Constraint Cellular Ant Algorithm for the Multi-Objective Vehicle Routing Problem

Wang, Yuanzhi

doi:10.4304/jsw.8.6.1339-1345

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…The objective function (1) minimizes the total number of vehicles, the objective function (2) minimizes the total travel distances, and the objective function (3) minimizes the total waiting time of the vehicles. Constraints (4) represent that each request is assigned to only one vehicle. Constraints (5) guarantee that each job is visited exactly once.…”

Section: B Mathmatical Modelmentioning

confidence: 99%

“…Since the Vehicle Routing Problem (VRP) was proposed by Dantzig and Ramser [1], it has been a hot issue in the field of management science and operational research. Due to the wide applications in the logistics management and transportation management [2][3][4][5], a lot of new constraints is added to the classical VRP and generated new problems, e.g. Vehicle Routing Problem with Backhauls (VRPB), Vehicle Routing Problem with Time Windows (VRPTW) [6], Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) [7] and Pickup and Delivery Problem with Time Windows (PDPTW), etc.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Particle Swarm Optimization Algorithm for Multi-Objective Pickup and Delivery Problem with Time Windows

Zou¹,

Jin²,

Li³

2013

JCP

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This paper studies the multi-objective pickup and delivery problem with time windows (PDPTW), in which a fleet of homogeneous vehicles with the same capacities located in a depot serve a collection of given transportation requests. Each request is composed of a pickup location, a delivery location and a given load. The PDPTW is to determine a vehicle scheduling strategy with the objectives of minimizing the number of vehicles utilized, the total travel distances and the total waiting times. A mixed integer programming model is built to formulate this multiobjective PDPTW. Then a novel hybrid particle swarm optimization (HPSO) is proposed to solve this problem. This algorithm adds particles neighbor information to diversify the particle swarm and use the variable neighborhood search (VNS) to enhance the convergence speed. Finally, some numerical experiments based on existing benchmark instances are given to show the effectiveness and feasibility of the algorithm.

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Section: B Mathmatical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization Algorithm for Multi-Objective Pickup and Delivery Problem with Time Windows

Zou¹,

Jin²,

Li³

2013

JCP

View full text Add to dashboard Cite

show abstract

“…Although evolutionary computation methods provide solutions combining computational efficiency and good performance, evolutionary computational research has been criticized for considering artificial test problem scenarios that are much less complex than real-world manufacturing cases [4]. Multi-objective optimization is the concurrent technique that defines more than one contrasting goal under specific restrictions [5]. Use (MOEA) to solve multifunctional optimization issues.…”

Section: Introductionmentioning

confidence: 99%

Solving multi-objective master production schedule problem using memetic algorithm

Sadiq

Abdulazeez

Haron

2020

IJEECS

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<span>A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. </span>

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“…Multi-objective optimization is a procedure for optimizing two or more conflicting objectives simultaneously subject to certain constraints [1]. Some examples of multi-objective problems are maximizing the classification accuracy of both majority and minority classes, minimizing both tree size and mean squared error in regression problems and minimizing manufacturing cost and maximizing fuel efficiency in vehicle manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Survey of multi objective evolutionary algorithms

Vachhani

Dabhi

Prajapati

2015

2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]

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Multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. Multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA. MOEA generates a set of non-dominated solutions at the end of run, which is called Pareto set. The Pareto front contains set of Pareto solutions. Any MOEA aims to improve (i) convergence of population towards true Pareto front and (ii) diversity of solutions belonging to Pareto set. Generally, an external archive is used by MOEAs to maintain a set of non-dominated Pareto set solutions. Sometimes, Pareto set contains more number of solutions than the size of archive. This paper presents survey of various methods used by different MOEAs for reducing the size of Pareto set while maintaining solutions diversity. It presents comparison of these methods along with their advantages and disadvantages. The paper concludes by giving limitation of crowding distance based method in various scenarios.

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Constraint Cellular Ant Algorithm for the Multi-Objective Vehicle Routing Problem

Cited by 5 publications

References 6 publications

A Hybrid Particle Swarm Optimization Algorithm for Multi-Objective Pickup and Delivery Problem with Time Windows

A Hybrid Particle Swarm Optimization Algorithm for Multi-Objective Pickup and Delivery Problem with Time Windows

Solving multi-objective master production schedule problem using memetic algorithm

Survey of multi objective evolutionary algorithms

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