A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority

Ghannadpour, Seyed Farid; Noori, Siamak; Tavakkoli–Moghaddam, Reza

doi:10.1007/s10878-012-9564-x

Cited by 35 publications

(11 citation statements)

References 44 publications

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“…The validity of the model is tested on some randomly generated data sets. Due to the proposed model belongs to NP-hard problems (Ghannadpour et al, 2014); the required time to solve large-sized problems is too much (more than 6 hours). Thus, simulated annealing (SA) and genetic algorithm (GA) are used to obtain a near-optimal solution in a reasonable computational time.…”

Section: Solution Proceduresmentioning

confidence: 99%

“…Afshar-Bakeshloo et al (2016) presented the mixedinteger linear program (MILP) model in the VRPTW and considered the customer satisfaction and pollution in addition total distance travel and the total required number of vehicles. Ghannadpour et al (2014) presented a mathematical model in VRPTW by considering the uncertainty in a customers' request. The main aim of the presented model is to minimize the total distance travel and the required number of vehicles and maximize the customers' satisfaction simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Yousefi¹,

Tavakkoli–Moghaddam²,

Oliaei³

et al. 2017

10.5267/j.ijiec

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A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer's satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA) and genetic algorithm (GA), are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.

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Section: Solution Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Yousefi¹,

Tavakkoli–Moghaddam²,

Oliaei³

et al. 2017

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Another aspect that has received attention is the integration of technological innovations in Vehicle Routing Problem (VRP) solutions. These include global location systems using computer data processing with high capacity and various techniques, which can be used to solve the model problem, such as the local search-based meta-heuristic technique, Genetic Algorithm (GA), and Tabu Search (TS) [11,12]. The Vehicle Routing Problem is basically about mathematical models to solve the problem to achieve an optimal solution which is the minimum total cost.…”

Section: Introductionmentioning

confidence: 99%

Congestion and Pollution, Vehicle Routing Problem of a Logistics Provider in Thailand

Moryadee¹,

Aunyawong²,

Shaharudin³

2019

TOTJ

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Aim and Objective:This study aims to minimise the travelling distance, operation cost in terms of fuel consumption, and CO 2 emissions. It introduces the Time-Dependency Pollution-Routing Problem (TDPRP) with the implementation of the time-dependency and emission model, including constraints such as the limitation of vehicle capacity and vehicle's speed during different time periods in Thailand. Furthermore, the time window constraint is applied for representing a more realistic model. The main objective is to minimise the total pollution generated because of transportation. Methods:The Genetic Algorithm (GA) and Tabu Search (TS) methods have been used to generate the optimal solution with a variety of experiments. The best solutions from all the experiments have been compared to the original solution in terms of the quality of the solution and the computation time. Results:The best solution was generated by using the TS method with 30,000 trials. The minimum of the total CO 2 emissions was 183.9846 kilograms produced from all of the vehicles during transportation, nearly half from the current transportation plan, which produced 320.94 kilograms of CO 2 emissions. Conclusion:The proposed model optimised both the route and schedules (multiple time periods) for a number of vehicles, for which the transportation during a fixed congestion period could be predicted to avoid traffic congestion and reduce the CO 2 emission. Future research is suggested to add other specific algorithms as well as constraints in order to make the model more realistic.at about 78% of the emissions from all transport modes [2]. CO 2 emissions are the result of fuel consumption and vehicle speed (travelling time). Transportation is the fastest-growing major contributor to global climate change, accounting for 23% of energy-related carbon dioxide (CO 2 ) emissions. Many experts foresee a three-to five-fold increase in CO 2 emissions from transportation in Asian countries by 2030 compared with emissions in 2000 if no changes are made to investment strategies and policies [3]. Traffic congestion directly affects the vehicle speed and the total time spent on the road. In addition, the fuel consumption rate depends on two factors, which are the speed of the vehicle and the load of the vehicle [4].

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“…One of the most important and widely studied combinatorial optimization problems in this area is the vehicle routing problem with time windows (VRPTW). The literature of the VRPTW, due to its inherent complexities and usefulness in real life is rich in different models and solution approaches (Chiang & Hsu 2014, Blaseiro et al 2011, Dhahri et al 2014, Ghannadpour et al 2014, Lin 2011, Mavrovouniotis & Yang 2015, Tan et al 2006and Feng & Liao 2014.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Vehicle Routing Problem with Time Windows and Fuel Consumption Minimizing

Ghannadpour

Hooshfar²

2016

Proceedings of 5th the International Conference on Operations Research and Enterprise Systems

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Transportation often represents the most important single element in logistics costs and its reduction and finding the best routes that a vehicle should follow through a network is an important decision. the energy cost is a significant part of total transportation cost and it is important to improve the operational efficiency by decreasing energy consumption. Unlike most of the studies trying to minimize the cost by minimizing overall travelling distance, the energy minimizing which meets the latest requirements of green logistics, is considered in this paper. the customers' priority for servicing is considered as well. Besides, the model is interpreted as multi-objective optimization where, the energy consumed and the total fleet are minimized and the total satisfaction rates of customers is maximized. A new solution based on the evolutionary algorithm is proposed and its performance is compared with the CPLEX Solver. Results illustrate the efficiency and effectiveness of proposed approach.

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A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority

Cited by 35 publications

References 44 publications

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Congestion and Pollution, Vehicle Routing Problem of a Logistics Provider in Thailand

Multi-Objective Vehicle Routing Problem with Time Windows and Fuel Consumption Minimizing

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