A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption

Wu, Weitiao; Zhou, Wei; Lin, Yue; Xie, Yuanqi; Jin, Wenzhou

doi:10.1016/j.eswa.2020.114034

Cited by 60 publications

(28 citation statements)

References 25 publications

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“…Constraints ( 9) and ( 10) are flow conservation constraints. Constraints (11) and (12) separately formulate the total number of trucks and delivery vehicles within one service period. Constraints ( 13) and ( 14) separately formulate the total number of routes executed by each truck and delivery vehicle within one service period.…”

Section: Model Formulationmentioning

confidence: 99%

“…Traditional two-echelon LRPs (2E-LRPs) involve selecting locations for logistics facilities, assigning customers to appropriate logistics facilities, and optimizing two-echelon vehicle routes [7][8][9]. However, from a long-term view, the operating costs in pure 2E-LRPs can be increased due to the various requirements of service time within a set up working period [10,11]. In modern logistics industry, delivery time is increasingly important to guarantee the efficient operations of logistics networks [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Wang

Sun

Guan

et al. 2021

Journal of Advanced Transportation

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In this work, a two-echelon location-routing problem with time windows and transportation resource sharing (2E-LRPTWTRS) is solved by selecting facility locations and optimizing two-echelon vehicle routes. The optimal solutions improve the efficiency of a logistics network based on the geographical distribution and service time windows of logistics facilities and customers. Furthermore, resource utilization is maximized by enabling resource sharing strategies within and among different logistics facilities simultaneously. The 2E-LRPTWTRS is formulated as a biobjective optimization model, and obtaining the smallest number of required delivery vehicles and the minimum total operating cost are the two objective functions. A two-stage hybrid algorithm composed of k-means clustering and extended multiobjective particle swarm optimization algorithm is proposed for 2E-LRPTWTRS optimization. A self-adaptive mechanism of flight parameters is introduced and adopted during the iterative process to balance the evolution of particles and improve the efficiency of the two-stage hybrid algorithm. Moreover, 20 small-scale instances are used for an algorithm comparison with multiobjective genetic algorithm and nondominated sorting genetic algorithm-II, and the solutions demonstrate the superiority of the proposed algorithm in optimizing logistics networks. The proposed optimization model and hybrid algorithm are tested by employing a real-world case of 2E-LRPTWTRS in Chongqing, China, and the optimization results verify the positive role of the developed model and algorithm in improving logistics efficiency, reducing operating cost, and saving transportation resources in the operations of two-echelon logistics networks.

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Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Wang

Sun

Guan

et al. 2021

Journal of Advanced Transportation

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“…In addition, instead of minimizing the costs, the GA has developed aims to maximize the total profits. Similarly, Wu et al (2021) has proposed GA but incorporated the fuel consumption into the variable transportation cost. The authors proposed a two-stage hybrid metaheuristics algorithm to address the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Green Inventory Routing Problem using Hybrid Genetic Algorithm

Halim

Azliana

Baharom

et al. 2021

JCRINN

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Carbon dioxide (CO2) is known as one of the largest sources of global warming. One of the ways to curb CO2 emissions is by considering the environmental aspect in the supply chain management. This paper analyses the influence of carbon emissions on the Inventory Routing Problem (IRP). The IRP network consists of a depot, an assembly plant and multiple suppliers. The deterministic demands vary and are determined by the assembly plant. Fixed transportation cost, fuel consumption cost and inventory holding cost are used to evaluate the system’s total cost in which fuel consumption cost is determined by fuel consumption rate, distance, and fuel price. Backordering and split pick-up are not allowed. The main purpose of this study is to analyze the distribution network especially the overall costs of the supply chain by considering the CO2 emissions as well. The problem is known as Green Inventory Routing Problem (GIRP). The mixed-integer linear programming of this problem is adopted from Cheng et al. wherein this study a different Hybrid Genetic Algorithm is proposed at mutation operator. As predicted, GIRP has a higher total cost as it considered fuel consumption cost together with the transportation and inventory costs. The results showed the algorithm led to different sequences of routings considering the carbon dioxide emission in the objective function.

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“…In researching the LRP algorithms, Mousavi et al [19][20][21] designed a two-stage algorithm to solve the LRP, divided into LP and VRP, which cannot reflect the LRP's integrity. The efficiency and quality of the optimal solution improve when the LRP is solved as a whole.…”

Section: Lrpmentioning

confidence: 99%

“…Constraint (16) means that each distribution vehicle will return to the original distribution center after the completion of the service, constraint (17) indicates that the customer's demand must be met, constraint (18) indicates that the vehicle load constraint, that is, the customer demand delivered by the vehicle on any route, must not exceed the vehicle load limit, and constraint (19) indicates that the customer demand scale does not exceed the vehicle load limit. Constraint (20) indicates that there is no distribution route between each distribution center, constraint (21) indicates that the total customer demand served by a distribution center should not exceed the capacity limit of the distribution center, and constraint (22) is the lower-level decision variable.…”

Section: The Distribution Vehicles' Dispatch Costsmentioning

confidence: 99%

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

2021

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To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

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A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption

Cited by 60 publications

References 25 publications

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Green Inventory Routing Problem using Hybrid Genetic Algorithm

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

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