Many network applications, eg, industrial control, demand ultra-low latency (ULL) However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds The IEEE 8021 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management Throughout, we identify the pitfalls and limitations of the existing standards and research studies This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Variable neighborhood search (VNS) is a framework for building heuristics, based upon systematic changes of neighborhoods both in a descent phase, to find a local minimum, and in a perturbation phase to escape from the corresponding valley. In this paper, we present some of VNS basic schemes as well as several VNS variants deduced from these basic schemes. In addition, the paper includes parallel implementations and hybrids with other metaheuristics.

Variable neighborhood search: basics and variants

A stochastic mesh method for pricing high-dimensional American options

Online vehicle routing is an important task of the modern transportation service provider. Contributed by the ever-increasing real-time demand on the transportation system, especially small-parcel last-mile delivery requests, vehicle route generation is becoming more computationally complex than before. The existing routing algorithms are mostly based on mathematical programming, which requires huge computation time in city-size transportation networks. To develop routes with minimal time, in this paper, we propose a novel deep reinforcement learning-based neural combinatorial optimization strategy. Specifically, we transform the online routing problem to a vehicle tour generation problem, and propose a structural graph embedded pointer network to develop these tours iteratively. Furthermore, since constructing supervised training data for the neural network is impractical due to the high computation complexity, we propose a deep reinforcement learning mechanism with an unsupervised auxiliary network to train the model parameters. A multisampling scheme is also devised to further improve the system performance. Since the parameter training process is offline, the proposed strategy can achieve a superior online route generation speed. To assess the proposed strategy, we conduct comprehensive case studies with a real-world transportation network. The simulation results show that the proposed strategy can significantly outperform conventional strategies with limited computation time in both static and dynamic logistic systems. In addition, the influence of control parameters on the system performance is investigated.

Online Vehicle Routing With Neural Combinatorial Optimization and Deep Reinforcement Learning

A Taxonomic Review of Metaheuristic Algorithms for Solving the Vehicle Routing Problem and Its Variants

Current logistics and transportation (LT and (b) strategic, planning and operational issues associated with “standard” EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems.

http://openaccess.uoc.edu/webapps/o2/bitstream/10609/83125/1/energies-09-00086-v2.pdf

Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

Abstract This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer’s willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

http://openaccess.uoc.edu/webapps/o2/bitstream/10609/70640/1/Calvet_OM17_Learnheuristics.pdf

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Biased randomization of heuristics using skewed probability distributions: A survey and some applications

The uncapacitated facility location problem (UFLP) is a popular combinatorial optimization problem with practical applications in different areas, from logistics to telecommunication networks. While most of the existing work in the literature focuses on minimizing total cost for the deterministic version of the problem, some degree of uncertainty (e.g., in the customers’ demands or in the service costs) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic UFLP (SUFLP), where optimization goals other than the minimum expected cost can be considered. The development of this simheuristic is structured in three stages: (i) first, an extremely fast savings-based heuristic is introduced; (ii) next, the heuristic is integrated into a metaheuristic framework, and the resulting algorithm is tested against the optimal values for the UFLP; and (iii) finally, the algorithm is extended by integrating it with simulation techniques, and the resulting simheuristic is employed to solve the SUFLP. Some numerical experiments contribute to illustrate the potential uses of each of these solving methods, depending on the version of the problem (deterministic or stochastic) as well as on whether or not a real-time solution is required.

/pdf/solving-the-deterministic-and-stochastic-uncapacitated-3hv94dfjb2.pdf

Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

https://onlinelibrary.wiley.com/doi/pdf/10.1111/itor.12540

Jesica de Armas

Papers

Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Biased randomization of heuristics using skewed probability distributions: A survey and some applications

Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands