Heuristics for vehicle routing problems with backhauls, time windows, and 3D loading constraints

Reil, Sebastian; Bortfeldt, Andreas; Mönch, Lars

doi:10.1016/j.ejor.2017.10.029

Cited by 71 publications

(29 citation statements)

References 42 publications

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“…(i) VRPB (VRP with backhauls) specifies that vehicles must serve all linehaul customers before serving the backhaul customers [8,10,11]. (ii) VRPMPD (VRP with mixed pickups and deliveries) allows vehicles to serve both linehaul and backhaul in any order [7,12].…”

Section: Pdp Vrp With Pickup and Delivery And Vrp Withmentioning

confidence: 99%

Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

Leelertkij

Parthanadee

Buddhakulsomsiri

2021

Journal of Advanced Transportation

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This paper presents a new variant of vehicle routing problem with paired transshipment demands (VRPT) between retail stores (customers) in addition to the regular demand from depot to retail stores. The problem originates in a real distribution network of high-end retail department stores in Thailand. Transshipment demands arise for one-order-per-season expensive items, whose inventories at the depot may become shortage after the middle of a season, while they remain available at some retail stores. A transshipment demand is a request for items that need to be picked up from a specific store that has the items and delivered to the store that requests the items. The objective of solving the VRPT is to find delivery routes that can satisfy both regular demands and transshipment demands in the same routes without incurring too much additional transportation distance. A mixed integer linear programming model is formulated to represent the VRPT. Six small problem instances are used to test the model. A hybrid threshold accepting and neighborhood search heuristic is also developed to solve large problem instances of VRPT. The heuristic is further extended to include a forbidden list of transshipment demands that should not be included in the same routes. The purpose is to prevent incurring too much additional distance from satisfying transshipment demands. With the forbidden list, the problem becomes vehicle routing problem with optional transshipment demands (VRPOT). Computational testing shows promising results that indicate effectiveness of the proposed hybrid heuristics as well as the forbidden list.

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Section: Pdp Vrp With Pickup and Delivery And Vrp Withmentioning

confidence: 99%

Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

Leelertkij

Parthanadee

Buddhakulsomsiri

2021

Journal of Advanced Transportation

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“…Reference [30] proposed a column generation technique-based heuristic for 3L-CVRP and applied an efficient heuristic pricing method to speed up the column generation. Reference [31] and [32] investigated VRPs with backhauls, time windows, and 3D loading constraints. Reference [33], [34] extended the classic pickup and delivery problem to integrate routing and 3D loading problems (3L-PDP).…”

Section: Related Workmentioning

confidence: 99%

A Double Traveling Salesman Problem With Three-Dimensional Loading Constraints for Bulky Item Delivery

et al. 2021

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This article considers bulky item delivery problems in which multiple items are retrieved and loaded onto a vehicle from different warehouses and then delivered. This problem is described as a double traveling salesman problem with three-dimensional container loading constraints with multiple stacks. The double TSP with multiple stacks is used to determining the shortest route performing pickups and deliveries in two separated networks (one for pickups and one for deliveries) using only one container. Repacking is not allowed after loading the items into the container. An integer linear programming model is proposed to solve this problem, a standard genetic algorithm and an improved genetic algorithm is designed. In the improved genetic algorithm, a Lin-Kernighan algorithm is used to improve the delivery route, a k-means clustering algorithm, and a heuristic packing scheme improvement rules work together to improve the loading route. The results show that the improved genetic algorithm is superior to the standard genetic algorithm in large scale problems.INDEX TERMS Double traveling salesman problem, three-dimensional container loading, bulky item delivery, genetic algorithm.

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“…Junqueira et al (2011) applied an MIP-based approach for the container loading problem with multi-drop constraints, which is a simplified 3L-CVRP. Metaheuristic approaches for the 3L-CVRP can be classified as: TS (Gendreau and Martello 2006;Tarantilis et al 2009;Bortfeldt 2012;Zhu et al 2012;Ruan et al 2013;Tao and Wang 2015;Reil et al 2018), ACO (Fuellerer et al 2010), Genetic Algorithm (GA) (Moura 2008;Moura and Oliveira 2009), VNS (Wei et al 2014) and Evolutionary Local Search (ELS) (Zhang et al 2015). Most recently Vega-Mejia et al 2019have extended an existing vehicle routing problem with loading constraints (VRPLC) optimization model to a nonlinear optimization model that considers weight-bearing strength of three-dimensional items, vehicle weight capacity, weight distribution inside vehicles, delivery time windows, and a balanced fleet of vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective vehicle routing and loading with time window constraints: a real-life application

et al. 2019

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Motivated by a real-life application, this research considers the multi-objective vehicle routing and loading problem with time window constraints which is a variant of the Capacitated Vehicle Routing Problem with Time Windows with one/two-dimensional loading constraints. The problem consists of routing a number of vehicles to serve a set of customers and determining the best way of loading the goods ordered by the customers onto the vehicles used for transportation. The three objectives pertaining to minimisation of total travel distance, number of routes to use and total number of mixed orders in the same pallet are, more often than not, conflicting. To achieve a solution with no preferential information known in advance from the decision maker, the problem is formulated as a Mixed Integer Linear Programming (MILP) model with one objective-minimising the total cost, where the three original objectives are incorporated as parts of the total cost function. A Generalised Variable Neighbourhood Search (GVNS) algorithm is designed as the search engine to relieve the computational burden inherent to the application of the MILP model. To evaluate the effectiveness of the GVNS algorithm, a real instance case study is generated and solved by both the GVNS algorithm and the software provided by our industrial partner. The results show that the suggested approach provides solutions with better overall values than those found by the software provided by our industrial partner.

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Heuristics for vehicle routing problems with backhauls, time windows, and 3D loading constraints

Cited by 71 publications

References 42 publications

Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

A Double Traveling Salesman Problem With Three-Dimensional Loading Constraints for Bulky Item Delivery

Multi-objective vehicle routing and loading with time window constraints: a real-life application

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