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Received Sep 3, 2017; Revised Dec 31, 2017; Accepted Feb 11, 2018
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1. Introduction
Automated guided vehicles (AGVs) participate in unmanned transport systems for material handling [1]. In automated logistics systems, such as automated container terminals and automated warehouses, conflicts may arise when several AGVs run along a narrow lane or pass crossing roads [2]. AGV conflict (which can significantly affect actual AGV speeds, expected travel time, and automated logistic system throughput) has been a key issue in AGV path planning [3]. Many studies have focused on conflict avoidance strategies when studying AGV path planning [1, 4, 5]. Smolic-Rocak et al. used time window insertion in vector form and performed window overlapping tests to dynamically solve the shortest path problem for the supervisory control of AGVs traveling within the layout of a given warehouse [4]. Saidi-Mehrabad et al. considered a conflict-free routing problem (CFRP) for AGVs, as well as a basic job shop scheduling problem (JSSP) to minimize total completion time (make-span). They proposed a two-stage ant colony algorithm (ACA) for this problem, especially for large-size problems [1]. Hidalgo-Paniagua et al. proposed a new multiobjective evolutionary approach based on a variable neighborhood search to produce good paths with shorter lengths, improved safety, and smoother mobile robot movements [5]. However, AGV transportation systems are subject to a high degree of uncertainty [6]. The above references simplified AGV conflict avoidance strategies for deterministic conditions, which might lead to suboptimal or even infeasible solutions.
In the real world, AGV conflict on road networks is highly affected by AGV travel times, which are very uncertain because of roadway capacity variations and traffic demand fluctuations [7, 8]. Therefore, the on-time arrival probability of AGVs in automated logistics systems cannot be ensured, especially for a large number of AGVs operating in a limited area. An interesting queuing approach is used to model routing problems with time-dependent travel time [9]. Strategies have been proposed to avoid stochastic travel time influence against unpredictable and random conflicts [10, 11]. Shao et al. used a two-stage traffic control strategy to resolve conflicts and deadlock problems...