, and geographic information systems. One of the most frequent operations applied to such multidimensional data is the selection of a subspace of the given multidimensional space, which involves predicate evaluation on multiple dimensions. Existing main-memory data layouts optimized for evaluating predicates on the columnar data can be used to accelerate the subspace extraction by sequentially performing filter scans on each dimension one at a time. However, optimization opportunities emerge if we can consider all predicates together. In this paper, we propose DimensionSlice, a new main-memory data layout optimized for evaluating predicates on multiple dimensions. More specifically, the dimension values are sliced into portions and the portions with the same order of each dimension are arranged together. Multiple predicates are simultaneously evaluated with the sliced dimension values during the scan. In addition, by storing the different portions separately, unnecessary loads and computations of lower portions can be eliminated if the evaluation results are assured after examining the upper portions. For further acceleration of scans, the DimensionSlice layout is designed to easily leverage the SIMD capabilities that most mainstream processors are equipped with. Through experiments, we demonstrate the performance gains of the proposed method over the columnar main-memory layout that evaluates the partial predicates one dimension at a time. We also show that the proposed method outperforms the state-of-the-art multidimensional index structure when the selectivity is over a very low threshold.     

Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic,data envelopment analysis and machine learning

The facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three-stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta-heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.     

A model for assessing the layout structural complexity of manufacturing systems

The layout of a manufacturing facility/system not only shapes its material flow pattern and influence transportation and operation cost, but also affects logistics and parts/machine assignment decisions. The layout of manufacturing systems determines its structural complexity by virtue of its design configuration characteristics. This paper introduces a new model and indices for assessing the structural complexity of manufacturing systems layout in the physical domain. Six complexity indices, based on the physical structural characteristics of the layout, have been introduced and formulated. They are layout density, path, cycle, decision points, redundancy distribution and magnitude indices. An overall Layout Complexity Index (LCI) which combines all indices is developed using a novel method based on radar plots which is insensitive to the order of plotting the individual indices. The use of the developed LCI is demonstrated using six typical types of manufacturing systems layouts and relevant guidelines are presented. The developed model and complexity indices help design system layouts for least complexity and compare layout alternatives that meet the specifications, at early design stages. It supports making trade-off decisions regarding manufacturing systems flexibility and complexity and their associated costs.     

Holon技术在敏捷制造系统中的应用研究

面对日益多样化的客户需求和市场能够快速响应的生产状况，制造业领域的生产模式也朝着客户化、敏捷化、柔性化的方向发展，敏捷制造模式有望成为21世纪的发展趋势。讨论了将全能体Holon作为实施敏捷制造的现实技术问题，分析比较了目前国内外在此领域的研究现状。通过简单归纳Holon的定义，并对Holon的内部逻辑结构加以分析说明。提出了基于Holon的面向订单生产敏捷制造企业参考模型。     

Design of robust layout for Dynamic Plant Layout Problems

In this paper, a design for robust facility layout is proposed under the dynamic demand environment. The general strategy for a multi period layout planning problem is adaptive approach. This approach for Dynamic Plant Layout Problem (DPLP) assumes that a layout will accommodate changes from time to time with low rearrangement and production interruption costs, and that the machines can be easily relocated. On the other hand the robust layout approach, assumes that rearrangement and production interruption costs are too high and hence, tries to minimize the total material handling costs in all periods using a single layout. Robust approach suggests a single layout for multiple scenarios as well as for multiple periods. As a solution procedure for the proposed model, a Simulated Annealing (SA) algorithm is suggested, which perform well for the problems from literature and QAPLIB website. The application of suggested model for robust layout to cellular layouts has given better results compared to the robust cellular layout model of literature. For the standard DPLP of the literature, the solution values of the suggested model are very near to the results of adaptive approach. The Total Penalty Cost (TPC) is used to test the suitability of the suggested layout to be a robust layout for the given data set. TPC values indicate that the suggested layout is suitable as robust layout for the given data sets.     

Agile Manufacturing and Management of Variability

In this study, the relationship between agile manufacturing and management of variability is discussed. First, the key concepts of agility and agile manufacturing are introduced. These key concepts are then related to the management of variability. Previous studies on the modeling of variability of manufacturing systems are reviewed. Then the effects of variability of the structure of the production system, and also the processing time variability on the performance of a manufacturing system are studied by utilizing a simple model of a synchronous production line with identical stations and random processing times. Numerical results that examine the effects of the coefficient of variation of service time and the number of stations on the variability of the cycle time are also given.     

