This page has only limited features, please log in for full access.

Dr. Longlong Leng
Zhejiang University of Technology

Basic Info


Research Keywords & Expertise

0 Logistics
0 Optimization Algorithms
0 algorithm
0 Hyperheuristics
0 Location-routing problem

Fingerprints

Location-routing problem
Logistics
algorithm
Hyperheuristics

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Research article
Published: 22 June 2021 in Complexity
Reads 0
Downloads 0

In practice, the parameters of the vehicle routing problem are uncertain, which is called the uncertain vehicle routing problem (UVRP). Therefore, a data-driven robust optimization approach to solve the heterogeneous UVRP is studied. The uncertain parameters of customer demand are introduced, and the uncertain model is established. The uncertain model is transformed into a robust model with adjustable parameters. At the same time, we use a least-squares data-driven method combined with historical data samples to design a function of robust adjustable parameters related to the maximum demand, demand range, and given vehicle capacity to optimize the robust model. We improve the deep Q-learning-based reinforcement learning algorithm for the fleet size and mix vehicle routing problem to solve the robust model. Through test experiments, it is proved that the robust optimization model can effectively reduce the number of customers affected by the uncertainty, greatly improve customer satisfaction, and effectively reduce total cost and demonstrate that the improved algorithm also exhibits good performance.

ACS Style

Jingling Zhang; Mengfan Yu; Qinbing Feng; Longlong Leng; Yanwei Zhao. Data-Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty. Complexity 2021, 2021, 1 -19.

AMA Style

Jingling Zhang, Mengfan Yu, Qinbing Feng, Longlong Leng, Yanwei Zhao. Data-Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty. Complexity. 2021; 2021 ():1-19.

Chicago/Turabian Style

Jingling Zhang; Mengfan Yu; Qinbing Feng; Longlong Leng; Yanwei Zhao. 2021. "Data-Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty." Complexity 2021, no. : 1-19.

Journal article
Published: 17 July 2020 in Journal of Cleaner Production
Reads 0
Downloads 0

Sustainable development is critical to cold chain logistics, including its economic, environmental, and social effects, especially in road transportation. To simultaneously address these issues, we propose a comprehensive cold-chain-based low-carbon location-routing-problem optimization model to minimize the total logistics costs and client and vehicle waiting time. The first objective comprises the fixed costs of depots to open and vehicles to rent, vehicle renting cost, driver salaries, fuel consumption cost, carbon emission costs, and damage costs of cargos that need to be refrigerated or frozen. The second objective consists of the waiting time of clients and vehicles to improve client satisfaction and the efficiency of the cold chain logistics network. In the proposed problem, we developed a strategy for improving the efficiency of the cold chain logistics network by mixing the types of cargos arranged in one vehicle. Aiming at efficiently solving the proposed model, six well-known multi-objective evolutionary algorithms (MOEAs) were used by combining an efficient framework, and first (FI) and best-improvement (BI) search mechanisms were considered. In the experiments, we examined the effectiveness of six MOEAs inserting the proposed framework and search mechanisms, and the result showed that NSGA-II/FI, SPEA2/FI, and NSGA-II/BI were the top three MOEAs. In the extensive experiments, the results showed that the delivery strategy, depot cost, depot capacity, crowding distance, and traveling speed have significant effects on the Pareto front, fuel consumption, carbon emission, vehicle and client waiting times, traveling distance, and traveling time.

ACS Style

Longlong Leng; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Jingling Zhang; Gongfa Li. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production 2020, 273, 122801 .

AMA Style

Longlong Leng, Chunmiao Zhang, Yanwei Zhao, Wanliang Wang, Jingling Zhang, Gongfa Li. Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches. Journal of Cleaner Production. 2020; 273 ():122801.

Chicago/Turabian Style

Longlong Leng; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Jingling Zhang; Gongfa Li. 2020. "Biobjective low-carbon location-routing problem for cold chain logistics: Formulation and heuristic approaches." Journal of Cleaner Production 273, no. : 122801.

