Optimal Allocation Method of Discrete Manufacturing Resources for Demand Coordination between Suppliers and Customers in a Fuzzy Environment

Complexity, Aug 2018

Discrete manufacturing products are often assembled from multiple parts through a series of discrete processes. How to effectively configure resources in a discrete manufacturing environment is an important research topic worthy of attention. Based on an in-depth analysis of the discrete manufacturing operation model and the manufacturing resource allocation process, this paper fully considers the uncertainty factors of the manufacturing resource customers and the interests of the manufacturing resource suppliers and proposes a bilevel planning model under a fuzzy environment that comprehensively considers the customers’ expectation bias and the suppliers’ profit maximization. The method firstly uses a language phrase to collect the language evaluation of the customers and suppliers for manufacturing tasks and uses a trapezoidal fuzzy number to convert the language evaluation phrase into a value that can be calculated. Then, we use the prospect theory to optimize the constraint indicators based on the language evaluation of customers and suppliers. Next, the bilevel planning model for optimal configuration of manufacturing resources in discrete manufacturing environment is established under the consideration of the respective interests of both the customers and the suppliers, and the fast nondominated sorting genetic algorithm (NSGA-II) is used to solve the model. Finally, an example is given to verify the validity and feasibility of the model.

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Optimal Allocation Method of Discrete Manufacturing Resources for Demand Coordination between Suppliers and Customers in a Fuzzy Environment

Optimal Allocation Method of Discrete Manufacturing Resources for Demand Coordination between Suppliers and Customers in a Fuzzy Environment Wei Xu and Yinyun Yu School of Management, Shenyang University of Technology, No. 111, Shenliao West Road, Economic & Technological Development Zone, Shenyang 110870, China Correspondence should be addressed to Yinyun Yu; moc.qq@3815095303 Received 27 April 2018; Accepted 24 June 2018; Published 29 August 2018 Academic Editor: Sing Kiong Nguang Copyright © 2018 Wei Xu and Yinyun Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Discrete manufacturing products are often assembled from multiple parts through a series of discrete processes. How to effectively configure resources in a discrete manufacturing environment is an important research topic worthy of attention. Based on an in-depth analysis of the discrete manufacturing operation model and the manufacturing resource allocation process, this paper fully considers the uncertainty factors of the manufacturing resource customers and the interests of the manufacturing resource suppliers and proposes a bilevel planning model under a fuzzy environment that comprehensively considers the customers’ expectation bias and the suppliers’ profit maximization. The method firstly uses a language phrase to collect the language evaluation of the customers and suppliers for manufacturing tasks and uses a trapezoidal fuzzy number to convert the language evaluation phrase into a value that can be calculated. Then, we use the prospect theory to optimize the constraint indicators based on the language evaluation of customers and suppliers. Next, the bilevel planning model for optimal configuration of manufacturing resources in discrete manufacturing environment is established under the consideration of the respective interests of both the customers and the suppliers, and the fast nondominated sorting genetic algorithm (NSGA-II) is used to solve the model. Finally, an example is given to verify the validity and feasibility of the model. 1. Introduction With the rapid economic growth, customer demand for products has become more diverse. How to effectively grasp customer demand, shorten lead time, lower production cost, and increase product quality are key factors for companies to achieve sustainable development [1]. Due to unpredictable market changes, this requires the manufacturing systems to be able to rapidly reconstruct in response to rapid market changes. Discrete manufacturing has gradually become the mainstream model of manufacturing industry because of its advantages such as noncontinuity and reconfigurability [2]. With lots of manufacturing units that provide the same functionality but have different parameters in a discrete system, effective resource configuration which can reflect customer needs is often considered as a key technology [3]. Manufacturing resource configuration (MRC) plays a very important role in discrete systems, especially when manufacturing systems have to cope with shorter product life cycles [4]. In order to fulfill the dynamic customer needs, it always needs discrete systems to invoke several manufacturing units in sequence and combine them together fast. Due to complexity and diversity of manufacturing resources, resource optimal configuration has become a key issue in discrete systems and has been widely studied in both industrial community and academia. Despite of significant progress achieved by the researchers in manufacturing resource configuration, grey relational analysis [5], manufacturing grids [6], the idea of Pareto [7], graph theoretic methods [8], artificial intelligence-based methods [9], and other methods have been proposed successively. Most of current MRC algorithms where the data of MRC are in the form of real number are not suitable for discrete systems environment because the MRC of discrete systems is often fuzzy and uncertain. For example, when MRD describe the quality of products, they can better express their customers’ perception by using language evaluation words of “good,” “bad,” and so on. Therefore, this paper proposes a resource optimization configuration that considers the customers’ expectation bias and considers the suppliers’ profit maximization under fuzzy environment. The remainder of this paper is organized as follows. After reviewing the related literature in Literature Reviews, some basic theories such as prospect theory and bilevel programming model are introduced in Preliminary Knowledge. Problem Description and Symbol Introduction presents the issues of the thesis research and some basic symbols. In The Proposed Method, it proposes a novel method for discrete manufacturing resource optimization configuration under fuzzy environment. An illustrative example is given to demonstra (...truncated)


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Wei Xu, Yinyun Yu. Optimal Allocation Method of Discrete Manufacturing Resources for Demand Coordination between Suppliers and Customers in a Fuzzy Environment, Complexity, 2018, 2018, DOI: 10.1155/2018/1410957