Freight Time and Cost Optimization in Complex Logistics Networks

Sert, Egemen; Hedayatifar, Leila; Rigg, Rachel A.; Akhavan, Amir; Buchel, Olha; Saadi, Dominic Elias; Kar, Aabir Abubaker; Morales, Alfredo J.; Bar‐Yam, Yaneer

doi:10.1155/2020/2189275

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Unfortunately, raw data was unavailable for any of the published real-world application work we reviewed. We thus support our argument by describing this work where the parameterization involves pairwise products of the targets (Mauttone and Urquhart 2009;Zhen, Zhuge, and Lei 2016;Alonso-Mora, Wallar, and Rus 2017;Sert et al 2020), and identifying plausible correlation structures between targets that would induce practical gaps between two-stage and end-to-end performance.…”

Section: Introductionsupporting

confidence: 65%

“…Finally, demand-weighted facility location problems place facilities to respond to customers and minimize transportation costs; as such, the cost of each route involves the product of a prediction for customer demands and the transportation costs from facilities to customers (Sert et al 2020). Transportation delays are higher during peak season (van der Tuin and Pel 2020) and most product demands will have seasonal effects.…”

Section: Two-stage Optimization With Potentially Correlated Targets I...mentioning

confidence: 99%

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The Perils of Learning Before Optimizing

Cameron¹,

Hartford²,

Lundy³

et al. 2022

AAAI

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Formulating real-world optimization problems often begins with making predictions from historical data (e.g., an optimizer that aims to recommend fast routes relies upon travel-time predictions). Typically, learning the prediction model used to generate the optimization problem and solving that problem are performed in two separate stages. Recent work has showed how such prediction models can be learned end-to-end by differentiating through the optimization task. Such methods often yield empirical improvements, which are typically attributed to end-to-end making better error tradeoffs than the standard loss function used in a two-stage solution. We refine this explanation and more precisely characterize when end-to-end can improve performance. When prediction targets are stochastic, a two-stage solution must make an a priori choice about which statistics of the target distribution to model---we consider expectations over prediction targets---while an end-to-end solution can make this choice adaptively. We show that the performance gap between a two-stage and end-to-end approach is closely related to the \emph{price of correlation} concept in stochastic optimization and show the implications of some existing POC results for the predict-then-optimize problem. We then consider a novel and particularly practical setting, where multiple prediction targets are combined to obtain each of the objective function’s coefficients. We give explicit constructions where (1) two-stage performs unboundedly worse than end-to-end; and (2) two-stage is optimal. We use simulations to experimentally quantify performance gaps and identify a wide range of real-world applications from the literature whose objective functions rely on multiple prediction targets, suggesting that end-to-end learning could yield significant improvements.

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Section: Introductionsupporting

confidence: 65%

Section: Two-stage Optimization With Potentially Correlated Targets I...mentioning

confidence: 99%

The Perils of Learning Before Optimizing

Cameron¹,

Hartford²,

Lundy³

et al. 2022

AAAI

View full text Add to dashboard Cite

show abstract

“…The comprehensive optimisation of distribution logistics and distribution costs and the simultaneous improvement of service quality provides significant competitive advantages for companies [11]. The complexity of providing timely and cost-effective distribution logistics of finished goods from industrial facilities to customers makes effective operational coordination difficult; on the other side, effectiveness is crucial for maintaining customer service levels and sustaining a business [12]. Recently, there have been efforts to achieve more effective and less polluting freight transportation systems; an advanced system which uses advanced methods to solve vehicle routing and scheduling problems, considering a plethora of constraints has been developed within a research project co-funded by Greece and the European Union [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kagnicioglu [28] proposed a fuzzy multipurpose model in which the goals and some limitations are fuzzy to consider many conflicting and unclear goals and limitations when deciding on the choice of supplier. Morlacchi [37] developed a model that combines the application of fuzzy locations, the uncertainty of future orders, and sometimes-extreme competitive pressure to reduce costs [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Selection of the Transport Mode Using the AHP Method Within Distribution Logistics of Motor Fuels

Hruska

Kmetík

Chocholáč

2021

Promet

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To remain competitive and respond to rapidly changing markets, we need to increase flexibility in today's global marketplace. In this respect, the selection of the appropriate mode of transport is one of the most important functions to be performed by logistics. The selection of the appropriate mode of transport is a multi-criteria problem involving both quantitative and qualitative criteria. This paper deals with the selection of the mode of transport using the Analytic Hierarchy Process method (AHP). AHP is a method of decomposing a complex unstructured situation into simpler components to create a hierarchical system problem. This paper describes a general model of selection of transport mode using AHP including its application to a manufacturing company that selects the appropriate mode of transport from three potential transport modes. The aim of this paper is to create a useful decision support tool for selection of the transport mode using the AHP method within distribution logistics of motor fuels. This tool helps companies to make the right decision on the choice of transport mode by taking into account different importance of the different criteria that influence the decision-making process.

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“…Logistics planning becomes increasingly complex with growing number of users, their varied locations, the uncertainty of orders, extreme competitive pressure to reduce inventory costs, etc. [7].…”

Section: Introductionmentioning

confidence: 99%

Novel Spherical Fuzzy MARCOS Method for Assessment of Drone‐Based City Logistics Concepts

et al. 2021

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Technological innovations from the last few years, in the combination with city logistics (CL) initiatives, make the definition of novel, complex, sustainable CL solutions possible. Unmanned aerial vehicles (drones) as a technology attracted lots of attention in the literature. Various researches focused on different drone-based delivery approaches, but there are only a few articles dealing with drones as the elements of complex CL concepts. The goal of this paper is to evaluate different drone-based CL concepts. Based on the existing ideas of drone application in delivery, the main group of CL concepts and their variants are defined, which represents the main contribution of the article. The evaluation and ranking of concepts are performed from the aspect of all CL stakeholders and the defined set of criteria by applying measurement of alternatives and ranking according to compromise solution (MARCOS) multicriteria decision-making (MCDM) method in spherical fuzzy environment, which represents another contribution of the article. The results indicate that the potentially best CL concept, with the final score of 0.408, is the one that refers to the transformation of the logistics system into a two-echelon system with the implementation of micro-consolidation centers (MCCs), in which the delivery of goods to MCCs is realized with rail transportation mode and the last delivery phase with drones. It is followed by the concepts that imply MCCs, and rail transportation in the function of mobile depots for drone launching and MCCs and ground delivery vehicles (GDVs), with the final scores of 0.395 and 0.390, respectively.

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Freight Time and Cost Optimization in Complex Logistics Networks

Cited by 11 publications

References 33 publications

The Perils of Learning Before Optimizing

The Perils of Learning Before Optimizing

Selection of the Transport Mode Using the AHP Method Within Distribution Logistics of Motor Fuels

Novel Spherical Fuzzy MARCOS Method for Assessment of Drone‐Based City Logistics Concepts

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