Journal article
Published: 25 June 2020 in Computers & Operations Research
Reads 0
Downloads 0

This paper proposed a novel approach for a practical version of the cold chain, namely location-routing problem-based low-carbon cold chain (LRPLCCC). In the proposed bi-objective model, the first objective is the total logistics cost, including the fixed costs of the opened depots and leased vehicles, as well as the cost of fuel consumption and carbon emissions, and the second is to minimize the amount of quality degradation that aims at improving clients’ satisfaction and maintain product freshness. The cargos of clients are classified into three types: general, refrigerated, and frozen cargos. Since the presented problem is NP-hard, a novel multi-objective hyperheuristic (MOHH) was proposed to obtain the Pareto solutions. In this framework, three selection strategies were developed to improve the performance of MOHH, that is, random simple, choice function, and FRR-MAB (fitness rate rank based multi-armed bandit), and three acceptance criteria using the decomposition approaches in MOEA/D were also developed, namely penalty-based boundary intersection, Tchebycheff, and modified Tchebycheff approaches. Extensive experiments were provided to verify the efficiency of the proposed algorithms and assessed the effects of algorithm parameters on the Pareto front. The results showed that the efficiency of the proposed algorithm outperforms several existing well-known multi-objective evolutionary algorithms (MOEA).

ACS Style

Longlong Leng; Jingling Zhang; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Gongfa Li. Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects. Computers & Operations Research 2020, 123, 105043 .

AMA Style

Longlong Leng, Jingling Zhang, Chunmiao Zhang, Yanwei Zhao, Wanliang Wang, Gongfa Li. Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects. Computers & Operations Research. 2020; 123 ():105043.

Chicago/Turabian Style

Longlong Leng; Jingling Zhang; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Gongfa Li. 2020. "Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects." Computers & Operations Research 123, no. : 105043.

Research article
Published: 09 April 2020 in PLOS ONE
Reads 0
Downloads 0

Economic, environmental, and social effects are the most dominating issues in cold chain logistics. The goal of this paper is to propose a cost-saving, energy-saving, and emission-reducing bi-objective model for the cold chain-based low-carbon location-routing problem. In the proposed model, the first objective (economic and environmental effects) is to minimize the total logistics costs consisting of costs of depots to open, renting vehicles, fuel consumption, and carbon emission, and the second one (social effect) is to reduce the damage of cargos, which could improve the client satisfaction. In the proposed model, a strategy is developed to meet the requirements of clients as to the demands on the types of cargos, that is, general cargos, refrigerated cargos, and frozen cargos. Since the proposed problem is NP-hard, we proposed a simple and efficient framework combining seven well-known multiobjective evolutionary algorithms (MOEAs). Furthermore, in the experiments, we first examined the effectiveness of the proposed framework by assessing the performance of seven MOEAs, and also verified the efficiency of the proposed model. Extensive experiments were carried out to investigate the effects of the proposed strategy and variants on depot capacity, hard time windows, and fleet composition on the performance indicators of Pareto fronts and cold chain logistics networks, such as fuel consumption, carbon emission, travel distance, travel time, and the total waiting time of vehicles.

ACS Style

Longlong Leng; Jingling Zhang; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Gongfa Li. A novel bi-objective model of cold chain logistics considering location-routing decision and environmental effects. PLOS ONE 2020, 15, e0230867 .

AMA Style

Longlong Leng, Jingling Zhang, Chunmiao Zhang, Yanwei Zhao, Wanliang Wang, Gongfa Li. A novel bi-objective model of cold chain logistics considering location-routing decision and environmental effects. PLOS ONE. 2020; 15 (4):e0230867.

Chicago/Turabian Style

Longlong Leng; Jingling Zhang; Chunmiao Zhang; Yanwei Zhao; Wanliang Wang; Gongfa Li. 2020. "A novel bi-objective model of cold chain logistics considering location-routing decision and environmental effects." PLOS ONE 15, no. 4: e0230867.

