In the near future, manufacturing industries will be mostly recognised with characteristics like IoT, and massive data transactions. To fulfil these characteristics, paradigms like Cloud manufacturing, Industry 4.0 and smart factory have passed their preliminary steps to become the primary inspirations. Considering the nature of Cloud manufacturing which consists of a vast number of service providers and Service demanders being introduced to the manufacturing cloud, service composition problem is introduced. However, there is a big challenge for fulfilling the dynamic behaviour of parameters which change rapidly over time in the service composition problem. This paper challenges the centralised mechanism of service composition problem and introduces a novel platform entitled Blockchain-based service composition model (Block-SC) based on the Blockchain technology. Block-SC as a novel manufacturing architecture conquers the centralised mechanism by dividing the original service composition problem into multiple sub-problems each of which contains a small fraction of the service/task pool. The capabilities of the proposed platform are remarkable from two perspectives; first, it provides an effective mechanism for collaboration of service composition service providers with a service-oriented approach and from the second perspective, the optimality of service composition problem is profoundly affected considering the dynamic behaviour of Cloud manufacturing.

The fast and cost-efficient delivery of goods ordered online is logistically a challenging problem. Many firms are looking for ways to cut delivery times and costs by exploring opportunities to take advantage of drone technology. Deploying drones as a promising technology is more efficient from both environmental and economic perspectives in last-mile delivery. This paper considers a last-mile delivery system in which a set of drones are operated in coordination with public transportation system to deliver a set of orders to customer locations. A mathematical model based on Vehicle routing Problem (VRP) is extended to solve this problem. A real-world case inspired by Bremen 2025 transportation paradigm is also developed to validate the developed mathematical formulation. Results show that the sequence of visiting customers and public transport stations highly impacts the remaining charge and efficiency of drone tour planning. Also, using public transport vehicles, which enables drones to charge their battery or to approach customers, can reduce the number of drones required for satisfying the demands in a service area. The results show that there are high potentials to save energy for drone-enabled last-mile delivery by using the public transportation network.

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.

In recent years, economic globalisation and manufacturing resource globalisation as two key factors have driven enterprises to transform their business processes to survive in competitive environments. This transformation is challenging as manufacturing enterprises should maintain their support for their customers with high-quality products, lower cost, product customisation capabilities, and quick delivery. Moreover, globalisation has resulted in geographically distributed suppliers across the globe. This challenge will turn into a major research topic when paradigms like Cloud manufacturing are introduced. Cloud manufacturing is a new paradigm which provides ubiquitous, convenient, on-demand network access to a shared pool of configurable manufacturing resources. In this paper, to achieve the ideal goal of Cloud manufacturing, the concept of supplier network logistics planning and manufacturing service composition is focused. Every production task and logistics operation would be defined as a service considering operation process chart flows. The paper has proposed a mathematical model which selects an optimal set of manufacturing and logistics service composition in order to lower operation and logistics costs in operational perspective while fulfilling a novel idea for configured cloud entropy of logistics and operation suppliers. Finally, the paper has presented a numerical example and concluded the remarks, and outlined future research.