Recent dynamic vehicle routing problems: A survey

Rios, Brenner Humberto Ojeda; Xavier, Eduardo C.; Miyazawa, Flávio Keidi; Amorim, Pedro; Curcio, Eduardo; Santos, Maria João

doi:10.1016/j.cie.2021.107604

Cited by 43 publications

(8 citation statements)

References 130 publications

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“…More than half of the papers in the literature review of Ulmer et al (2020) have been published after 2010. Other literature reviews on this topic have been published by Berbeglia et al (2010), Pillac et al (2013), Ritzinger et al (2016), Psaraftis et al (2016), andOjeda Rios et al (2021). It is worth mentioning that multiple trends (e.g., e-commerce growth, sharing economies, and sustainability) and technological advances (e.g., digital connectivity, big data, and automation) are fostering the development of new approaches to tackle dynamic routing problems (Savelsbergh and Van Woensel 2016).…”

Section: Dynamic Routing Problemsmentioning

confidence: 99%

Playing hide and seek: tackling in-store picking operations while improving customer experience

Neves-Moreira¹,

Amorim²

2023

Preprint

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Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named journal. INFORMS journal templates are for the exclusive purpose of submitting to an INFORMS journal and should not be used to distribute the papers in print or online or to submit the papers to another publication.

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Section: Dynamic Routing Problemsmentioning

confidence: 99%

Playing hide and seek: tackling in-store picking operations while improving customer experience

Neves-Moreira¹,

Amorim²

2023

Preprint

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“…The online VRPs contemplate a planning process in which information regarding the VRP instance is provided progressively, that is, in a real-time manner (see a survey [184]). This type of VRP is also connected to dynamic VRPs, which take into account information changes throughout the operational horizon (see a survey [185]). Such changes could be, for example, newly arrived or left orders, changes in service time, and time-dependent traffic conditions.…”

Section: Online/dynamic Routing Problemsmentioning

confidence: 99%

A review on learning to solve combinatorial optimisation problems in manufacturing

Zhang

et al. 2023

IET Collab Intel Manufact

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An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state-of-the-art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges. K E Y W O R D Sbin packing, combinatorial optimisation, deep reinforcement learning, job shop scheduling, manufacturing systems, vehicle routing | INTRODUCTIONCombinatorial optimisation problems (COPs), as one important branch of mathematical optimisation, have practical applications in many fields, such as communication, transportation, manufacturing and aroused broad research in industrial engineering, computer science, and operations research. Due to the NP (non-deterministic polynomial-time) hardness, finding their optimal solutions is challenging. In specific, the discrete solution space in COPs renders the optimisation less efficient, without the guidance of gradient as in continuous optimisation. Meanwhile, the complexity of searching the (near-)optimal solution(s) among feasible solutions could exponentially increase as the problem scale grows. Classic methods, including exact algorithms and (meta-)heuristics, generally depend on massive expertise and tuning work to solve specific problems. They are Cong Zhang, Yaoxin Wu, and Yining Ma are equal contribution.

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“…Later, the general overview written by Braekers et al [17] highlighted the importance of dynamic models in uncertain scenarios as the key problem for transportation and distribution problems in the near future. Furthermore, Rios et al [18] offered a detailed description of the current taxonomy of dynamic VRPs and their relationship with applied problems in the real world. Apart from the previous dynamic scenarios, the novel paradigm of urban mobility based on non-internal-combustion vehicles (mainly electric vehicles) has suggested the need for the design of dynamic VRP models adapted to city logistics [19].…”

Section: Recent Trends In Vrpsmentioning

confidence: 99%

Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

et al. 2022

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Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might be better modeled as random variables than as deterministic values. Likewise, traffic conditions could evolve over time, synchronization issues should need to be considered, or a real-time re-optimization of the routing plan can be required as new data become available in a highly dynamic environment. Clearly, different solving approaches are needed to efficiently cope with such a diversity of scenarios. After providing an overview of current trends in VRPs, this paper reviews a set of heuristic-based algorithms that have been designed and employed to solve VRPs with the aforementioned properties. These include simheuristics for stochastic VRPs, learnheuristics and discrete-event heuristics for dynamic VRPs, and agile optimization heuristics for VRPs with real-time requirements.

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Recent dynamic vehicle routing problems: A survey

Cited by 43 publications

References 130 publications

Playing hide and seek: tackling in-store picking operations while improving customer experience

Playing hide and seek: tackling in-store picking operations while improving customer experience

A review on learning to solve combinatorial optimisation problems in manufacturing

Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

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