Journal article
Published: 28 February 2020 in Complexity
Reads 0
Downloads 0

This paper presents an evolution-based hyperheuristic (EHH) for addressing the capacitated location-routing problem (CLRP) and one of its more practicable variants, namely, CLRP with simultaneous pickup and delivery (CLRPSPD), which are significant and NP-hard model in the complex logistics system. The proposed approaches manage a pool of low-level heuristics (LLH), implementing a set of simple, cheap, and knowledge-poor operators such as “shift” and “swap” to guide the search. Quantum (QS), ant (AS), and particle-inspired (PS) high-level learning strategies (HLH) are developed as evolutionary selection strategies (ESs) to improve the performance of the hyperheuristic framework. Meanwhile, random permutation (RP), tabu search (TS), and fitness rate rank-based multiarmed bandit (FRR-MAB) are also introduced as baselines for comparisons. We evaluated pairings of nine different selection strategies and four acceptance mechanisms and monitored the performance of the first four outstanding pairs in 36 pairs by solving three sets of benchmark instances from the literature. Experimental results show that the proposed approaches outperform most fine-tuned bespoke state-of-the-art approaches in the literature, and PS-AM and AS-AM perform better when compared to the rest of the pairs in terms of obtaining a good trade-off of solution quality and computing time.

ACS Style

Yanwei Zhao; Longlong Leng; Jingling Zhang; Chunmiao Zhang; Wanliang Wang. Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery. Complexity 2020, 2020, 1 -24.

AMA Style

Yanwei Zhao, Longlong Leng, Jingling Zhang, Chunmiao Zhang, Wanliang Wang. Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery. Complexity. 2020; 2020 ():1-24.

Chicago/Turabian Style

Yanwei Zhao; Longlong Leng; Jingling Zhang; Chunmiao Zhang; Wanliang Wang. 2020. "Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery." Complexity 2020, no. : 1-24.

Research article
Published: 04 January 2020 in Computational Intelligence and Neuroscience
Reads 0
Downloads 0

In response to violent market competition and demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emission for better development. In this paper, a biobjective mathematical model is established for cold chain logistics network in consideration of economic, social, and environmental benefits; in other words, the total cost and distribution period of cold chain logistics are optimized, while the total cost consists of cargo damage cost, refrigeration cost of refrigeration equipment, transportation cost, fuel consumption cost, penalty cost of time window, and operation cost of distribution centres. One multiobjective hyperheuristic optimization framework is proposed to address this multiobjective problem. In the framework, four selection strategies and four acceptance criteria for solution set are proposed to improve the performance of the multiobjective hyperheuristic framework. As known from a comparative study, the proposed algorithm had better overall performance than NSGA-II. Furthermore, instances of cold chain logistics are modelled and solved, and the resulting Pareto solution set offers diverse options for a decision maker to select an appropriate cold chain logistics distribution network in the interest of the logistics company.

ACS Style

Zheng Wang; Longlong Leng; Shun Wang; Gongfa Li; Yanwei Zhao. A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption. Computational Intelligence and Neuroscience 2020, 2020, 1 -17.

AMA Style

Zheng Wang, Longlong Leng, Shun Wang, Gongfa Li, Yanwei Zhao. A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption. Computational Intelligence and Neuroscience. 2020; 2020 ():1-17.

Chicago/Turabian Style

Zheng Wang; Longlong Leng; Shun Wang; Gongfa Li; Yanwei Zhao. 2020. "A Hyperheuristic Approach for Location-Routing Problem of Cold Chain Logistics considering Fuel Consumption." Computational Intelligence and Neuroscience 2020, no. : 1-17.

Journal article
Published: 27 June 2019 in Algorithms
Reads 0
Downloads 0

This paper proposes a low-carbon location routing problem (LCLRP) model with simultaneous delivery and pick up, time windows, and heterogeneous fleets to reduce the logistics cost and carbon emissions and improve customer satisfaction. The correctness of the model is tested by a simple example of CPLEX (optimization software for mathematical programming). To solve this problem, a hyper-heuristic algorithm is designed based on a secondary exponential smoothing strategy and adaptive receiving mechanism. The algorithm can achieve fast convergence and is highly robust. This case study analyzes the impact of depot distribution and cost, heterogeneous fleets (HF), and customer distribution and time windows on logistics costs, carbon emissions, and customer satisfaction. The experimental results show that the proposed model can reduce logistics costs by 1.72%, carbon emissions by 11.23%, and vehicle travel distance by 9.69%, and show that the proposed model has guiding significance for reducing logistics costs.

ACS Style

Chunmiao Zhang; Yanwei Zhao; Longlong Leng. A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem. Algorithms 2019, 12, 129 .

