Abstract
As a service oriented and networked model, cloud manufacturing (CM) has been proposed recently for solving a variety of manufacturing problems, including diverse requirements from customers. In CM, on-demand manufacturing services are provided by a temporary production network composed of several enterprises participating within an enterprise network. In other words, the production network is the main agent of production and a subset of an enterprise network. Therefore, it is essential to compose the enterprise network in a way that can respond to demands properly. A properly-composed enterprise network means the network can handle demands that arrive at the CM, with minimal costs, such as network composition and operation costs, such as participation contract costs, system maintenance costs, and so forth. Due to trade-offs among costs (e.g., contract cost and opportunity cost of production), it is a non-trivial problem to find the optimal network enterprise composition. In addition, this includes probabilistic constraints, such as forecasted demand. In this paper, we propose an algorithm, named the dynamic enterprise network composition algorithm (DENCA), based on a genetic algorithm to solve the enterprise network composition problem. A numerical simulation result is provided to demonstrate the performance of the proposed algorithm.
Highlights
As consumer demand has changed drastically and quickly, mass customization to deal with the customers’ demands has been popular in manufacturing
It is impossible for an enterprise, especially for small and medium-sized enterprises (SMEs), to retain all of these manufacturing resources, or change the amount of resources to satisfy all of the requirements of customers
The rest of this paper is organized as follows: Section 2 describes a research problem called the enterprise network composition problem and introduces assumptions and notations used throughout this paper; Section 3 proposes an algorithm to solve the problem, and each step of the algorithm is explained in detail; Section 4 provides a numerical simulation to illustrate the suggested algorithm; and Section 5 concludes the paper
Summary
As consumer demand has changed drastically and quickly, mass customization to deal with the customers’ demands has been popular in manufacturing. Many researchers have recently focused on the dynamic manufacturing problems, such as real-time scheduling or resource allocation using genetic algorithm (GA). Enterprises which participate in CM should be considered as an inventory unit for scheduling or resource allocation since their resources ensure the circulation of whole manufacturing activities by cooperating with each other in the production network. It is very important, to compose an enterprise network that handles the requirements from customers (i.e., demands) with minimal costs, and this problem is called the enterprise network composition problem in CM. The rest of this paper is organized as follows: Section 2 describes a research problem called the enterprise network composition problem and introduces assumptions and notations used throughout this paper; Section 3 proposes an algorithm to solve the problem, and each step of the algorithm is explained in detail; Section 4 provides a numerical simulation to illustrate the suggested algorithm; and Section 5 concludes the paper
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