A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

Abstract

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm's performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

“…Ahmadi-Javid and Hooshangi-Tabrizi [20] addressed a ternary integration scheduling problem, which incorporated employee timetabling into the schedules of machines and transportation systems in a job shop environment. A timed coloured Petri net (TCPN) approach with two performance objectives was devised in [21] for the simultaneous scheduling problem of machines and AGVs. Moreover, two heuristics (A * and ALS [22]) were developed to partially explore the graph.…”

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

“…Lacomme et al introduced a framework based on a disjunctive graph to model the joint scheduling problem of machines and AGVs [11]. Baruwa et al proposed a simultaneous scheduling method of machines and AGVs based on a timed colored petri net approach [12]. Besides, there are also some practical conditions in the workshop that have been taken into consideration by researchers in their studies, such as battery charging of AGVs, cost of operation, time of operation, and so on [13][14][15][16].…”

A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems

“…A token in this place represents a production task p 2 A token in this place represents a worker p 3 A token in this place represents a material p 4 A token in this place represents the attendance of the worker p 5 A token in this place represents enough materials p 6 A token in this place represents the absence of the worker p 7 A token in this place represents insufficient materials p 8 A token in this place represents the sataus information of a machine p 9 A token in this place represents the sataus information of an AGV p 10 A token in this place represents the finish of the machine processing p 11 A token in this place represents the request to the logistics task p 12 A token in this place represents the detection result p 13 A token in this place represents the qualified WIP p 14 A token in this place represents the nearest and available AGV p 15 A token in this place represents the unqualified WIP p 16 A token in this place represents the remote or unavailable AGV p 17 A token in this place represents an out-buffer p 18 A token in this place represents the selected logistics task p 19 A token in this place represents the correct start point p 20 A token in this place represents the transport time p 21 A token in this place represents the transport to the destination p 22 A token in this place represents the correct destination p 23 A token in this place represents the end and next cycle Figure 3 shows the TCPN model of the self-adaptive collaboration method. This model refers to the basic cycle of production-logistics systems, which consists of twenty-three places and fourteen transitions.…”

“…The transition aims at modeling the check of the worker t 2 The transition aims at modeling the check of the material t 3 The transition aims at modeling requesting the worker t 4 The transition aims at modeling requesting the material t 5 The transition aims at modeling the machine processing and publishing a logistics task t 6 The transition aims at modeling the quality detection t 7 The transition aims at modeling the selection of the AGV t 8 The transition aims at modeling the temporary storage t 9 The transition aims at modeling the selected AGV going to the start point t 10 The transition aims at modeling the reprocessing or the scarp t 11 The transition aims at modeling the rejection of the AGV t 12 The transition aims at modeling picking pallet and loading t 13 The transition aims at modeling the selected AGV going to the destination t 14 The transition aims at modeling the unloading Table 2. Places in Figure 3.…”