Energy-conscious dynamic sequencing method for dual command cycle unit-load multiple-rack automated storage and retrieval systems

Abstract

Abstract. A Dual Command (DC) cycle \dynamic sequencing" method in unit-load multiple-rack AS/RS system under Time-Of-Use (TOU) electricity tari s is applied in this paper. To make a type of energy-e cient model, overconsumption cost of on-peak period electricity, penalty cost of overconsumption of power, bounds on the total consumed energy, and accessible times of all facilities are considered in the model. Moreover, a Genetic Algorithm (GA) is developed to achieve a near-optimum solution to the suggested energy-based mathematical model with the objective of minimizing the total cost of the AS/RS system under TOU tari s. Considering that no benchmark is obtainable in the literature, a Simulated Annealing (SA) algorithm is developed to certify the outcome gained. For supplementary con rmation, we compare the total cost of our model with that of single-tari model and also conduct sensitivity analysis of the allowable amount of power consumption. The system throughput, in terms of time and cost, is calculated for the model too. In the last part, sixteen numerical examples with di erent numbers of required storage/retrieval orders are suggested to display the function of the proposed procedure. Our outcomes veri ed that GA is able to obtain favorable and closer optimal solutions and the TOU tari s model get the minimum total cost.

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

“…Moreover, natural or biological phenomena have stimulated metaheuristic algorithms based on swarm intelligence and evolution (Abdullah and Ahmed 2020 ; Islam and Ahmed 2020 ) and applied them to various models (Wang et al 2020a ). Many researchers have successfully employed traditional swarm intelligence and evolutionary algorithms, for instance, ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA) (Roozbeh Nia et al 2017a , b ). Despite these algorithms, there are some modern and attractive examples involving the Horse herd Optimization Algorithm (HOA) (MiarNaeimi et al 2021 ; Moldovan 2020 ), Whale Optimization Algorithm (WOA) (Mirjalili and Lewis 2016 ; Islam et al 2021 ; Yan et al 2021 ; Zhang and Wen 2021 ; Wang et al 2021b ), Lion Optimization Algorithm (LOA) (Yazdani and Jolai 2016 ; Varshney et al 2021 ; Selvi and Ramakrishnan 2020 ; Wang et al 2020b ; Gope et al 2019 ), Ant Lion Optimizer (ALO) (Mirjalili 2015 ; Wang et al 2020a ; Bekakra et al 2021 ; Singh et al 2021 ; Chen et al 2020a ; Pradhan et al 2020 ), and Grey wolf optimizer (GWO) (Mirjalili et al 2014 ; Padhy and Panda 2021 ; Bekakra et al 2021 ; Wang et al 2021a ; Liu et al 2021 ; Tütüncü et al 2021 ).…”

Task Scheduling Problem of Double-Deep Multi-Tier Shuttle Warehousing Systems

“…Ene et al [11] adopted a genetic algorithm to minimize the energy consumption with a proper storage policy other than service time. For the unit-load multiple rack AS/RS, Nia et al [8] proposed a dual-command "dynamic sequencing" method. In order to estimate greenhouse gas (GHG) emissions more comprehensively, limitations on the available time of all automation equipment and the total allowable GHG emissions generated by all facilities are calculated in this model.…”

“…Research on the carbon emissions of warehousing systems has gradually attracted more and more academic and industrial attention. In the literature reviews, most researchers have focused on carbon emissions in warehousing design and path optimization and storage policies [8][9][10][11]. Some scholars have established novel models to study the sustainability of warehousing systems [12,13].…”