A new AGV scheduling algorithm based on harmony search for material transfer in a real-world manufacturing system

Abstract

With the development of advanced manufacturing technology, more and more enterprises utilize automated guided vehicles to transfer materials for computer numerical control machines in the workshop. Because automated guided vehicle scheduling problem is non-deterministic polynomial-hard, highly efficient automated guided vehicle scheduling algorithms are necessary to improve productivity of the workshop. In order to solve this problem, a mathematical model with a new objective function is established firstly, which incorporates two indicators, that is, the standard deviation of the waiting time of computer numerical control material buffers and the total travel distance of automated guided vehicle, reflecting the overall capacity of computer numerical control machines and the energy efficiency of automated guided vehicles during the actual production, respectively. Then, an improved harmony search algorithm is proposed, which includes an effective discrete encoding scheme of harmony, a new initialization method for harmony memory based on opposition-based learning strategy, a dynamic harmony memory considering rate parameter and a local search strategy. The property of the proposed algorithm is evaluated through three cases from the real-world manufacturing system for producing back cover of smart phone. Compared with the competing algorithms, the results demonstrate the superiority of proposed algorithm for solving automated guided vehicle scheduling problem obviously.

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

“…The constraints (8-9) represent the relationship of the waiting time between a Call Cell and its predecessor. The constraints (10)(11) respectively present two indicators. The time constraint is represented by (12) and the constraints (13) impose restrictions on the decision variables.…”

“…As a result, the production efficiency of enterprises is seriously affected and the transportation cost is considerably increased. The AGVSP is an NP-hard problem [10], and it almost impossible to solve it by using exact solution methods in a limited amount of computing time [11]. Heuristic and meta-heuristic are the best choice for solving such a complex problem [12], [13] because they have been employed in many academic literatures.…”

“…In the existing research, there is still no research on the AGVSP in a linear manufacturing workshop except for Ref. [10], so our research is to re-examine the AGVSP in linear manufacturing workshop in term of the VRP and find a more effective approach to solve it.…”

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

“…Taking the path network shown in Figure 1, for example, edge (1,2) should have the same direction as edge (2,3), in order to make node 2 satisfy the constraint shown in formula (6). erefore, in the design process, edges (24, 1), (1, 2), (2, 3), (3,4), (4,5), and (5,6) can be merged into one combined edge (24,6). According to this preprocessing principle, the guide-path network shown in Figure 1 has nine combined edges.…”

“…To study these problems simultaneously, they proposed a genetic algorithm combined with the Dijkstra algorithm that is based on a time window. Li et al [24] proposed a mathematical model and an improved harmony search algorithm for material transfer in a realworld manufacturing system.…”