A novel approach based on heuristics and a neural network to solve a capacitated location routing problem

Oudouar, Fadoua; Lazaar, Mohamed; Miloud, Zaoui El

doi:10.1016/j.simpat.2019.102064

Cited by 17 publications

(8 citation statements)

References 48 publications

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“…In the 1970s, Cooper first proposed the LRP, combining LP with VRP, and many scholars have researched the model and its solving algorithm. In researching the LRP model, Li et al [12][13][14][15][16] used a multi-objective model, Tokgoz et al [17] established a mixedinteger non-linear programming problem to research the manifold LRP and provided the corresponding heuristic algorithm solution, and Tirkolaee et al [18] established a new mixed-integer linear programming model, which aims to reduce the total travel time and minimize the risk of infection, and constructed a sustainable multi-travel time window LRP for medical waste management during the COVID-19 pandemic.…”

Section: Lrpmentioning

confidence: 99%

“…Equation ( 11) is the objective function of the lower model, which indicates that the energy consumption costs, dispatch costs, and carbon emissions generated by distribution vehicles are minimized. Constraint (12) indicates that a customer can only be served by one distribution center, constraint (13) indicates that each distribution vehicle can only be deployed by one distribution center, and constraint (14) indicates that a customer can be served by multiple vehicles; that is, cargo are allowed to be split. Constraint (15) indicates that the distribution route of the distribution vehicle is continuous and closed, and the vehicle entering the node must leave from the node, and the distribution vehicle will not stop at a certain customer point.…”

Section: The Distribution Vehicles' Dispatch Costsmentioning

confidence: 99%

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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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Section: Lrpmentioning

confidence: 99%

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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“…Many studies on the LRP consider either capacitated depots or capacitated routes. Since most authors address the LRP with capacity constraint for both the depots and the routes [8].…”

Section: Introductionmentioning

confidence: 99%

An approach based on Clustering AH and VND for Supply Chain Management

Oudouar¹,

Zaoui²

2021

IJIES

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In this work, we present a new approach to solve the Capacitated Location-Routing Problem (CLRP). The aim of this method is to determine the depot locations, to assign customers to facilities and to define routes for each depot to serve the associated clients. The proposed approach contains two phases, which are the constructive phase and the improvement phase. In the first phase, we select the depots to be opened, allocate the customers to open depots using a Hierarchical Ascendant (HA) method and we solve the vehicle routing problem for each depot using Sweep algorithm. In the second phase, we apply a Variable Neighborhood Descent (VND) with three structures in order to optimize the cost obtained by the first phase. Two sets of well-known instances from the literature are used to evaluate the performance of this method, and the numerical results obtained are compared with the experimental results of other methods. Results show that our method is competitive with respect to the best-known solutions (BKSs) and demonstrate its efficiency in comparison with other approaches.

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“…A Vehicle Routing Problem (VRP) model plays an important role in various industrial sectors, ranging from productionbased industries to logistical issues. Various methods are used to solve this problem, which are grouped into two categories: deterministic optimization algorithms [1], [2], [3], [4], [5], and probabilistic optimization algorithms [6], [7], [8], [9]. An optimization algorithm determines such a great solution in solving VRP that finding the global optimum solution takes a long time.…”

Section: Introductionmentioning

confidence: 99%

Clustering Nodes and Discretizing Movement to Increase the Effectiveness of HEFA for a CVRP

Abdillah¹,

Suyanto²

2020

IJACSA

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A Capacitated Vehicle Routing Problem (CVRP) is an important problem in transportation and industry. It is challenging to be solved using some optimization algorithms. Unfortunately, it is not easy to achieve a global optimum solution. Hence, many researchers use a combination of two or more optimization algorithms, which based on swarm intelligence methods, to overcome the drawbacks of the single algorithm. In this research, a CVRP optimization model, which contains two main processes of clustering and optimization, based on a discrete hybrid evolutionary firefly algorithm (DHEFA), is proposed. Some evaluations on three CVRP cases show that DHEFA produces an averaged effectiveness of 91.74%, which is much more effective than the original FA that gives mean effectiveness of 87.95%. This result shows that clustering nodes into several clusters effectively reduces the problem space, and the DHEFA quickly searches the optimum solution in those partial spaces.

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A novel approach based on heuristics and a neural network to solve a capacitated location routing problem

Cited by 17 publications

References 48 publications

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

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

An approach based on Clustering AH and VND for Supply Chain Management

Clustering Nodes and Discretizing Movement to Increase the Effectiveness of HEFA for a CVRP

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