AMA Style

Chunmiao Zhang, Yanwei Zhao, Longlong Leng. A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem. Algorithms. 2019; 12 (7):129.

Chicago/Turabian Style

Chunmiao Zhang; Yanwei Zhao; Longlong Leng. 2019. "A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem." Algorithms 12, no. 7: 129.

Review
Published: 11 June 2019 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions.

ACS Style

Longlong Leng; Yanwei Zhao; Jingling Zhang; Chunmiao Zhang. An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem. International Journal of Environmental Research and Public Health 2019, 16, 2064 .

AMA Style

Longlong Leng, Yanwei Zhao, Jingling Zhang, Chunmiao Zhang. An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem. International Journal of Environmental Research and Public Health. 2019; 16 (11):2064.

Chicago/Turabian Style

Longlong Leng; Yanwei Zhao; Jingling Zhang; Chunmiao Zhang. 2019. "An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem." International Journal of Environmental Research and Public Health 16, no. 11: 2064.

Original paper
Published: 19 April 2019 in Operational Research
Reads 0
Downloads 0

This paper addresses a new variant of location-routing problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). A hyper-heuristic approach based on iterated local search (ILS-HH) is introduced to automatically optimize the LRPSPD. On basis of the novel proposed framework of hyper-heuristic, four selections mechanisms and five activation strategies are developed to examine the performance of the proposed framework. Three types computational evaluations were carried out and several conclusions can be drawn: (1) the proposed framework performs better than the classical one with performing several heavy-duty combinations of strategies in terms of solution quality and computing time; (2) different activated strategies have slight (not significant) effect on exploiting best solutions; (3) FRR-MAB-TS (fitness ratio rank based on multi-armed bandit with tabu search) works best among all selection methods. Moreover, the proposed approach could provide competitive, even better results compared to fine-tuned bespoke state-of-the-art approaches.

ACS Style

Yanwei Zhao; Longlong Leng; Chunmiao Zhang. A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery. Operational Research 2019, 21, 1299 -1332.

AMA Style

Yanwei Zhao, Longlong Leng, Chunmiao Zhang. A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery. Operational Research. 2019; 21 (2):1299-1332.

Chicago/Turabian Style

Yanwei Zhao; Longlong Leng; Chunmiao Zhang. 2019. "A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery." Operational Research 21, no. 2: 1299-1332.

Journal article
Published: 15 March 2019 in Sustainability
Reads 0
Downloads 0

With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

ACS Style

Longlong Leng; Yanwei Zhao; Zheng Wang; Jingling Zhang; Wanliang Wang; Chunmiao Zhang. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability 2019, 11, 1596 .

AMA Style

Longlong Leng, Yanwei Zhao, Zheng Wang, Jingling Zhang, Wanliang Wang, Chunmiao Zhang. A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints. Sustainability. 2019; 11 (6):1596.

Chicago/Turabian Style

Longlong Leng; Yanwei Zhao; Zheng Wang; Jingling Zhang; Wanliang Wang; Chunmiao Zhang. 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints." Sustainability 11, no. 6: 1596.

Research article
Published: 31 December 2018 in Mathematical Problems in Engineering
Reads 0
Downloads 0

In this paper, we consider a variant of the location-routing problem (LRP), namely, the regional low-carbon LRP with reality constraint conditions (RLCLRPRCC), which is characterized by clients and depots that located in nested zones with different speed limits. The RLCLRPRCC aims at reducing the logistics total cost and carbon emission and improving clients satisfactory by replacing the travel distance/time with fuel consumption and carbon emission costs under considering heterogeneous fleet, simultaneous pickup and delivery, and hard time windows. Aiming at this project, a novel approach is proposed: hyperheuristic (HH), which manipulates the space, consisted of a fixed pool of simple operators such as “shift” and “swap” for directly modifying the space of solutions. In proposed framework of HH, a kind of shared mechanism-based self-adaptive selection strategy and self-adaptive acceptance criterion are developed to improve its performance, accelerate convergence, and improve algorithm accuracy. The results show that the proposed HH effectively solves LRP/LRPSPD/RLCLRPRCC within reasonable computing time and the proposed mathematical model can reduce 2.6% logistics total cost, 27.6% carbon emission/fuel consumption, and 13.6% travel distance. Additionally, several managerial insights are presented for logistics enterprises to plan and design the distribution network by extensively analyzing the effects of various problem parameters such as depot cost and location, clients’ distribution, heterogeneous vehicles, and time windows allowance, on the key performance indicators, including fuel consumption, carbon emissions, operational costs, travel distance, and time.

