Developing an Agent Based Heuristic Optimisation System for Complex Flow Shops with Customer-Imposed Production Disruptions

The study of complex manufacturing flow-shops has seen a number of approaches and frameworks proposed to tackle various production-associated problems. However, unpredictable disruptions, such as change in sequence of order, order cancellation and change in production delivery due time, imposed by customers on flow-shops that impact production processes and inventory control call for a more adaptive approach capable of responding to these changes. In this research work, a new adaptive framework and agent-based heuristic optimization system was developed to investigate the disruption consequences and recovery strategy. A case study using an Original Equipment Manufacturer (OEM) production process of automotive parts and components was adopted to justify the proposed system. The results of the experiment revealed significant improvement in terms of total number of late orders, order delivery time, number of setups and resources utilization, which provide useful information for manufacturer’s decision-making policies.

Dynamic agent-based bi-objective robustness for tardiness and energy in a dynamic flexible job shop

Nowadays, manufacturing systems are shifting rapidly with the significant change in technology, business, and industry to become more complex and involved in more difficult issues, customised products, variant services and products, unavailable machines, and rush jobs. In the current practices, there are limited models or approaches that are dealing with these complexities. Most of the scheduling models in literature are proposed as centralised approaches. Researchers recently started to pay attention to reduce energy consumption in manufacturing due to the rising cost and the environmental impact. The energy consumption factor has been lately introduced into scheduling research among other traditional objectives such as time, cost and quality. Although reducing energy in manufacturing systems is very important, few researchers have considered energy consumption factor into scheduling in dynamic flexible manufacturing systems. This paper proposes an agent-based dynamic bio-objective robustness for energy and time in a job shop. Two types of agent are introduced which are machine agent and product agent. A new decision making and negotiation model for multi-agent systems is developed. Two types of dynamic unexpected events in the shop floor are introduced: dynamic job arrival and machines breakdown. A case study is provided in order to verify the result

Design and Planning of Manufacturing Networks for Mass Customisation and Personalisation: Challenges and Outlook

AbstractManufacturers and service providers are called to design, plan and operate globalized manufacturing networks, addressing to challenges such as ever-decreasing lifecycles and increased product complexity. These factors, caused primarily by mass customisation and demand volatility, generate a number of issues related to the design and planning of manufacturing systems and networks, which are not holistically tackled in industrial and academic practices. The mapping of production performance requirements to process and production planning requires automated closed-loop control systems, which current systems fail to deliver. Technology-based business approaches are an enabler for increased enterprise performance. Towards that end, the issues discussed in this paper focus on challenges in the design and planning of manufacturing networks in a mass customization and personalization landscape. The development of methods and tools for supporting the dynamic configuration and optimal routing of manufacturing networks and facilities under cost, time, complexity and environmental constraints to support product-service personalization are promoted

Literature survey about elements of manufacturing shop floor operation key performance indicators

In the era of globalisation, manufacturing industries are compelled to continuously monitor their manufacturing operations to maintain competitiveness. As a result, manufacturers have integrated several measurement models to inspect their manufacturing operations. These models comprise of a set of Key Performance Indicators (KPIs), which are capable to enumerate the effectiveness, competence, efficiency and proficiency of manufacturing operations. This paper presents a review of manufacturing shop floor operation KPIs that has been studied in the recent literature. Based on the reviewed literature author proposes various KPI elements such as: description, category, scope, formula, unit of measure, range, trend, mode of display, viewers and manufacturing approach. These elements can help manufacturers to better describe, classify, analyze and measure the appropriate KPIs for their shop floor operations. Thus, enabling manufacturers to accomplish and uphold great quality, increased productivity and throughput

Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems

Due to increasing demand for unique products, large variety in product portfolios and the associated rise in individualization, the efficient use of resources in traditional line production dwindles. One answer to these new challenges is the application of matrix-shaped layouts with multiple production cells, called Matrix Production Systems. The cycle time independence and redundancy of production cell capabilities within a Matrix Production System enable individual production paths per job for Flexible Mass Customisation. However, the increased degrees of freedom strengthen the need for reliable production control systems compared to traditional production systems such as line production. Beyond reliability a need for intelligent production within a smart factory in order to ensure goal-oriented production control under ever-changing manufacturing conditions can be ascertained. Learning-based methods can leverage condition-based reactions for goal-oriented production control. While centralized control performs well in single-objective situations, it is hard to achieve contradictory targets for individual products or resources. Hence, in order to master these challenges, a production control concept based on a decentralized multi-agent bidding system is presented. In this price-based model, individual production agents - jobs, production cells and transport system - interact based on an economic model and attempt to maximize monetary revenues. Evaluating the application of learning and priority-based control policies shows that decentralized multi-agent production control can outperform traditional approaches for certain control objectives. The introduction of decentralized multi-agent reinforcement learning systems is a starting point for further research in this area of intelligent production control within smart manufacturing

Revolution of Production System for the Industry 4.0

Nowadays, good coordination of production and logistics at a production operational level is required to handle rapidly evolving technology, frequently changing customer demand and satisfaction, and remain competitive. Accelerated by exponentially growing technologies in information and communication technology, production industries are in the throes of a digital transformation, which is referred to as the fourth industrial revolution or Industry 4.0. The shorter product life cycles due to market-demand variables and volatile developments in the production system have forced manufacturing company to work flexibly in order to adapt to changing customer needs. These environments cannot be managed through traditional production systems such as job shops and dedicated production lines. Reconfigurable manufacturing system, which combines the versatility and capability to re-configure of job shops and the dedicated production lines, has been seen as a potential solution in such situations. As the main component of production systems, a new concept of material handling, a reconfigurable conveyor system is introduced

Multi-objective shop floor scheduling using monitored energy data

Modern factories will become more and more directly connected to intermittent energy sources like solar systems or wind turbines as part of a smart grid or a self-sufficient supply. However, solar systems or wind turbines are not able to provide a continuous energy supply over a certain time period. In order to enable an effective use of these intermittent energy sources without using temporary energy storages, it is necessary to rapidly and flexibly adapt the energy demand of the factory to the constantly changing requirements of the energy supply. The adaption of the energy demand to the intermittent supply results in different energy-related objectives for the production system of the factory, such as reducing energy consumption, avoiding power peaks, or achieving a power use within the available power supply. Shop Floor Scheduling can help to pursue these objectives within the production system. For this purpose, a solution methodology based on a meta-heuristic will be described for Flexible Job Shop Scheduling taking into account different energy- as well as productivity-related objectives

Multi-objective shop floor scheduling using monitored energy data

Modern factories will become more and more directly connected to intermittent energy sources like solar systems or wind turbines as part of a smart grid or a self-sufficient supply. However, solar systems or wind turbines are not able to provide a continuous energy supply over a certain time period. In order to enable an effective use of these intermittent energy sources without using temporary energy storages, it is necessary to rapidly and flexibly adapt the energy demand of the factory to the constantly changing requirements of the energy supply. The adaption of the energy demand to the intermittent supply results in different energy-related objectives for the production system of the factory, such as reducing energy consumption, avoiding power peaks, or achieving a power use within the available power supply. Shop Floor Scheduling can help to pursue these objectives within the production system. For this purpose, a solution methodology based on a meta-heuristic will be described for Flexible Job Shop Scheduling taking into account different energy- as well as productivity-related objectives

Scheduling With Alternatives Machine Using Fuzzy Inference System And Genetic Algorithm.

As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing company to have a good shop floor production scheduling to plan and schedule their production orders. Industri pengeluarcim kini telah berkembang pesat dan aktiviti pengeluarannya semakin kompleks, dengan itu syarikat pengeluar memerlukan jadual lantai pengeluaran (shop floor) yang terbaik untuk merancang permintaan pengeluaran (product)

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches