Activity Based Costing for the better Supply Chain Management: An Integrated Approach for the Business Performance

In the present business environment with the higher level of competition at world glance, supply chain management (SCM) helps the business firms to progressively improve their performance. For this purpose, the integration of cost management with the profitability is very much significant. For the proper SCM in the business, cost data with more accuracy and efficiency related to all the activities is much needed. For the better business performance, activity-based costing or ABC approach has significantly contributed towards SCM. In this context, the present study has been conducted to address the relationship between ABC and SCM from the context of various Indonesian business firms. For this purpose, various improvements which are offered by ABC to SCM for the business performance are examined. By using the questionnaire approach, the current study is conducted regarding the SCM improvement with the organizational performance with the adoption of ABC. It is found that for the increasing performance through SCM, the adoption of ABC is much needed in the business firms

A human centred hybrid MAS and meta-heuristics based system for simultaneously supporting scheduling and plant layout adjustment

Manufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurance of unexpected events.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Anarchic manufacturing: implementing fully distributed control and planning in assembly

This paper demonstrates that a distributed control and planning system can fulfil an idealised mixed-model assembly problem and compete with traditional systems. The anarchic manufacturing system is a distributed planning and control system, based on a free market structure, where system elements have decision-making authority and autonomy. Mixed-model assembly is typically managed centrally for production planning and control, using simplification and hierarchical structures to manage complexity. In developing anarchy, inter-job cooperation is implemented to synergise jobs together and fulfil global objectives efficiently. The anarchic system maximises available flexibility, through embracing complexity, and reduces myopic decision making by maximising an agent’s lifetime profitability. Through agent-based simulation experiments, the anarchic system is compared to fixed and flexible centralised systems. The proposed system outperforms traditional systems when the scenario’s structural flexibility allows agile and delayed dynamic decision making. Additionally, the anarchic system managed dynamic bottleneck disruptions as effectively as flexible centralised systems

Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective

[EN] Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling (SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and technologies that model SMS. This research can provide guidance for practitioners and researchers¿ efforts to move toward the digital transformation of job shops.The research leading to these results received funding from the European Union H2020 Programme ,Belgium with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP) ", No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q) " and 872548 "Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS) ", from Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe" and the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana entitled "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065).Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2022). Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective. Journal of Manufacturing Systems. 63:185-202. https://doi.org/10.1016/j.jmsy.2022.03.0111852026

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents. Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical. The proposed architecture has been tested to illustrate its feasibility and practicality

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of methodology and programmed in agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of Prometheus TM methodology and programmed in JACK TM agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms. © 2015, Springer Science+Business Media New York