ACS Style

Longlong Leng; Yanwei Zhao; Zheng Wang; Hongwei Wang; Jingling Zhang. Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows. Mathematical Problems in Engineering 2018, 2018, 1 -21.

AMA Style

Longlong Leng, Yanwei Zhao, Zheng Wang, Hongwei Wang, Jingling Zhang. Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows. Mathematical Problems in Engineering. 2018; 2018 ():1-21.

Chicago/Turabian Style

Longlong Leng; Yanwei Zhao; Zheng Wang; Hongwei Wang; Jingling Zhang. 2018. "Shared Mechanism-Based Self-Adaptive Hyperheuristic for Regional Low-Carbon Location-Routing Problem with Time Windows." Mathematical Problems in Engineering 2018, no. : 1-21.

Conference paper
Published: 26 May 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0

In this paper, the carbon emission factor is taken into account in the Location Routing Problem (LRP), and a multi-objective LRP model combining carbon emission with total cost is established. Due to the complexity of the proposed problem, a generality-oriented and emerging Multi-Objective Hyper Heuristic algorithm (MOHH) is proposed. In the framework of MOHH, the LRP related operates are constructed as the low level heuristics, and the different high level strategies are designed. Compared with the NSGA-II algorithm, the MOHH can better solve the multi-objective problem of LRP, and can quickly find the better solution, and achieve higher search efficiency and stability of the algorithm.

ACS Style

Zhenyu Qian; Yanwei Zhao; Shun Wang; Longlong Leng; Wanliang Wang. A Hyper Heuristic Algorithm for Low Carbon Location Routing Problem. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 173 -182.

AMA Style

Zhenyu Qian, Yanwei Zhao, Shun Wang, Longlong Leng, Wanliang Wang. A Hyper Heuristic Algorithm for Low Carbon Location Routing Problem. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():173-182.

Chicago/Turabian Style

Zhenyu Qian; Yanwei Zhao; Shun Wang; Longlong Leng; Wanliang Wang. 2018. "A Hyper Heuristic Algorithm for Low Carbon Location Routing Problem." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 173-182.

Journal article
Published: 01 January 2016 in Procedia Computer Science
Reads 0
Downloads 0

The capacitated vehicle routing problem (CVRP) has been proved to be NP complete problem. The CVRP is not just a purely academic construct, it has many applications in practice. In this work, a discrete invasive weed optimization algorithm (DIWO) is proposed to solve the capacitated vehicle routing problem. Adaptive mutation and crossover in the genetic operation process are introduced to ensure the diversity of the algorithm and prevent it from falling into a local optimal solution with premature convergence. We use real matrix encoding and construct a discretization process for the subgeneration in the parent generation region. An improved 2-Opt and exchange operations structure based on the property of the problem is proposed to construct the two-stage hybrid variable-domain search method, strengthening the capacity of the local and global search ability of the algorithms. Comparing the experimental simulation and the algorithm with literature for different scale benchmarks proves that the DIWO algorithm is simple, efficient, adaptable, and robust for discrete combinatorial optimization problems

ACS Style

Yanwei Zhao; Longlong Leng; Zhenyu Qian; Wanliang Wang. A Discrete Hybrid Invasive Weed Optimization Algorithm for the Capacitated Vehicle Routing Problem. Procedia Computer Science 2016, 91, 978 -987.

AMA Style

Yanwei Zhao, Longlong Leng, Zhenyu Qian, Wanliang Wang. A Discrete Hybrid Invasive Weed Optimization Algorithm for the Capacitated Vehicle Routing Problem. Procedia Computer Science. 2016; 91 ():978-987.

Chicago/Turabian Style

Yanwei Zhao; Longlong Leng; Zhenyu Qian; Wanliang Wang. 2016. "A Discrete Hybrid Invasive Weed Optimization Algorithm for the Capacitated Vehicle Routing Problem." Procedia Computer Science 91, no. : 978-987.