Petri Net Decomposition Approach for Bi-Objective Routing for AGV Systems Minimizing Total Traveling Time and Equalizing Delivery Time

Eda, Shuhei; Nishi, Tatsushi; Mariyama, Toshisada; Kataoka, Satomi; Shoda, Kazuya; Matsumura, Katsuhiko

doi:10.1299/jamdsm.6.672

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Xu and Guo [27] set up a model with the maximum completion time, machine energy consumption, and the number of AGVs as targets, and proposed a hybrid evolutionary algorithm based on a segmented coding mode. Li et al [28] set up a mathematical model and proposed an improved harmonic search algorithm with the objectives of AGV traveling distance, standard deviation of loading capacity, and standard deviation of the difference between the latest and expected delivery times. Eda et al [29] established a model with AGV travel and balanced delivery times as targets, and proposed a Petri net decomposition method.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Kang,

Miao,

Pan

et al. 2024

Tsinghua Sci. Technol.

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With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristicbased initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Kang,

Miao,

Pan

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Finally, the efficiency of this method in trajectory research and time optimization has been demonstrated. The method of bi-objective optimization was also used to express the dispatching and conflict-free path problem for AGVs as a bi-objective optimal firing sequence problem for Petri Net [136]. In this research, two objective functions were set, one for the deviation of delivery time from an initial position to a destination, and another for the sum of the total traveling time.…”

Section: The Shortest Time To Completementioning

confidence: 99%

Analysis of Multi-AGVs Management System and Key Issues: A Review

Lu¹,

Guo²,

Song³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Multiple Automatic Guided Vehicle (multi-AGVs) management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario, such as flexible manufacturing systems, warehouses, container terminals, etc. This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator. The multi-AGVs management problem is a multi-objective, multi-constraint combinatorial optimization problem, which depends on the types of application scenarios. This article classifies and compares the research papers on multi-AGVs management in detail. Firstly, according to the different dimensions of the problem, the multi-AGVs management system is analyzed from three perspectives, namely, 1) task dimensiondispatch, 2) spatial dimension-path, and 3) time dimension-scheduling. The detailed comparison between the three dimensions and their respective solutions are discussed in detail as well. Secondly, according to their utility, the multi-AGVs management problems are divided into three categories: 1) cost reduction, resource-oriented, 2) efficiency improvement, problem-oriented, 3) personalized demand, goal-oriented. The algorithm and methods of the different utility-oriented are analyzed and discussed. The related literature is summarized and corresponds to the composition of the multi-AGVs management system and the multi-AGVs management problems. Finally, according to the literature review, suggestions are made for further research.

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“…To avoid collisions, we should exclude the situations that multiple vehicles are at the same point at the same time or that a vehicle overtakes another vehicle by accelerating or decelerating. It is assumed that tasks are assigned to vehicles by a nearest neighbor method (Eda, et al, 2012). The method is designed such that the task is assigned to the vehicle which has the least estimated traveling time.…”

Section: Routing Problem For Automated Guided Vehicles With Acceleratmentioning

confidence: 99%

A practical model of routing problems for automated guided vehicles with acceleration and deceleration

Nishi

Matsushita

Hisano

et al. 2014

JAMDSM

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We consider an optimization of conflict-free routing problems for automated guided vehicles (AGV) with acceleration and deceleration. A continuous time model is developed to represent the dynamics of vehicles. In the proposed model, the transportation model is discretized into several regions. A network model is created by taking into account the acceleration and deceleration motions. The acceleration and deceleration are represented at curve locations. Column generation heuristic is used to find a near-optimal solution. In this algorithm, we construct a heuristic rule to generate a feasible solution with acceleration and deceleration of vehicles after the column generation. The pricing problem is represented by a resource constrained shortest path problem, which is effectively solved by a labeling algorithm. The dominance relation for acceleration and deceleration is addressed. In the proposed model, the dynamics of real speed AGV model are reflected into the routing problems. By comparing the performance of the conventional method, the effectiveness of the proposed method is demonstrated. IntroductionIn transportation systems such as semiconductor plants, container terminals, and flexible manufacturing systems (FMS), multiple automated guided vehicles (AGVs) are widely used. Transportation tasks are performed at loading points and/or unloading points. In practice, it is required to generate conflict-free routes for multiple vehicles, which minimizes the total transportation time.In previous research, there are a number of studies on vehicle scheduling problems (Bunte and Kliewer, 2009). A branch-and-cut-and-price algorithm for vehicle routing problem using column generation approach was proposed by Bettinelli, et al. (2011). Reveliotis and Roszkowska (2011) regarded the vehicle route planning problem as a resource allocation system and they developed a deadlock avoidance policy. In (2003) applied a column generation approach for routing problems of AGVs. By utilizing the column generation approach, a tight lower bound can be derived by solving the restricted master problem which is a linear programming problem with huge number of columns. In previous studies, for simplicity, the vehicle speed is constant. However, in practical transportation systems, each vehicle has its acceleration and deceleration at traveling, stopping and curving. It is therefore important to take into account the acceleration and

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Petri Net Decomposition Approach for Bi-Objective Routing for AGV Systems Minimizing Total Traveling Time and Equalizing Delivery Time

Cited by 4 publications

References 7 publications

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Analysis of Multi-AGVs Management System and Key Issues: A Review

A practical model of routing problems for automated guided vehicles with acceleration and deceleration

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