scholarly journals Joint optimization of green vehicle scheduling and routing problem with time-varying speeds

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0192000 ◽  
Author(s):  
Dezhi Zhang ◽  
Xin Wang ◽  
Shuangyan Li ◽  
Nan Ni ◽  
Zhuo Zhang
Keyword(s):  
Vehicle Scheduling ◽  
Joint Optimization ◽  
Time Varying ◽  
Routing Problem

scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

An Optimization Model for Vehicle Scheduling and Routing Problem

2021 ◽  
pp. 630-638
Author(s):  
Tunay Tokmak ◽  
Mehmet Serdar Erdogan ◽  
Yiğit Kazançoğlu
Keyword(s):  
Optimization Model ◽  
Vehicle Scheduling ◽  
Routing Problem

Joint Optimization of Economic Project Life and Well Controls

SPE Journal ◽  
2017 ◽  
Vol 23 (02) ◽  
pp. 482-497 ◽  
Author(s):  
Mehrdad G. Shirangi ◽  
Oleg Volkov ◽  
Louis J. Durlofsky
Keyword(s):  
Production Optimization ◽  
Rate Of Return ◽  
Joint Optimization ◽  
Time Varying ◽  
Outer Loop ◽  
Loop Optimization ◽  
Contract Duration ◽  
Hurdle Rate ◽  
Project Life ◽  
The Impact

Summary A new methodology for the joint optimization of optimal economic project life (EPL) and time-varying well controls is introduced. The procedure enables the maximization of net present value (NPV) subject to satisfaction of a specified modified internal rate of return (MIRR). Knowledge of the economic project life enables the operator to plan for infill drilling or some other type of field development in the case that the lease/contract duration is longer than the optimal project life. This will enable NPV to be maximized, and the hurdle rate to be honored, over the entire duration of the lease. The optimization is formulated as a nested procedure in which economic project life is optimized in the outer loop, and the associated well settings [time-varying bottomhole pressures (BHPs) in the cases considered] are optimized in the inner loop. The inner-loop optimization is accomplished by use of an adjoint-gradient-based approach, while the outer-loop optimization entails an interpolation technique. The successful application of this framework for production optimization for 2D and 3D reservoir models under waterflood is demonstrated. The tradeoff between maximized NPV and rate of return is assessed, as is the impact of discount rate on optimal operations. In the second example, we illustrate the advantages of initiating a new project (that satisfies the hurdle rate) once the EPL is reached. Taken in total, the results in this paper demonstrate the importance of explicitly incorporating both NPV and rate of return in production-optimization formulations.

scholarly journals An Adaptive Memetic Algorithm for Dynamic Electric Vehicle Routing Problem with Time-Varying Demands

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Na Wang ◽  
Yihao Sun ◽  
Hongfeng Wang
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Dynamic Environment ◽  
Time Varying ◽  
Routing Problem ◽  
Battery Capacity ◽  
Encoding Strategy ◽  
Local Search Operator

Dynamic electric vehicle routing problem (DEVRP) is an extension of the electric vehicle routing problem (EVRP) into dynamic logistical transportation system such that the demand of customer may change over time. The routing decision of DEVRP must concern with the driving range limitation of electric vehicle (EV) in a dynamic environment since both load degree and battery capacity are variable according to the time-varying demands. This paper proposes an adaptive memetic algorithm, where a special encoding strategy, an adaptive local search operator, and an economical random immigrant scheme are employed in the framework of evolutionary algorithm, to solve DEVRP efficiently. Numeric experiments are carried out upon a series of test instances that are constructed from a stationary VRP benchmark. The computational results show that the proposed algorithm is more effective in finding high-quality solution than several peer algorithms as well as significant in improving the capacity of the routing plan of EVs in dynamic transportation environment.

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