Energy-efficient multi-objective flexible manufacturing scheduling

Abstract

This paper presents a novel scheduling of a resource-constrained Flexible Manufacturing System (FMS) with consideration of the following sub-problems: (i) machine loading and unloading, (ii) manufacturing operation scheduling, (iii) machine assignment, and (iv) Automated Guided Vehicle (AGV) scheduling. In the proposed model, both the AGV and machinery are considered as the required resources. Energy efficiency of AGVs has been studied in order to improve environmental sustainability in terms of a linear function, which is based on load and distance, accordingly. Because of the NP-hard characteristics of the problem, a modified multi-objective particle swarm optimization (MMOPSO) has been developed for 2 solving the model and compared with the classic version of the multi-objective particle swarm optimization (MOPSO) algorithm in terms of five performance metrics. Finally, the results are evaluated by the application of a multi-criteria decision-making (MCDM) algorithm according to which the MMOPSO outperforms the MOPSO.

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

“…Nevertheless, there are a few exceptions. Some authors [37,38,85,[87][88][89][90][91][92] schedule transport and take into account the energy required to load/unload vehicles, and others [93][94][95][96][97][98] consider the energy required to transport the jobs between geographically distributed facilities.…”

“…Regarding economic objectives, the most frequently considered one is the total production cost (PC), which is optimized in about 13% of the studies. The costs accounted for include costs of human resources [13,35,110,111], raw materials [73,112], maintenance [73,113], transportation [88], and processing [97,114,115]. Other economic objectives include penalty costs associated with earliness and/or tardiness [22,97,106], machine under-and overutilization [38], and customer dissatisfaction [116].…”

“…The transportation imposes its own additional energy requirements, as vehicles need to move around the shop floor. Transportation may also lead to a more energy-consuming schedule due to the required synchronization of machines and vehicles [37,87,88,90,94]. The vast majority of the studies either disregard transportation or consider that transport time is included in the processing time; consequently, a job can be processed on the next machine immediately after its completion on the previous one.…”

“…Some of the approaches proposed just rank the solutions, typically using the non-dominated sets (see, e.g., [18,31,93]); however, most approaches also use CD to sort the solutions with the same rank (see, e.g., [16,19,[23][24][25][26]56,147]). Other metaheuristics resorting to NDS and CD include ICA [120], PSO [88], and GWO [148].…”

Energy Efficient Order Picking Routing for a Pick Support Automated Guided Vehicle (Ps-AGV)

“…In the context of flexible manufacturing, Barak et al [47] proposed an approach to modeling operation scheduling, machine allocation, and AGV scheduling while minimizing energy consumption. In particular, they used an adapted multi-objective particle swarm optimization method that takes into account both distance and load.…”