Robust Multiperiod Vehicle Routing Under Customer Order Uncertainty

Subramanyam, Anirudh; Mufalli, Frank; Laínez-Aguirre, José M.; Pinto, José M.; Gounaris, Chrysanthos E.

doi:10.1287/opre.2020.2009

Cited by 26 publications

(13 citation statements)

References 61 publications

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“…Bertsimas and Van Ryzin (1991) generalize the contribution of the former paper and propose a mathematical model for the stochastic and dynamic vehicle routing problems (VRP). Other interesting extensions of stochastic or dynamic settings appear in Savelsbergh and Sol (1998), Bent and Van Hentenryck (2004), Secomandi (2001), Secomandi and Margot (2009), Goodson et al (2013), Subramanyam et al (2021). A recent contribution (Liu et al (2021)) captures the uncertainty of service time in the last-mile delivery services: with the aim of improving the on-time delivery performance, a framework integrating travel-time predictors with order-assignment optimization is proposed, which aims at improving the on-time delivery performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates

Li¹,

Archetti²,

Ljubić³

2022

Preprint

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In this paper, we study a sequential decision making problem faced by e-commerce carriers related to when to send out a vehicle from the central depot to serve customer requests, and in which order to provide the service, under the assumption that the time at which parcels arrive at the depot is stochastic and dynamic. The objective is to maximize the number of parcels that can be delivered during the service hours. We propose two reinforcement learning approaches for solving this problem, one based on a policy function approximation (PFA) and the second on a value function approximation (VFA). Both methods are combined with a look-ahead strategy, in which future release dates are sampled in a Monte-Carlo fashion and a tailored batch approach is used to approximate the value of future states. Our PFA and VFA make a good use of branch-and-cut-based exact methods to improve the quality of decisions. We also establish sufficient conditions for partial characterization of optimal policy and integrate them into PFA/VFA. In an empirical study based on 720 benchmark instances, we conduct a competitive analysis using upper bounds with perfect information and we show that PFA and VFA greatly outperform two alternative myopic approaches. Overall, PFA provides best solutions, while VFA (which benefits from a two-stage stochastic optimization model) achieves a better tradeoff between solution quality and computing time.

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Section: Literature Reviewmentioning

confidence: 99%

Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates

Li¹,

Archetti²,

Ljubić³

2022

Preprint

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“…In many applications, customer demands are naturally discrete quantities. For example, logistics operators may not know a priori which customers to visit [1,50]. Another example is the phenomenon of no-shows in queuing or appointment systems with scheduled arrivals, such as in healthcare clinics [35].…”

Section: Motivationmentioning

confidence: 99%

A Lagrangian Dual Method for Two-Stage Robust Optimization with Binary Uncertainties

Subramanyam¹

2021

Preprint

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This paper presents a new exact method to calculate worst-case parameter realizations in two-stage robust optimization problems with categorical or binary-valued uncertain data. Traditional exact algorithms for these problems, notably Benders decomposition and column-andconstraint generation, compute worst-case parameter realizations by solving mixed-integer bilinear optimization subproblems. However, their numerical solution can be computationally expensive not only due to their resulting large size after reformulating the bilinear terms, but also because decision-independent bounds on their variables are typically unknown. We propose an alternative Lagrangian dual method that circumvents these difficulties and is readily integrated in either algorithm. We specialize the method to problems where the binary parameters switch on or off constraints as these are commonly encountered in applications, and discuss extensions to problems that lack relatively complete recourse and to those with integer recourse. Numerical experiments provide evidence of significant computational improvements over existing methods.

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“…However, traditional flexibility analysis focuses primarily on nonlinear problems, which may be the reason why it so far has not been recognized or noticed by the operations research community. In the last few years, the number of ARO-related works in PSE has increased rapidly, addressing diverse applications in process design 19,20 , planning and scheduling [14][15][16][21][22][23][24] , model predictive control [25][26][27] , supply chain optimization 28,29 , etc.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty

Zhang

Feng

2020

Preprint

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This work proposes a framework for multistage adjustable robust optimization that unifies the treatment of three different types of endogenous uncertainty, where decisions, respectively, (i) alter the uncertainty set, (ii) affect the materialization of uncertain parameters, and (iii) determine the time when the true values of uncertain parameters are observed. We provide a systematic analysis of the different types of endogenous uncertainty and highlight the connection between optimization under endogenous uncertainty and active learning. We consider decision-dependent polyhedral uncertainty sets and propose a decision rule approach that incorporates both continuous and binary recourse, including recourse decisions that affect the uncertainty set. The proposed method enables the modeling of decision-dependent nonanticipativity and results in a tractable reformulation of the problem. We demonstrate the effectiveness of the approach in computational experiments that cover a range of applications, including plant redesign, maintenance planning with inspections, optimizing revision points in capacity planning, and production scheduling with active parameter estimation. The results show significant benefits from the proper modeling of endogenous uncertainty and active learning.

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Robust Multiperiod Vehicle Routing Under Customer Order Uncertainty

Abstract: Deliver today or deliver tomorrow?

Cited by 26 publications

References 61 publications

Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates

Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates

A Lagrangian Dual Method for Two-Stage Robust Optimization with Binary Uncertainties

A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty

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