Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries (original) (raw)
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VRPBench: A Vehicle Routing Benchmark Tool
2016
The number of optimization techniques in the combinatorial domain is large and diversified. Nevertheless, there is still a lack of real benchmarks to validate optimization algorithms. In this work we introduce VRPBench, a tool to create instances and visualize solutions to the Vehicle Routing Problem (VRP) in a planar graph embedded in the Euclidean 2D space. We use VRPBench to model a real-world mail delivery case of the city of Artur Nogueira. Such scenarios were characterized as a multi-objective optimization of the VRP. We extracted a weighted graph from a digital map of the city to create a challenging benchmark for the VRP. Each instance models one generic day of mail delivery with hundreds to thousands of delivery points, thus allowing both the comparison and validation of optimization algorithms for routing problems.
Operations Research/Computer Science Interfaces, 2008
Multi-objective optimization knows a fast growing interest for both academic researches and real-life problems. An important domain is the one of vehicle routing problems. In this chapter, we present the possible motivations for applying multi-objective optimization on vehicle routing problems and the potential uses and benefits of doing so. To illustrate this fact, we also describe two problems, namely the vehicle routing problem with route balancing and the bi-objective covering tour problem. We also propose a two-phased approach based on the combination of a multi-objective evolutionary algorithm and single-objective techniques that respectively provide diversification and intensification for the search in the objective space. Examples of implementation of this method are provided on the two problems.
HAL (Le Centre pour la Communication Scientifique Directe), 2008
Multi-objective optimization knows a fast growing interest for both academic researches and real-life problems. An important domain is the one of vehicle routing problems. In this chapter, we present the possible motivations for applying multi-objective optimization on vehicle routing problems and the potential uses and benefits of doing so. To illustrate this fact, we also describe two problems, namely the vehicle routing problem with route balancing and the bi-objective covering tour problem. We also propose a two-phased approach based on the combination of a multi-objective evolutionary algorithm and single-objective techniques that respectively provide diversification and intensification for the search in the objective space. Examples of implementation of this method are provided on the two problems.
Multi-objective vehicle routing and loading with time window constraints: a real-life application
Annals of Operations Research, 2019
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.
Expert Systems with Applications, 2013
The Capacitated Vehicle Routing Problem with Time Windows (VRPTW) consists in determining the routes of a given number of vehicles with identical capacity stationed at a central depot which are used to supply the demands of a set of customers within certain time windows. This is a complex multiconstrained problem with industrial, economic, and environmental implications that has been widely analyzed in the past. This paper deals with a multi-objective variant of the VRPTW that simultaneously minimizes the travelled distance and the imbalance of the routes. This imbalance is analyzed from two perspectives: the imbalance in the distances travelled by the vehicles, and the imbalance in the loads delivered by them. A multi-objective procedure based on Simulated Annealing, the Multiple Temperature Pareto Simulated Annealing (MT-PSA), is proposed in this paper to cope with these multi-objective formulations of the VRPTW. The procedure MT-PSA and an island-based parallel version of MT-PSA have been evaluated and compared with, respectively, sequential and island-based parallel implementations of SPEA2. Computational results obtained on Solomon's benchmark problems show that the island-based parallelization produces Pareto-fronts of higher quality that those obtained by the sequential versions without increasing the computational cost, while also producing significant reduction in the runtimes while maintaining solution quality. More specifically, for the most part, our procedure MT-PSA outperforms SPEA2 in the benchmarks here considered, with respect to the solution quality and execution time.
Multi-objective optimisation for the vehicle routing problem using metaheuristics
International Journal of Enterprise Network Management, 2018
The capacitated vehicle routing problem is a combinatorial optimisation problem that determines a set of routes of minimum distance to deliver the goods, using a fleet of identical vehicles with restricted capacity. The objective of this article it to optimise the total distance required to deliver the goods and also the workload imbalance in terms of distances travelled by the vehicles and their loads. Due to the combinatorial in nature, it requires metaheuristic to solve these types of problems and this is a rapidly growing field of research. Here two metaheuristics such as ant colony optimisation (ACO) and simulated annealing (SA) are proposed and analysed for solving this multi-objective formulation of the vehicle routing problem. The results obtained from these two methods were compared and found that the ACO gives better results than the SA for the VRP.
Journal of Industrial and Systems Engineering, 2017
One of the most important problems for distribution companies is to find the best locations for depots and to find proper routes for transportation vehicles and to optimize supply network. This study intends to develop a model for the problem of location-routing in post offices. So, a new Bi-Objective Location-Routing Problem for Locating Town Post Office and Routing Parcels is defined. This problem is modeled through mixed-integer mathematical programming. The aim of proposed model is to select potential post offices and to find optimal routes for transportation vehicles while time constraints are taken into account. The proposed model is applied in a real case study including eight main post area and 21 regional offices in Tehran, Iran. A goal programming approach is proposed to solve this bi-objective optimization model. The GAMS Software is used to code and solve the associated mathematical model. Some required parameters of the model such as demands are estimated using Geograph...
IEEE transactions on cybernetics, 2015
This paper investigates a practical variant of the vehicle routing problem (VRP), called VRP with simultaneous delivery and pickup and time windows (VRPSDPTW), in the logistics industry. VRPSDPTW is an important logistics problem in closed-loop supply chain network optimization. VRPSDPTW exhibits multiobjective properties in realworld applications. In this paper, a general multiobjective VRPSDPTW (MO-VRPSDPTW) with five objectives is first defined, and then a set of MO-VRPSDPTW instances based on data from the real-world are introduced. These instances represent more realistic multiobjective nature and more challenging MO-VRPSDPTW cases. Finally, two algorithms, multiobjective local search (MOLS) and multiobjective memetic algorithm (MOMA), are designed, implemented and compared for solving MO-VRPSDPTW. The simulation results on the proposed real-world instances and traditional instances show that MOLS outperforms MOMA in most of instances. However, the superiority of MOLS over MOMA in real-world instances is not so obvious as in traditional instances.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
The Vehicle Routing Problem under time window uncertainty A new type of travel time disturbance is constructed to capture the characteristics of the life scenario, and its disturbance region is determined by the maximum disturbance degree Two objectives are defined to minimise the total distance and the number of vehicles An advanced Robust Multi Objective Particle Swarm Optimization approach is developed using advanced coding, decoding model, reliability metrics and local search strategies to identify optimal solutions Due to the particles in the decision space, the properties of the problem space are fully exploited to drive the robust optimization, while the proposed metric measures the robustness of the solutions during the process Further improvements are achieved by problem based local search and route based local search strategies Several robust optimization benchmarks, where perturbations are applied to selected problems, demonstrate that our proposed algorithm can generate sufficiently robust solutions and ensure their optimality.