Two-phase Matheuristic for the vehicle routing problem with reverse cross-docking

Gunawan, Aldy; Widjaja, Audrey Tedja; Vansteenwegen, Pieter; Yu, Vincent F.

doi:10.1007/s10472-021-09753-3

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…The VRP-FRCD is formally formulated as follows, while the list of decision variables is presented in Table I. This model is an extension of the VRP-RCD [11]. Amount of product k delivered to node i by vehicle v q v∈V 0 Initial load of vehicle v when leaving CD in the supplier delivery and pickup process q v∈V 0 Initial load of vehicle v when leaving CD in the retailer delivery and pickup process q v∈V 0 Initial load of vehicle v when leaving CD in the outlet delivery and pickup process q i∈S , q i∈R , q i∈O Amount of load remaining in the vehicle upon visiting node i T sdpmax…”

Section: Fig 1 An Illustration Of Vrp-frcdmentioning

confidence: 99%

“…Constraint (10) ensures the outflow and inflow of a vehicle. Constraint (11) ensures that each supplier is visited at most once. The amount of products to be delivered to each supplier is calculated by Constraints ( 12) and (13).…”

Section: Fig 1 An Illustration Of Vrp-frcdmentioning

confidence: 99%

“…Let D = {random removal (D 1 ), worst removal (D 2 ), route removal (D 3 ), node pair removal (D 4 ), worst pair removal (D 5 ), shaw removal (D 6 )} be the set of DESTROY operators and I = {Greedy insertion (I 1 ), k-regret insertion (I 2 , I 3 , I 4 ), Greedy insertion with noise function (I 5 ), k-regret insertion with noise function (I 6 , I 7 , I 8 ), GRASP insertion (I 9 )} be the set of REPAIR operators. More details of all operators can be found in [11].…”

Section: The Proposed Matheuristicmentioning

confidence: 99%

“…In previous work [9], we studied the VRP with reverse cross-docking (VRP-RCD) that models a four-level supply chain network consisting of suppliers, customers (more appropriate to be addressed as retailers), outlets, and a cross-dock facility. In [10] and [11], we revisited this VRP-RCD and designed an adaptive large neighborhood search (ALNS) algorithm and matheuristic to effectively solve the problem, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed VRP-FRCD is formally presented as a mixed integer linear programming model. Then we design a two-phase matheuristic including an ALNS algorithm (adopted from [10]) and set partitioning (adopted from [11]) to effectively solve the problem.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Routing Problem with Forward and Reverse Cross-Docking: Formulation and Matheuristic Approach

Gunawan

Widjaja

Vansteenwegen

et al. 2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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This paper studies an integration of the forward products flow, as in the vehicle routing problem with crossdocking (VRPCD), and the reverse products flow, as in the VRP with reverse cross-docking (VRP-RCD), namely the VRP with forward and reverse cross-docking (VRP-FRCD). It is modelled as a mixed-integer linear programming model that covers a four-level supply chain network, including suppliers, retailers, outlets, and a cross-dock facility. The objective is to determine the appropriate number of vehicles used to complete the delivery process, together with the route sequence of every vehicle, such that the total fixed vehicle and distance-related costs are minimized. In order to solve the problem, a two-phase matheuristic is developed. The first phase focuses on finding as many route combinations as possible. This is implemented by an adaptive large neighborhood search (ALNS) algorithm. Subsequently, a set partitioning formulation is formulated and solved in the second phase to determine the best combination over all routes found in the first phase that minimizes the total cost. The performance of the matheuristic in solving the newly developed benchmark instances is compared against those of CPLEX and a pure ALNS algorithm. Experimental results suggest that the matheuristic is more powerful and beneficial than both ALNS and CPLEX to obtain high-quality solutions within an acceptable computational time.

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Section: Fig 1 An Illustration Of Vrp-frcdmentioning

confidence: 99%