A Double Traveling Salesman Problem With Three-Dimensional Loading Constraints for Bulky Item Delivery

Minzhi, Ruan; Cheng, Shiou–Ying; Tang, Jing; Qi, Chao; Qiu, Shuang

doi:10.1109/access.2021.3051248

Cited by 9 publications

(8 citation statements)

References 42 publications

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“…The primary goal is to find an arrangement of the items within the container(s) that optimises a specific objective function, often related to space efficiency [5][6][7]. This problem is of practical importance in the field of logistics and transportation, where efficient cargo loading can lead to cost savings and improved overall supply chain efficiency [8][9][10]. There are different variants of the CLP, including the Pallet Loading Problem, and the 2D and 3D packing problems.…”

Section: Optimisation Problems: Container and Pallet Loading Problemsmentioning

confidence: 99%

“…It is an extension of the classic Travelling Salesman Problem (TSP), a problem of finding the shortest closed tour that visits a set of given locations exactly once. The DTSP introduces an additional layer of complexity by requiring two salesmen to find their individual shortest routes while covering the same set of locations [10].…”

Section: The Three-dimensional Double Travelling Salesman Problem Wit...mentioning

confidence: 99%

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Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Lopes,

Trovati,

Pereira

2024

Future Internet

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Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach.

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Section: Optimisation Problems: Container and Pallet Loading Problemsmentioning

confidence: 99%

Section: The Three-dimensional Double Travelling Salesman Problem Wit...mentioning

confidence: 99%

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Lopes,

Trovati,

Pereira

2024

Future Internet

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“…Recently, Ruan et al. (2021) study a particular case of DTSPMS, considering three‐dimensional container loading constraints. They propose an ILP model and implement a standard genetic algorithm (GA) and an improved GA.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Again the DTSPMS is studied in a theoretical way, Alfandari and Toulouse (2021) present several approximation algorithms and provide interesting approximation results, in both cases of standard and differential approximation. Recently, Ruan et al (2021) study a particular case of DTSPMS, considering three-dimensional container loading constraints. They propose an ILP model and implement a standard genetic algorithm (GA) and an improved GA.…”

Section: Literature Reviewmentioning

confidence: 99%

A metaheuristic for the double traveling salesman problem with partial last‐in‐first‐out loading constraints

Mardones

Gatica

Contreras‐Bolton

2022

Int Trans Operational Res

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This paper addresses the double traveling salesman problem with partial last‐in‐first‐out (LIFO) loading constraints. A vehicle picks up items in the pickup area and loads them into its container, a horizontal stack. Once all the pickup operations are done, the vehicle can deliver the items to the delivery area because pickup‐and‐delivery areas are geographically separated. Additionally, the loading and unloading operations also follow a partial LIFO policy. The aim is to find a Hamiltonian cycle that satisfies the loading and unloading policy described above in order to minimize the distance traveled by the vehicle in both areas and the cost of the rearrangements that are performed across the routes. The state‐of‐the‐art algorithm finds good solutions to the existing instances. However, finding good‐quality solutions in large instances requires longer computing times. Therefore, we propose an iterated local search that considers two additional components to the classical components, the intensification and restart procedure. The first helps in a more thorough examination of promising neighborhoods, while the second helps in the complete diversity of the solutions in the hopes of escaping the local optima. Moreover, a new unique decoding approach for the solution representation is proposed to effectively generate feasible solutions. The proposed algorithm's performance is evaluated on an existing set of instances and in two new additional sets of larger instances that are proposed. The computational results obtained on the three test sets show that the proposed algorithm outperforms the state‐of‐the‐art algorithm in terms of computing time required to find good‐quality solutions.

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“…The unloading and reloading operations are called additional operations according to Battarra et al [2]. Since the rearrangement of large household appliances takes a lot of effort [3], we associate a handling cost with each additional operation on these commodities as a penalty.…”

Section: Introductionmentioning

confidence: 99%

A Selective Many-to-Many Pickup and Delivery Problem With Handling Cost in the Omni-Channel Last-Mile Delivery

2022

IEEE Access

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This paper introduces a selective many-to-many pickup and delivery problem with handling cost (SMMPDPH) arising in the omni-channel last-mile delivery. In SMMPDPH, each request is associated with multiple pickup and delivery nodes, which provide or require commodities. A request is to load commodities from the pickup nodes and transport them to the delivery nodes. Since the total supply is greater than the total demand, we need to determine the selected subset of pickup nodes to visit for each request. Based on the loading and unloading policy, we take into account the handling cost incurred by additional operations. SMMPDPH aims to obtain a routing plan with the minimum total cost, including the travel cost and handling cost, for a fleet of homogeneous vehicles with limited capacity and driving mileage to satisfy the requests. We present two mixed integer programming formulations of the problem and propose two algorithms, including an iterated local search (ILS) and a memetic algorithm (MA), with specially designed move operators to solve the problem. Experiments on small instances clearly indicate the effectiveness of the two heuristic methods. With a comparison of efficiency on large-scale instances, we find that MA outperforms ILS in terms of both the best and average solution quality.INDEX TERMS Vehicle routing problem, pickup and delivery, handling cost, mixed integer programming, heuristics.

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A Double Traveling Salesman Problem With Three-Dimensional Loading Constraints for Bulky Item Delivery

Cited by 9 publications

References 42 publications

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

A metaheuristic for the double traveling salesman problem with partial last‐in‐first‐out loading constraints

A Selective Many-to-Many Pickup and Delivery Problem With Handling Cost in the Omni-Channel Last-Mile Delivery

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