Capacitated vehicle routing problem with pick-up and alternative delivery (CVRPPAD): model and implementation using hybrid approach

Sitek, Paweł; Wikarek, Jarosław

doi:10.1007/s10479-017-2722-x

Cited by 87 publications

(29 citation statements)

References 28 publications

(28 reference statements)

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“…It is defined by Ozbaygin et al [2017] and solved to optimality by a Branch-and-Price (B&P) algorithm for instances with up to 60 customers. Sitek and Wikarek [2017] define the Capacitated VRP with Pick-up and Alternative Delivery (CVRPPAD). This paper considers multiple options for each parcel to be delivered.…”

Section: Closely Related Problemsmentioning

confidence: 99%

A large neighborhood search approach to the vehicle routing problem with delivery options

Dumez

Lehuédé

Péton

2021

Transportation Research Part B: Methodological

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To reduce delivery failures in last mile delivery, several types of delivery options have been proposed in the past twenty years. Still, customer satisfaction is a challenge because a single location is chosen independently of the time at which customers will be delivered. In addition, delivery in shared locations such as lockers and shops also experience failure due to capacity or opening-time issues at the moment of delivery. To address this issue and foster consolidation in shared delivery points, we investigate the case where a customer can specify several delivery options together with preference levels and time windows. We define, in this article, the Vehicle Routing Problem with Delivery Options, which integrates several types of delivery locations. It consists of designing a set of routes for a fleet of vehicles that deliver to each customer at one of his/her options during the corresponding time window. These routes should respect capacities at shared locations such as lockers and minimum service level requirements, while minimizing the total routing costs. To solve this problem, we have designed a large neighborhood search in which a set partitioning problem is solved to regularly reassemble routes. Specific ruin and recreate operators are proposed and combined with numerous operators from the literature. A thorough experimental study was carried out to determine a subset of efficient and complementary operators. The proposed method outperforms existing algorithms from the literature on particular cases of the problem under consideration, such as the vehicle routing problem with roaming delivery locations and the vehicle routing problem with home and roaming delivery locations. New instances are generated and used both to serve as a benchmark and to propose some managerial insight into the vehicle routing problem with alternative delivery options.

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Section: Closely Related Problemsmentioning

confidence: 99%

A large neighborhood search approach to the vehicle routing problem with delivery options

Dumez

Lehuédé

Péton

2021

Transportation Research Part B: Methodological

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“…An optimization model implemented in a hybrid environment that contains constraint logic programming, mathematical programming, and metaheuristics for the capacitated vehicle routing problem with pick‐up and alternative delivery is presented in (Sitek, 2019). The zero‐inventory approach, which has been widely adopted by many automobile companies, seems to be impractical in certain real production contexts as it requires an inventory of all parts but in low amounts.…”

Section: Materials Flow and Related Workmentioning

confidence: 99%

Modelling material flow using the Milk run and Kanban systems in the automotive industry

Simić

Svirčević²,

Corchado

et al. 2020

Expert Systems

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Material flow management refers to the analysis and specific optimization of the inventory-production system. Material flow can be characterized as the organized flow of material in a production process with the required sequence determined by a technological procedure. The Milk run system assures the transportation of materials at the right time and in an optimal manner. It should be combined with the Kanban system to highlight when something is required in the production process. This paper presents biological swarm intelligence, in general, and a particular model, particle swarm optimization (PSO), for modelling material flow using a Milk run system supported by a Kanban system in the automotive industry. The aim of this study is to create a new model for the optimal number of trailers for one train and optimal number of containers in a tugger train system when the route time period has been defined. A new modified PSO approach for integrating inventory-production in a unique optimization model is used. The major modification to the original PSO is using the capacity of a container instead of a velocity component. Each new Kanban trigger is checked, and the total timing for the Milk run delivery solution is calculated for the necessary raw material capacity for each shop floor.

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“…Gendreau et al [2006] used neighborhood search heuristics to optimize the planned routes of vehicles in the context where new requests with a pick-up and a delivery location occur in real time. Sitek and Wikarek [2017] presented an optimization model implemented in hybrid environments that contain constraint logic programming, mathematical programming, and metaheuristics for the capacitated vehicle routing problem with pick-up and alternative delivery. However, in the automotive industry considered in our study, the assembly processes of different cars are almost the same and the production of cars is different from other kinds of products that have many production lines and high set-up costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Milk-run routing problem with progress-lane in the collection of automobile parts

et al. 2019

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In recent years, the automotive industry has faced an unprecedented crisis. In particular, the zero-inventory approach, which has been widely pursued by many automobile companies, seems to be impractical in some real production contexts since it requires an inventory of all parts but in low amounts. In this paper, we investigate a new logistics method which collects automobile parts by integrating the progress-lane (P-LANE) into the corresponding vehicle routing problem. We propose a mixed integer programming formulation for this new model, which can simultaneously determines the trip routes to collect automobile parts, as well as the PLANE that each collected part should be assigned to, so as to minimize the total costs of the production and inbound logistics. The comparison with the zero-inventory model shows that the use of the PLANE within the milk-run system could significantly decrease the total costs and also improve the transportation efficiency. To be specific, for small and large size instances, the total costs of the zero-inventory model are about 10% and 30% higher than the ones with PLANE , respectively, which suggests that the periodic part collection model with PLANE could be more appropriate for automobile manufacturing.

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Capacitated vehicle routing problem with pick-up and alternative delivery (CVRPPAD): model and implementation using hybrid approach

Cited by 87 publications

References 28 publications

A large neighborhood search approach to the vehicle routing problem with delivery options

A large neighborhood search approach to the vehicle routing problem with delivery options

Modelling material flow using the Milk run and Kanban systems in the automotive industry

Milk-run routing problem with progress-lane in the collection of automobile parts

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