A local branching matheuristic for the multi-vehicle routing problem with stochastic demands

Hernandez, Florent; Gendreau, Michel; Jabali, Ola; Rei, Walter

doi:10.1007/s10732-018-9392-y

Cited by 14 publications

(10 citation statements)

References 29 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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show abstract

“…This method improves the accuracy of message delivery in VDTNs. Hernandez et al 26 proposed a local branching metaheuristic approach to the vehicle routing problem with stochastic demands (VRPSD) to minimize the sum of planned route cost and the expected resource cost. This method improves routing performance with a high computational complexity.…”

Section: Related Workmentioning

confidence: 99%

An adaptive and secure traffic information forwarding mechanism in Internet of Vehicles

Fan

Duan

et al. 2020

Int J Communication

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SummaryTraffic information of disparate types in Internet of Vehicles (IoV) is the basis for supporting various IoV applications. Since traffic flows vary in real time, it is challenging to carry out efficient and reliable transfer of such information. Moreover, IoV is vulnerable to security threats due to its inherent properties such as dynamically changing topology and high‐speed motion of vehicles. Attacks, once launched successfully, would also disrupt message transmission between vehicles. We propose an adaptive traffic information forwarding mechanism, which divides traffic information in two categories, that is, early warning information and service information. The former is handled by selecting a relay node based on node connection stability evaluation, while the latter is handled by adopting an appropriate forwarding method according to the identified traffic flow density. Specifically, in a low‐density environment, we employ a broadcast method; while in a high‐density environment, we evaluate the cognitive interaction values of vehicle nodes and employ a cognitive interaction‐based method to select a relay node. Simulation results show that the proposed mechanism improves the forwarding efficiency of traffic information and yields satisfactory performance in mitigating black hole attacks.

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“…For the VRP with stochastic demands, a stochastic programming model, composed of a route-planning stage and an execution stage, was introduced in [17]. If a vehicle cannot meet a customer's random demand requested during the execution process, it needs to return to the distribution center for replenishment and resume its planned route at the point of failure.…”

Section: Introductionmentioning

confidence: 99%

“…The objective is to minimize the sum of the planned route cost and the expected recourse cost. A local branching metaheuristic was implemented for the MVRP with stochastic demands in [17].…”

Section: Introductionmentioning

confidence: 99%

Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy

Wang

2022

Technologies

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The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises.

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A local branching matheuristic for the multi-vehicle routing problem with stochastic demands

Cited by 14 publications

References 29 publications

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

An adaptive and secure traffic information forwarding mechanism in Internet of Vehicles

Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy

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