Time slot management in selective pickup and delivery problem with mixed time windows

Fallahtafti, Alireza; Karimi, Hossein; Ardjmand, Ehsan; Ghalehkhondabi, Iman

doi:10.1016/j.cie.2021.107512

Cited by 13 publications

(11 citation statements)

References 53 publications

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“…He et al [32] explored and analysed a range of rapid heuristic strategies to identify the most suitable construction approaches for the VRPTW and its variant, the VRPTW-Roaming Delivery Location (VRPTW-RDL), thereby achieving high-quality initial solutions promptly. Regarding mixed time windows, Alireza Fallahtafti et al [33] addressed the complex task of transport logistics coordination for thirdparty logistics firms, presenting a Mixed MILP that integrates time slot allocation with selective pickup and delivery challenges. This approach led to an approximate 90% reduction in both time and node count, thereby substantially lowering total costs and enhancing operational efficiency.…”

Section: Literature Review On Vrptwmentioning

confidence: 99%

“…Regarding mixed time windows, Alireza Fallahtafti et al. [33] addressed the complex task of transport logistics coordination for third‐party logistics firms, presenting a Mixed MILP that integrates time slot allocation with selective pickup and delivery challenges. This approach led to an approximate 90% reduction in both time and node count, thereby substantially lowering total costs and enhancing operational efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

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Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

Jiang,

Chen,

Zheng

et al. 2024

IET Collab Intel Manufact

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The authors investigate new Automated Guided Vehicle (AGV) Routing Problem with Simultaneous Pickup and Delivery with Mixed Time Windows (VRPSPDMTW) in smart workshops, a variation of the classic Vehicle Routing Problem (VRP). A mixed time window vehicle routing model was developed for simultaneous deliveries. This model reduces the cost of AGVs used and distribution cost, along with time window penalties. To address this complex challenge, a Hybrid Adaptive Genetic Algorithm using Variable Neighbourhood Search (AGA‐VNS) is proposed. This algorithm enhances the genetic algorithm's local search capabilities while preserving solution diversity, thereby improving both efficiency and quality of solutions. Comprehensive computational experiments are conducted, which include both VRPSPDTW test benchmark and real‐world smart factory instance studies. The outcomes reveal that the AGA‐VNS algorithm outperforms both professional solver software and advanced heuristic methods significantly. Moreover, the newly developed mixed time window model is more aligned with the requirements of real‐world production processes compared to the traditional time window model. Thus, this research not only presents novel insights into the domain of vehicle routing problems but also demonstrates its significant applicability and potential in the background of intelligent workshops.

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Section: Literature Review On Vrptwmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

Jiang,

Chen,

Zheng

et al. 2024

IET Collab Intel Manufact

View full text Add to dashboard Cite

show abstract

“…Domestic and international research on vehicle routing optimization problems predominantly centers around three key aspects: vehicle routing problems with time windows [1][2][3], vehicle routing problems involving capacity constraints [4][5][6][7], and dynamic vehicle routing problems [8][9][10]. However, there is a lack of comprehensive studies that simultaneously address time window constraints, vehicle load constraints, and speed constraints in vehicle routing problems.…”

Section: Introductionmentioning

confidence: 99%

Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA

Cheng,

Jia,

Gao

2024

Applied Sciences

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In order to tackle the issue of carbon emissions in logistics and distribution, a vehicle routing model was proposed with the aim of minimizing the overall cost, which includes the vehicle’s fixed cost, transportation costs, and carbon emission costs. An enhanced genetic algorithm, based on a modified Nearest Neighbor Construction (NNC) method, was developed to solve this model. A comparative analysis was conducted using the Solomon dataset to study the impact of carbon emissions on vehicle routing optimization, comparing scenarios with and without considering carbon emission costs. The research findings revealed that the Improved NNC (INNC) method exhibited faster convergence compared with the random generation and random insertion methods. Despite a slight increase of 0.5127% in transportation cost when factoring in carbon emission costs, there was a decrease of 4.6914% in carbon emission costs and 0.3578% in total cost. These results offer theoretical insights and empirical evidence to inform the development of models for the logistics industry in the context of a low-carbon economy.

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“…(Sharma, 2017) developed a mathematical problem for truck dock allocation. (Fallahtafti et al, 2021) proposed a mathematical model for time slot management of loading docks in warehouses. Most recently, (Song et al, 2022), proposed an integrated optimization approach for the simultaneous planning of vehicle routes for the TSP and schedules the dock time slots at the SC facility.…”

Section: Introductionmentioning

confidence: 99%

Supply chain control tower and the adoption of intelligent dock booking for improving efficiency

Wyciślak,

Pourhejazy

2023

Front. Energy Res.

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Poor coordination at distribution centers is a prime source of supply chain delays and energy waste that can be avoided through real-time planning and enhanced visibility. As a modern logistics topic with implications for supply chain transformation, Intelligent Dock Booking (IDB) coordinates the incoming and outgoing shipments at distribution centers. The research on IDB is at the early development stage. This study contributes to the Supply Chain Control Tower (SCCT) by developing a conceptual model of IDB, identifying its implementation requirements, and exploring its impacts on the supply chain performance. The causal loops and stock/flow diagrams are used to investigate how several efficiency indicators like the number of cancellations, planning time, utilization of space for loading and unloading, and the duration of processing trucks at distribution centers can be improved. Further, real-time data integration, operational preconditions, automated scheduling, dynamic responsiveness, and interdepartmental integration are identified as the key implementation requirements. The findings provide a foundation for implementing IDB systems in SCCTs.

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Time slot management in selective pickup and delivery problem with mixed time windows

Cited by 13 publications

References 53 publications

Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA

Supply chain control tower and the adoption of intelligent dock booking for improving efficiency

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