Two meta-heuristics for solving the capacitated vehicle routing problem: the case of the Tunisian Post Office

Sbai, Ines; Krichen, Saoussen; Limam, Olfa

doi:10.1007/s12351-019-00543-8

Cited by 22 publications

(14 citation statements)

References 52 publications

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“…Differential Evolution (DE) algorithm with a triangular mutation operator is proposed to solve the optimization problem [22] and applied to the stochastic programming problems [23]. Many researchers presented the applications of metaheuristic algorithms in different types of problems such as unconstrained function optimization [24], vehicle routing problems [25][26][27], machine scheduling [28,29], mine production schedules [30], project selection [31], soil science [32], feature selection problem [33,34], risk identification in supply chain [35] etc. For constrained optimization problems, Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) was presented and compared to other metaheuristic algorithms [36].…”

Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Agrawal¹,

Alnowibet²,

Mohamed³

2022

Computers, Materials &Amp; Continua

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This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are solved using GSK algorithm and the results are compared with eight other state-of-the-art metaheuristic algorithms. The obtained results are also compared with the optimal global solution and the results quoted in the literature. To investigate the performance of the GSK algorithm on a real-world problem, a solid stochastic fixed charge transportation problem is examined, in which the parameters of the problem are considered as random variables. The obtained results show that the GSK algorithm outperforms other algorithms in terms of convergence, robustness, computational time, and quality of obtained solutions.

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Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Agrawal¹,

Alnowibet²,

Mohamed³

2022

Computers, Materials &Amp; Continua

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“…-Time horizon T = (3, 6); -Number of customers: we will be interested in two sets of instances. The first one contains 50 small instances of the model up to 50 customers when T = 3 and 60 instances up to 200 customers composed of small and large instances when T = 6; -Product quantity r t i consumed by customer i at each period t : randomly generated as an integer number in the interval [10,100]; -Product quantity p t made available at the supplier at each period t :…”

Section: Implementation and Benchmarksmentioning

confidence: 99%

A Memetic Algorithm for the Inventory Routing Problem

Sakhri¹,

Tlili²,

Korbaa³

2021

Preprint

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In this paper, we investigated an Inventory Routing Problem (IRP) with deterministic customer demand, in a two-tiered supply chain. The supply chain network consists of a supplier who uses a single vehicle with a given capacity to deliver a single type of product to many customers. We are interested in population-based algorithms to solve our problem. A Memetic Algorithm (MA) is developed based on Genetic Algorithm (GA) and Variable Neighborhood Search methods (VNS). The proposed metaheuristics are tested on small and large sizes referenced benchmarks. The results of MA are compared with those of classical GA and with the optimal solutions from the literature. The comparison showed the efficiency of the MA use and its ability to generate high quality solutions within a reasonable time.

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“…Since there is no benchmark problem for MDSDVRPs yet, we assessed the competitiveness of our proposed GA by comparison with the Eilon instances. Much research has used these benchmark instances to demonstrate the effectiveness of their algorithms ( [89], [90], [91]), considering the NP-hardness of VRPs. As explained in Section 1, many approximation algorithms and heuristics keep updating the best solutions for these benchmark instances.…”

Section: Experiments Of Benchmark Sdcvrpsmentioning

confidence: 99%

Multi-Depot Split-Delivery Vehicle Routing Problem

2021

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The rapid advancements in information technologies and globalization change the way of distributing goods to customers. Many enterprises have multiple factories, warehouses, and distribution centers and strive for competitive efficiency in the distribution operations to minimize transportation costs. This study proposed the mixed-integer programming (MIP) model for the multi-depot split-delivery vehicle routing problems (MDSDVRPs) with hetero vehicles, allowing multiple visits to a customer. A genetic algorithm (GA) with a novel two-dimensional chromosome representation has been proposed with dynamic mutation policies. The process parameters of the proposed GA are optimized using the Taguchi method. The proposed algorithms showed the benefits of split-delivery in MDSDVRPs and showed the competitive performance even for the classical single-depot vehicle routing problems with no split-delivery.INDEX TERMS Vehicle routing problem (VRP), Multi-depot split-delivery VRP, Genetic algorithm (GA), Taguchi method This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Two meta-heuristics for solving the capacitated vehicle routing problem: the case of the Tunisian Post Office

Cited by 22 publications

References 52 publications

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

A Memetic Algorithm for the Inventory Routing Problem

Multi-Depot Split-Delivery Vehicle Routing Problem

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