Full length ArticleMulti-phase integrated scheduling of hybrid tasks in cloud manufacturing environment
Introduction
Cloud Manufacturing (CMfg) is a paradigm that uses Internet-of-Things and cloud computing technology to integrate distributed manufacturing resources as services [1], [2]. Customers can post their requirement and find suitable services for manufacturing products they required without purchasing additional equipment. It has made customized production easier and state-of-the-art machining technologies accessible for particularly middle and small-sized enterprise [3].
Since many manufacturing resources are organized in a production line with complex control programs, suppliers only provide simplified interface information of these resources [4]. Without an effective communication and pre-established trust between customer and suppliers, a production order, which contains a series of processing tasks, is more likely to be executed with low-quality or be delayed in delivery [5]. This phenomenon becomes more serious when hybrid production tasks from multiple orders are submitted to the platform. Therefore, CMfg platform is in charge of finding suitable schedule to execute these tasks in multiple production lines [6].
The above process is normally divided into three phases, demand negotiation, supplier selection, and resource allocation [7], [8], [9], [10], [11], [12], to assign the priority of different customer orders, find suitable suppliers, and allocate manufacturing resources with proper size to carry out the tasks. It is assumed that production orders in CMfg platform are handled one by one. The production process for each order is pre-determined without change. Each task of an order can find independent resources without preemption. If a production task is assigned to a group of resources, it does not need to queue for execution. But in fact, this is not the case.
Enterprise conducts their own resource pool and shares it to a public cloud platform. The resources can be either managed independently, or organized in a product line. Thus, a CMfg platform is established as a multi-centered resource pool in which each center is maintained by an enterprise [13]. Customer has a lot of options on the production process of a customized product. In this situation, a production order is firstly decomposed into multiple tasks. These tasks are allocated to a group of manufacturing suppliers. Resource preemption inside an enterprise and load imbalance among multiple centers become two common phenomena that hinder the efficiency and quality of distributed production when limited number of resources are shared for multiple orders [14].
Though existing researches of supplier selection, service composition and production line scheduling are carried out considering the characteristics of CMfg, there is still large cooperation barriers to be reduced between customer and enterprise. In this case, recent researchers put more emphasis on the hierarchical structure of CMfg. Some of them built hierarchical resource allocation models in CMfg environment with individual users, cloud manager, and enterprise providers [15], [16]. They demonstrated the interconnections among users, cloud manager, and enterprise providers in general production process. Researchers summarized the characteristic of different production and categorized them into order allocation, supplier selection, service composition, production line scheduling and so on [17], [18], [19], [20]. However, the interactions between different production phases and the resource constraints in distributed production lines have not been considered in the above research. If the CMfg platform selects the best suited supplier and process for each task, it can cause serious resources preemption and even production delay in the underlying production lines. On the contrary, better production line scheduling strategies would be hard to satisfy high production quality for each order simultaneously.
Motivated by the above discussion, a three-phase integrated scheduling model for CMfg is established in this paper. The three phases combine order priority assignment, supplier and process selection, and resource scheduling. In this way, the whole execution scheme for multiple production orders is optimized so as to maximize the benefit of manufacturing enterprises and increase customer satisfaction. After that, six typical multi-objective evolutionary algorithms (MOEAs) are used to solve the model in different phases. Through numerical analysis on the CMfg instances, one can observe that the integration of three-phase optimization for hybrid manufacturing orders is feasible and productive. The result and discussion drawn from this paper also show the performance of the six MOEAs for different scheduling requirements.
Section snippets
Related work
Since CMfg was introduced in 2010, more and more researchers and engineers contribute to the establishment of CMfg platform for manufacturing resource sharing. Xu et al. [21] outlined that CMfg mainly consists of three layers, user domain, enterprise domain, and provider domain. Based on these three layers, Liu et al. [22] summarized the life-cycle of CMfg service as resource encapsulation, resource perception and connection, resource provisioning and task execution.
To optimize the resource
The multi-phase integrated scheduling model
In this section, an integrated scheduling model for determining priorities, suppliers, processes, and production schedules of hybrid customer orders is established. The optimization process is originally divided into three decision phases, order priority assignment, supplier and production process selection, and production line scheduling. The definition and preliminaries of this integrated problem are provided. The interrelationship between every two phases is analyzed mathematically. The
Multi-objective evolutionary algorithm for multi-phase integrated scheduling
Since the inter-relationship among the three scheduling phases are embodied in different objectives, the multi-phase integrated scheduling model is a multi-objective optimization problem. In this study, multi-objective evolutionary algorithms (MOEAs) are introduced to solving the integrated problem. MOEA is developed to balance the trade-off among different objectives and find non-dominated solutions through various solution selection strategies. Three key problem-specific issues for solving
Experimental results and discussions
Comparative experiments were conducted on our integrated scheduling model and the typical scheduling model. In a typical scheduling model, different optimization phases are determined separately. The upper phases make optimization decisions for the overall planning based on the average situation of the underlying production procedure, rather than accurate scheduling. By comparing the scheduling of the integrated and separated models, we would like to verify the practicality of the integrated
Conclusion
Considering the cooperative barriers between enterprises and customer requirements in the CMfg environment, this paper studied a multi-phase integrated scheduling problem that combines order priority assignment, supplier and production process selection, and production line scheduling. Multiple production indicators of the manufacturing process were evaluated and optimized to measure the scheduling scheme more comprehensively. To better address the complex hybrid scheduling problems, six of
Declaration of Competing Interest
None.
Acknowledgments
This paper was supported by National Key R&D Program of China (No. 2018YFB1003703) and partially supported by the Natural Science Foundation of China (Grant no. 61703015) .
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