An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Abstract

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS 2 O) in terms of solution quality and computational robustness.Keywords: RFID network planning; mixed-model assembly line; heterogeneous coverage; multi-objective genetic algorithm 1. Introduction Radio frequency identification (RFID) refers to the technology that uses radio waves to transfer data between tags and readers without requiring contact or line of sight. It enables automatic object identification and data transfer, where it can accurately provide real-time information about the locations or states of machines, materials, people or tools. An RFID network consists of readers, which are deployed in different geographical locations within a field to collectively monitor this field, and tags, which are attached to the objects of interest. RFID networks are becoming increasingly pervasive in various applications, e.g. ubiquitous learning (Chen and Huang 2012; Chen and Lin 2014), wireless manufacturing (Brintrup, Ranasinghe, and McFarlane 2010; Huang, Williams, and Zheng 2011), warehousing operation (Pacciarelli, D'Ariano, and Scotto 2011), production schedule (Pacciarelli and D'Ariano 2012) and product life cycle management (Cao et al. 2009(Cao et al. , 2010.Owing to the limited effective tag-reader communication distance (e.g. 3-5 m for UHF RFID devices), deploying an RFID application in a large field often requires a great number of readers, which makes it expensive. The coverage performance, loosely defined as the data communication quality in the coverage area, is mainly impacted by the interference of surrounding objects and the collisions caused by the RFID devices themselves. The high cost and the unstable coverage performance resulted from interference and collusions are two main obstacles for large-scale implementation of RFID technology (Bindel, Conway, and West 2012)...

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