Comparison of PSO and DE for truck scheduling in multi-door cross docking terminals

Wisittipanich, Warisa; Hengmeechai, Piya

doi:10.1109/ieem.2017.8289849

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Besides the Lagrangian relative methods mentioned above, the two-stage differential evolution (2S-DE) algorithm is introduced in comparison with the AMRDA. The 2S-DE is proved to achieve better performance than PSO and original DE and to be used as an effective solution approach for solving truck scheduling problem in multi-door cross-docking (Wisittipanich and Hengmeechai, 2017). This paper presents the comparison of 2S-DE and AMRDA for solving truck scheduling problems with middle to large-sized problem sets and a duality gap is also generated for each instance to evaluate the quality of the solution.…”

Section: Experimental Studiesmentioning

confidence: 99%

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

Zhou

Zong

2021

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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.

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Section: Experimental Studiesmentioning

confidence: 99%

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

Zhou

Zong

2021

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“…Therefore, many researchers have started applying various SI algorithms with great promise because of their high computational efficiency, reliability, scalability, self-organizing, longevity and low-cost [4]. In recent years, SI techniques have been widely used on many practical real-world problems in the fields of scheduling [5], production [6], facility control [7], [8], transportation [9] and so on.…”

Section: Dementioning

confidence: 99%

A Survey of Swarm Intelligence Techniques in VLSI Routing Problems

et al. 2020

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Routing is a complex and critical stage in the physical design of Very Large Scale Integration (VLSI), minimizing interconnect length and delay to optimize overall chip performance. With the rapid development of modern technology, VLSI routing faces enormous challenges such as large delay, high congestion, and high-power consumption. As a rising optimization method, Swarm Intelligence (SI) inspired from collective intelligence behaviors through cooperation or interaction with the environment provides effectiveness and robustness for solving NP-hard problems. Many researchers have consequently used SI techniques to solve routing-related problems in VLSI. This paper reviews the application of several SI techniques to the VLSI routing filed. Firstly, five commonly used SI techniques and related models, and three classic routing problems are described: Steiner tree construction, global routing and detailed routing. Then an overview of the current state of this field is given according to the above categories, and the survey offers informative discussions from five aspects: 1) Steiner minimum tree construction; 2) wirelength-driven routing; 3) obstacle-avoiding routing; 4) timing-driven routing; 5) power-driven routing. Finally, under three new technology models: X-architecture, multiple dynamic supply voltage and via-pillar, the future development trends are pointed as follows: 1) suggesting suitable SI techniques to specific routing problems for advanced technology models; 2) exploring new and available SI techniques that have not yet been applied to VLSI routing.

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Comparison of PSO and DE for truck scheduling in multi-door cross docking terminals

Cited by 2 publications

References 11 publications

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

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

A Survey of Swarm Intelligence Techniques in VLSI Routing Problems

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