Synchronization-oriented reconfiguration of FPAI under graduation intelligent manufacturing system in the COVID-19 pandemic and beyond

Companies with manufacturing systems that are more responsive and resilient will be able to survive or even gain market shares in the face of the unpredicted variable of an outbreak similar to the COVID-19 pandemic. Motivated by an industrial company restructuring its manufacturing system with the layout of fixed-position assembly islands (FPAI) during the COVID-19 pandemic, this paper introduces the synchronization-oriented reconfiguration of FPAI under Graduation Intelligent Manufacturing System (GiMS). Inspired by the graduation ceremony, a novel manufacturing mode-Graduation Manufacturing System (GMS) with ticket-based reconfigurable structures, is designed for organizing production operations with simplicity and resilience for the layout of FPAI. The IIoT and digital twin-enabled GiMS is developed for transforming real-time visibility in operations to support the reconfiguration of the manufacturing system. A synchronization-oriented reconfiguration mechanism is proposed to achieve the synchronous interaction among changing customer demand, island configuration, and production activities allocation rapidly and cost-effectively. Cloud services integrating the proposed reconfiguration mechanism are developed for managers and onsite operators for supporting the successful reconfiguration implementation with enhanced operational visibility. Through the case study of an industrial company, the effectiveness of the proposed concept and approach is verified.     

面向结构化布局设计的产品布局多色模型

为实现产品原理方案向结构化布局映射,提出产品布局元层次关系网及产品布局多色模型.首先在布局模块及其属性关系网的基础上构建布局元层次关系网;然后应用多色集合的个人颜色、统一颜色,给出产品布局元层次关系网的数学描述,形成产品布局多色模型,并提出推理算法;再利用个人颜色、统一颜色之间的围道布尔矩阵推理原理,进行产品结构布局设计过程中功能—运动分配—布局模块之间映射过程的形式化描述.应用该模型进行结构化布局设计,可有效地支持产品结构布局设计的公理化;最后在Pro/E平台中利用VC.net 2003工具进行二次开发,并以数控机床结构布局设计为例验证了文中模型和设计方法的可行性和有效性.     

Multiple-attribute decision making methods for plant layout design problem

The layout design problem is a strategic issue and has a significant impact on the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision. The present study explores the use of MADM approaches in solving a layout design problem. The proposed methodology is illustrated through a practical application from an IC packaging company. Two methods are proposed in solving the case study problem: Technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. Empirical results showed that the proposed methods are viable approaches in solving a layout design problem. TOPSIS is a viable approach for the case study problem and is suitable for precise value performance ratings. When the performance ratings are vague and imprecise, the fuzzy TOPSIS is a preferred solution method.     

制造系统中的单向环型设备布局设计

提出一种优化建模与虚拟现实技术相结合的求解策略，较好地解决了制造系统中的单向环型设备布局问题．研究该问题的固有特性，提出三条定理，构建了一个启发式算法，并实现了一个沉浸式虚拟布局设计的例子．     

Lessons learned for building agile and flexible scheduling tool for turbulent environments in the extended enterprise

This paper presents results of a 2.5-year, multidisciplinary, university–industry collaborative effort investigating design of “Internet-based Scheduler for material Optimisation and agile ProducTion In MUlti-Site enterprises in agile manufacturing” (IS-OPTIMUS), a four nation collaborative project aimed at improvements in turbulent manufacturing environments. The focus of this paper is specifically on the content of the work carried out, along with the main benefits and results. Key to achieving the goals is following a complete project-life-cycle path from the initial stages where the industrial users requirements were identified and the system specification took place to the development and tuning of the final system. Design choices for software must strike a balance between the user flexibility required and the representing environment constraints, i.e. finite capacity scheduling, which takes production requirements from existing production planning systems, to schedule production resources like plant workers, critical tools and machines. The system consists of a material optimiser working closely with the finite capacity scheduler and a dynamic scheduler providing automatic reaction to real-time exceptions, thus deriving in solutions of higher performance.     

A multi-agent system using iterative bidding mechanism to enhance manufacturing agility

The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed.