A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

A Case for Cooperative and Incentive-Based Coupling of Distributed Clusters

Research interest in Grid computing has grown significantly over the past five years. Management of distributed resources is one of the key issues in Grid computing. Central to management of resources is the effectiveness of resource allocation as it determines the overall utility of the system. The current approaches to superscheduling in a grid environment are non-coordinated since application level schedulers or brokers make scheduling decisions independently of the others in the system. Clearly, this can exacerbate the load sharing and utilization problems of distributed resources due to suboptimal schedules that are likely to occur. To overcome these limitations, we propose a mechanism for coordinated sharing of distributed clusters based on computational economy. The resulting environment, called \emph{Grid-Federation}, allows the transparent use of resources from the federation when local resources are insufficient to meet its users' requirements. The use of computational economy methodology in coordinating resource allocation not only facilitates the QoS based scheduling, but also enhances utility delivered by resources.Comment: 22 pages, extended version of the conference paper published at IEEE Cluster'05, Boston, M

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Smart manufacturing scheduling: A literature review

[EN] Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart manufacturing scheduling: A literature review. Journal of Manufacturing Systems. 61:265-287. https://doi.org/10.1016/j.jmsy.2021.09.0112652876

An expert system for a local planning environment

In this paper, we discuss the design of an Expert System (ES) that supports decision making in a Local Planning System (LPS) environment. The LPS provides the link between a high level factory planning system (rough cut capacity planning and material coordination) and the actual execution of jobs on the shopfloor, by specifying a detailed workplan. It is divided in two hierarchical layers: planning and scheduling. At each level, a set of different algorithms and heuristics is available to anticipate different situations.\ud \ud The Expert System (which is a part of the LPS) supports decision making at each of the two LPS layers by evaluating the planning and scheduling conditions and, based on this evaluation, advising the use of a specific algorithm and evaluating the results of using the proposed algorithm.\ud \ud The Expert System is rule-based while knowledge (structure) and data are separated (which makes the ES more flexible in terms of fine-tuning and adding new knowledge). Knowledge is furthermore separated in algorithmic knowledge and company specific knowledge. In this paper we discuss backgrounds of the expert system in more detail. An evaluation of the Expert system is also presented

Scheduling in the industry 4.0: a systematic literature review

Industry 4.0 is characterised for being a new way of organising the supply chains, coordinating smart factories that should be capable of a higher adaptivity, making them more responsive to a continuously changing demand. This paper presents a Systematic Literature Review (SLR) with three main objectives. First, to identify in the literature on Industry 4.0, the need for new job scheduling methods for the factories of the digital era. Second, to identify in the literature of scheduling, which of these issues have been accomplished and what are the most critical gaps. Third, to propose a new research agenda on scheduling methodology, that fulfils the needs of scheduling in the field of Industry 4.0. The results show that literature related to the subject of study is rapidly growing and the needs of new methods for job scheduling in the digital factories concern two main ideas. First, the need to create and implement a digital architecture where data can be appropriately processed and second, the need of giving a decentralised machine scheduling solution inside such a framework. Although we can find some studies on small production lines, research with practical results remains scarce in the literature to date

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Agent-based Three Layer Framework of Assembly-Oriented Planning and Scheduling for Discrete Manufacturing Enterprises

To solve the cost burden caused by delivery tardiness for small and medium sized packaging machinery enterprises, the assembly-oriented planning and scheduling is studied based on the multi-agent technology. Taking into account the due date, the planning and scheduling are optimized iteratively with the rule-based algorithms complying with the machining and assembling process constraints. To make the realistic large-scale production planning scheme tailored to fit the optimization algorithms, a multi-agent system is utilized to conceptually construct a three-layer framework corresponding to three planning horizons of different task level. These planning horizons are quarter/month of product/subassembly level, week of part level, and day of operation level. With the strategy of combining top-down task decomposition and bottom-up plan update (where the quarterly orders are decomposed into the monthly, weekly, and daily tasks according to the product processing structure while the resulting plans of every layer are updated iteratively based on the bottom layer optimization), the proposed framework not only addresses the planning but also the periodic and event-driven scheduling of the processes. In this paper, a gravure printing machine is considered as a test case for evaluating the proposed framework. The simulation results with the historical data of the orders for this machine demonstrate the effectiveness of the proposed scheme on the control of the distribution of idle-time. It can also provide a resolution to tardiness penalty for small and medium sized enterprises