Alternative process routing and consolidated production-distribution planning with a destination oriented strategy in cloud manufacturing

Abstract

Cloud manufacturing as a new service-oriented business model transforms manufacturing resources into manufacturing services and manages them centrally via Cloud manufacturing platforms. Using distributed manufacturing resources is an opportunity to decrease the logistics and production costs by allocating tasks to manufacturing resources toward task destination nodes. Decomposing ordered tasks to several subtasks, allocating distributed manufacturing resources to tasks, scheduling manufacturing tasks, and select appropriate logistics strategies to deliver tasks to service providers are important activities that must be considered to improve the performance of a Cloud manufacturing system. This paper proposes a mathematical model to improve the performance of the Cloud manufacturing systems. To that end, task decomposition is implemented based on the alternative process routing concept. Decomposing each task to several manufacturing plans results in enhancing system flexibility and consequently improve the performance of cloud manufacturing. Besides, a consolidated production-distribution toward destination nodes strategy is employed to allocate and deliver tasks to manufacturing resources at minimum costs. The impacts of the alternative process routing concept and consolidated productiondistribution toward destination nodes strategy are discussed by considering different scenarios and the capabilities of the proposed model have been demonstrated.

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

“…Liu et al [8] developed a multi-objective mathematical model to minimise the total cost, carbon emission, and water resources from economic and environmental perspectives while meeting time, cost, and quality requirements. Mehdi Zeynivand et al [9] used the concept of alternative process routing for task decomposition, which enhances the flexibility of the system to complete resource allocation, and task delivery at the lowest cost. Hu et al [10] established a scheduling metric assessment based on five aspects, task loadability, task reliability, manufacturing efficiency, manufacturing resource richness, and IoT matching, and used the AHP method to determine the relative weights of each factor at each level to transform the multi-objective optimisation into a single-objective problem, improving timeliness of the cloud manufacturing platform.…”

Blockchain-Based Architecture for a Sustainable Supply Chain in Cloud Architecture

“…Reference [56] investigated the production-distribution planning problem that considers the production and distribution activity in a supply chain. In this study, a productiondistribution planning problem with a very large scale is examined to compare the speed of the proposed model with the centralized model to solve the optimization problem, which it is not possible to solve with exact methods in a short time due to the dimensions and nature of the problem.…”

Locating Transportation Logistics Centers and Their Dynamic Synergy for Equilibrium Economic Behavior

“…Enhancing technological flexibility and, consequently, improving the performance of digital manufacturing is a newly applied technique in selecting a node place for a TLC network. Besides, a consolidated production distribution toward the destination nodes strategy is employed to allocate and deliver tasks to manufacturing resources at minimum costs [18]. Furthermore, the development of TLC networks using new technologies such as applications of smart logistics based on the Internet of Things (IoT) is in progress [19].…”