Solution methodologies for vehicle routing problems with stochastic demand

Goodson, Justin

doi:10.17077/etd.trbfswku

Cited by 7 publications

(7 citation statements)

References 74 publications

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“…The approximated value of the post decision state seen in Equation ( 33) could be obtained through the averaged rollout return after the N number of the Monte Carlo simulation for each PDS following the basic pseudo Algorithm 2 (see [8]). Here, the expected rollout return B(π B(s a k k ) , k + 1, K) [67] is obtained per rollout episode until the end horizon K following a policy π B(s a k k ) , which is obtained by B given the current state s k :…”

Section: Online Mdp Mddvrpsrc Lookahead Approachmentioning

confidence: 99%

A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm

et al. 2021

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A matheuristic approach based on a reduced two-stage Stochastic Integer Linear Programming (SILP) model is presented. The proposed approach is suitable for obtaining a policy constructed dynamically on the go during the rollout algorithm. The rollout algorithm is part of the Approximate Dynamic Programming (ADP) lookahead solution approach for a Markov Decision Processes (MDP) framed Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity (MDDVRPSRC). First, a Deterministic Multi-Depot VRP with Road Capacity (D-MDVRPRC) is presented. Then an extension, MDVRPSRC-2S, is presented as an offline two-stage SILP model of the MDDVRPSRC. These models are validated using small simulated instances with CPLEX. Next, two reduced versions of the MDVRPSRC-2S model (MDVRPSRC-2S1 and MDVRPSRC-2S2) are derived. They have a specific task in routing: replenishment and delivering supplies. These reduced models are to be utilised interchangeably depending on the capacity of the vehicle, and repeatedly during the execution of rollout in reinforcement learning. As a result, it is shown that a base policy consisting of an exact optimal decision at each decision epoch can be obtained constructively through these reduced two-stage stochastic integer linear programming models. The results obtained from the resulting rollout policy with CPLEX execution during rollout are also presented to validate the reduced model and the matheuristic algorithm. This approach is proposed as a simple implementation when performing rollout for the lookahead approach in ADP.

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Section: Online Mdp Mddvrpsrc Lookahead Approachmentioning

confidence: 99%

A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm

et al. 2021

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“…To prevent from low quality solution, non-improving moves are probabilistically accepted in an attempt. With its simple logic, the cyclic order neighbourhoods can be utilized in many problems such as the VRP and the set covering problem [27,28]. SA is a hill climbing algorithm but it is slower than a simple hill climbing procedure thus it needs longer computation time.…”

Section: Tabu Search and Sa Comparisons With The Proposed Structuresmentioning

confidence: 99%

An optimization algorithm for a capacitated vehicle routing problem with time windows

Kirci

2016

Sādhanā

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In this paper, vehicle routing problem (VRP) with time windows and real world constraints are considered as a real-world application on google maps. Also, tabu search is used and Hopfield neural networks is utilized. Basic constraints consist of customer demands, time windows, vehicle speed, vehicle capacity and working hours. Recently, cost and on-time delivery are the most important factors in logistics. Thus, the logistic applications attract attention of companies. In logistic management, determining the locations of delivery points and deciding the path are the vital components that should be considered. Deciding the paths of vehicles provides companies to use their vehicles efficiently. And with utilizing optimized paths, big amounts of cost and time savings will be gained. The main aim of the work is providing the best path according to the needs of the customers, minimizing the costs with utilizing the VRP and presenting an application for companies that need logistic management. To compare the results, simulated annealing is used on special scenarios. And t-test is performed in the study for the visited path in km with p-value of 0.05.

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“…A return trip to the depot for the purpose of capacity replenishment is required in the event of a route failure and is not permitted otherwise. Although it is possible to consider fixed-route policies that permit replenishment prior to route failures (e.g., in the spirit of Bertsimas et al (1995)), preemptive capacity replenishment policies for the VRPSDL are computationally challenging to evaluate (Goodson, 2010). In §5, we discuss how we consider preemptive capacity replenishments via our rollout policies.…”

Section: Fixed-route Policiesmentioning

confidence: 99%

Rollout Policies for Dynamic Solutions to the Multivehicle Routing Problem with Stochastic Demand and Duration Limits

Goodson

Ohlmann

Thomas

2013

Operations Research

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We develop a family of rollout policies based on fixed routes to obtain dynamic solutions to the vehicle routing problem with stochastic demand and duration limits (VRPSDL). In addition to a traditional one-step rollout policy, we leverage the notions of the pre-and post-decision state to distinguish two additional rollout variants. We tailor our rollout policies by developing a dynamic decomposition scheme that achieves high quality solutions to large problem instances with reasonable computational effort. Computational experiments demonstrate that our rollout policies improve upon the performance of a rolling horizon procedure and commonly employed fixed-route policies, with improvement over the latter being more substantial.

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Solution methodologies for vehicle routing problems with stochastic demand

Cited by 7 publications

References 74 publications

A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm

A Multi-Depot Vehicle Routing Problem with Stochastic Road Capacity and Reduced Two-Stage Stochastic Integer Linear Programming Models for Rollout Algorithm

An optimization algorithm for a capacitated vehicle routing problem with time windows

Rollout Policies for Dynamic Solutions to the Multivehicle Routing Problem with Stochastic Demand and Duration Limits

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