A systematic literature review of the vehicle routing problem in reverse logistics operations

Sar, Kubra; Ghadimi, Pezhman

doi:10.1016/j.cie.2023.109011

Cited by 17 publications

(3 citation statements)

References 131 publications

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“…Sar and Ghadami [13] focused on variants for vehicle routing problems in RL. They took information from 109 relevant articles.…”

Section: Problem Domainmentioning

confidence: 99%

Third-Party Reverse Logistics Selection: A Literature Review

Anon,

Amin,

Baki

2024

Logistics

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Background: This literature review delves into the concept of ‘Third-party Reverse Logistics selection’, focusing on its process and functionality using deterministic and uncertain decision-making models. In an increasingly globalized world, Reverse Logistics (RL) plays a vital role in optimizing supply chain management, reducing waste, and achieving sustainability objectives. Deterministic decision-making models employ predefined criteria and variables, utilizing mathematical algorithms to assess factors such as cost, reliability, and capacity across various geographical regions. Uncertain decision-making models, on the other hand, incorporate the unpredictability of real-world scenarios by considering the uncertainties and consequences of decision making and choices based on incomplete information, ambiguity, unreliability, and the option for multiple probable outcomes. Methods: Through an examination of 41 peer-reviewed journal publications between the years 2020 and 2023, this review paper explores these concepts and problem domains within three categories: Literature Reviews (LR), Deterministic Decision-Making (DDM) models, and Uncertain Decision-Making (UDM) models. Results: In this paper, observations and future research directions are discussed. Conclusions: This paper provides a comprehensive review of third-party reverse logistics selection papers.

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“…Sar and Ghadami [13] focused on variants for vehicle routing problems in RL. They took information from 109 relevant articles.…”

Section: Problem Domainmentioning

confidence: 99%

Third-Party Reverse Logistics Selection: A Literature Review

Anon,

Amin,

Baki

2024

Logistics

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“…For instance, tight bounding constraints and relatively large values of m make the problem more challenging, and finding feasible solutions for larger graph instances would require more time. Finally, although our work is limited to mTSP, it might be extended to more challenging logistics or scheduling problems, particularly those related to VRP [121][122][123][124][125].…”

Section: Ip Dummy Depots Cp Op Minsum Minmaxmentioning

confidence: 99%

Compact Integer Programs for Depot-Free Multiple Traveling Salesperson Problems

Cornejo-Acosta,

García-Díaz,

Pérez-Sansalvador

et al. 2023

Mathematics

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Multiple traveling salesperson problems (mTSP) are a collection of problems that generalize the classical traveling salesperson problem (TSP). In a nutshell, an mTSP variant seeks a minimum cost collection of m paths that visit all vertices of a given weighted complete graph. This paper introduces novel compact integer programs for the depot-free mTSP (DFmTSP). This fundamental variant models real scenarios where depots are unknown or unnecessary. The proposed integer programs are adapted to the main variants of the DFmTSP, such as closed paths, open paths, bounding constraints (also known as load balance), and the minsum and minmax objective functions. Some of these integer programs have O(n2m) binary variables and O(n2) constraints, where m is the number of salespersons and n=|V(G)|. Furthermore, we introduce more compact integer programs with O(n2) binary variables and O(n2) constraints for the same problem and most of its main variants. Without losing their compactness, all the proposed programs are adapted to fixed-destination multiple-depots mTSP (FD-MmTSP) and a combination of FD-MmTSP and DFmTSP, where fewer than m depots are part of the input, but the solution still consists of m paths. We used off-the-shelf optimization software to empirically test the proposed integer programs over a classical benchmark dataset; these tests show that the proposed programs meet desirable theoretical properties and have practical advantages over the state of the art.

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“…f) Delivery Route Optimization: AI algorithms can analyze factors such as traffic patterns, deliverywindows, and the number and location of deliveries to determine the most efficient routes. This can reduce fuel costs, improve delivery speed, and increase customer satisfaction[67]. g) Supplier Selection and Risk Management: AI can help businesses evaluate and select suppliers based on various factors such as cost, quality, delivery performance, and risk.…”

mentioning

confidence: 99%

A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation

Aldoseri,

Al-Khalifa,

Hamouda

2023

Preprint

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The integration of automation and process optimization within the context of AI-powered digital transformation has emerged as a pivotal strategy for organizations aiming to enhance efficiency, foster innovation, and competitiveness. This paper is devoted to present innovative contribution by providing a comprehensive structured roadmap that outlines the foundational principles necessary for the successful integration of automation and optimizing processes within the con-text of emerging AI technologies. The paper introduce a cohesive framework consisting of es-sential key pillars: Data-Driven Insights, Seamless Automation, Adaptive Learning and Contin-uous Improvement, Human-Centric Collaboration, Ethical and Responsible AI, Strategic Align-ment, Scalability, and Innovation. These pillars function as guiding principles to navigate the in-tricate landscape of automation-driven initiatives within AI-powered digital transformation. By embracing these pillars, organizations can embark on a transformative journey that maximizes the potential of automation, fosters innovation, and positions them as leaders in the ever-evolving landscape of AI-driven business operations.

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A systematic literature review of the vehicle routing problem in reverse logistics operations

Cited by 17 publications

References 131 publications

Third-Party Reverse Logistics Selection: A Literature Review

Third-Party Reverse Logistics Selection: A Literature Review

Compact Integer Programs for Depot-Free Multiple Traveling Salesperson Problems

A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation

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