Adaptive memory red deer algorithm for cross-dock truck scheduling with products time window

Abstract

Purpose The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks. Design/methodology/approach This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time. Findings Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window. Research limitations/implications The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies. Originality/value For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

A case-oriented computational study for sustainable fleet planning in a battery closed-loop supply chain network under uncertainty

“…However, adding social and environmental aspects leads to more complexity in SC network design problems (Fragoso and Figueira, 2021). On the other hand, although many recent researches are available on the sustainable CLSC network design problems in the literature (Zhen et al ., 2019; Zhou and Zong, 2021; Moheb-Alizadeh et al ., 2021; Soleimani et al ., 2022; Golpîra and Javanmardan, 2022; Mohammadi and Nikzad, 2022; Momenitabar et al ., 2022; Alinezhad et al ., 2022; Seydanlou et al ., 2022; Baghizadeh et al ., 2022; Guo et al ., 2022; Elfarouk et al ., 2022; Soleimani et al ., 2022; Ghalandari et al ., 2023; Xu et al ., 2023; Goodarzian et al ., 2023; Mirzagoltabar et al ., 2023), there is still a lack of studies on the quantitative modeling of social objectives for sustainable fleet planning and CLSC network design problems. It was also highlighted by Fragoso and Figueira (2021) and Mohammadi and Nikzad (2022) that most of the existing studies in the literature consider one or two aspects of sustainability, usually neglecting the quantitative modeling of the social dimension.…”

Review and analysis for the Red Deer Algorithm

“…They found that the RDA-SA algorithm outperformed NSGA-II through different criteria and analyses. The red deer algorithm was also used to solve the real-world size case of truck scheduling problems in cross-docking with product time window (Zhou and Zong 2021 ). The lower bound of the problem is based on both the Lagrangian relaxation problem and the NP-hard nature of the truck scheduling problem.…”

Solving the trapezoidal fuzzy assignment problem using the novel Dhouib-Matrix-AP1 heuristic

“…Figure 10 depicts the generated solution using Python programming language. The total trapezoidal fuzzy assignment cost is 𝐹 ̃= (17,22,37,49). This solution is computed by: 𝐹 ̃= (3,4,6,9) + (2,3,5,7) + (6,7,9,10) + (4,5,7,9) + (2,3,10,14) = (17,22,37,49) Thus, for the decision maker, the total allocation cost will lie at [56] with a 100% level of satisfaction (lying at [61].…”

“…The total trapezoidal fuzzy assignment cost is 𝐹 ̃= (17,22,37,49). This solution is computed by: 𝐹 ̃= (3,4,6,9) + (2,3,5,7) + (6,7,9,10) + (4,5,7,9) + (2,3,10,14) = (17,22,37,49) Thus, for the decision maker, the total allocation cost will lie at [56] with a 100% level of satisfaction (lying at [61]. Also, increasing and decreasing levels for the remaining values of the minimal cost as shown in (3).…”