A Decomposition-Based Local Search for Large-Scale Many-Objective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows

Zhou, Ying; Kong, Long; Cai, Yiqiao; Wu, Ziyan; Liu, Shaopeng; Hong, Jiaming; Wu, Keke

doi:10.1109/jsyst.2019.2959664

Cited by 15 publications

(13 citation statements)

References 57 publications

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“…The neighborhood constitutes a set of similar solutions perturbed by the original one. NS operators are usually applied in a synergistic manner by simultaneously using more than one operator with different neighborhood sizes or search behavior, e.g., [170][171][172][173]. As NS variant, tabu search, which uses attributive memory structures to guide the search away from local optima, has also been used as a complementary operator in MOEA/D [174][175][176][177].…”

Section: Memetic Search Methodsmentioning

confidence: 99%

“…Instead of a steady-state scheme, [102,103] proposed the first generational environmental selection mechanism for MOEA/D. Its basic idea is to transform the environmental selection process as a matching problem among subproblems and solutions, i.e., subproblems aim to find their appropriate [125], DEMPSO [170], MS-GPSO/D w [253], MaPSO [229] MOEA/D-ACO [95], ACO-Index [250], NMOACO/D [168], MACO/D-LOA [228] ACO dMOABC [254], MOCS/D [20], MOGWO [158], MIWOALS [1] Other SI Model-based methods MEDA/D [47,255] EDAs MOEA/D-GM [30] Probabilistic graphic model MOEA/D-PBIL [233] PBIL MACE-gD [53] Cross-entropy method MOEA/D-CMA [151], MO-CMA-D [172],MOEA/D-CMA [112] CMA-ES MOPC/D [161] Probability collectives…”

Section: Environmental Selectionmentioning

confidence: 99%

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Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including weight vector settings, subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

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Section: Memetic Search Methodsmentioning

confidence: 99%

Section: Environmental Selectionmentioning

confidence: 99%

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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“…Wang et al [13] investigated a vehicle routing problem (VRP) with simultaneous delivery and pickup and time windows for five objectives in the logistics industry. Zhou et al [15] proposed a decomposition-based local search algorithm for large-scale VRP with simultaneous delivery and pickup and time windows. A Hamiltonian graph-guided algorithm is designed in [16] to solve the vehicle routing problem.…”

Section: Related Workmentioning

confidence: 99%

Online Routing and Charging Schedule of Electric Vehicles With Uninterrupted Charging Rates

Cao

2022

IEEE Access

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With the development of electric vehicle technology and the Internet of Things (IoT) technology, IoT-Based electric vehicles (IoEVs) will become a main public transposition in smart city. The increasing penetration of IoEV will have a great impact on the transportation network and power grid. In this work, we aim to study the routing and charging scheduling problem of IoEVs with the constraint of power limit. This paper proposes a clustered rolling framework and a mathematical model for the IoEVs optimal routing and charging problem to minimize the waiting time of customers and get a good converge ratio of the energy block with power limit. This framework can effectively deal with the multi-step pickup and delivery of each vehicle over the time horizon in a dynamic environment. We give a criterion to judge whether pickup and delivery tasks can be assigned to the IoEV. Then, we design a new rectangle packing method for IoEV charging dispatch with the constraint of power limit. Moreover, we consider that the charging process of IoEVs cannot be interrupted before IoEVs meet the electricity demand. We propose the IoEV Routing and Charging Algorithm and analyze its computational complexity. Simulation results show that IoEVs can offer the shuttle service to customers with the minimum waiting time and IoEV charging energy block has a good coverage ratio of the total energy block. Simulation results can verify the effectiveness of our proposed framework, compared with two benchmark algorithms.INDEX TERMS IoT-Based electric vehicle, clustered rolling framework, pickup and delivery, routing and charging, rectangle packing algorithm.

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“…Ref. [89] adopts a Benders decomposition based framework for addressing a high-dimensional energy dispatching problem. However, the decomposition approach assumes a natural decomposition of the problem, since it is very subjective in drawing boundaries around physical components and subassemblies in large, highly dependent systems.…”

Section: Addressing Scalability Issue In Smart Gridsmentioning

confidence: 99%

Integrated energy scheduling and routing for a network of mobile prosumers

Alqahtani

2020

Energy

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A Decomposition-Based Local Search for Large-Scale Many-Objective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows

Cited by 15 publications

References 57 publications

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Online Routing and Charging Schedule of Electric Vehicles With Uninterrupted Charging Rates

Integrated energy scheduling and routing for a network of mobile prosumers

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