Location-Routing-Inventory Problem with Stochastic Demand in Logistics Distribution Systems

Zhang, Bo; Ma, Zhenjun; Jiang, Sai

doi:10.1109/wicom.2008.1547

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Así mismo, se encuentran diversos modelos LIRP multiperiodo con demanda estocástica, como [29], donde se comparan dos métodos de localización de depósitos con el fin de mejorar la eficiencia de un sistema de riego de carreteras. Algunos autores han considerado que la demanda estocástica de sus modelos obedece una distribución Poisson; tales como [30], donde se propone un algoritmo genético mixto para resolver el modelo; [31] busca optimizar un sistema logístico de ciclo cerrado a través de una heurística de dos fases; asimismo [32] también utiliza una heurística de dos fases para resolver su modelo de logística inversa, para un sistema de recolección de desperdicios sólidos municipales; y por último en [18] se propone un algoritmo genético mixto para resolver el modelo. Otros autores por su parte consideran que la distribución normal es la que más se ajusta a las demandas de sus modelos, como es el caso de [9] quienes formulan su modelo con programación entera no lineal y proponen un algoritmo basado en relajación lagrangiana; también se encuentran los modelos multiobjetivos presentados por [33] quienes buscan soluciones a su modelo explorando 4 diferentes algoritmos metaheurísticos multiobjetivo, y [34] que proponen una metaheurística.…”

Section: Lirp Para Una Red De Distribución De Dos Escalonesunclassified

Una nueva metaheurística híbrida para resolver el problema 2eLIRP

Arias-Osorio

Camacho-Pinto

2021

Rev. UIS ing.

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En este trabajo se considera el problema de optimización que integra las funciones logísticas de localización, transporte e inventario multiperiodo en una red de distribución de dos escalones, denominado 2eLIRP. En este artículo se propone para su solución una nueva metaheurística híbrida, basada en Búsqueda Tabú, Algoritmos Genéticos y la heurística del Vecino más cercano. Los rendimientos de esta técnica híbrida se comparan con las metaheurísticas clásicas que tiene como base, encontrando mejores soluciones en el 96,67% de los experimentos realizados.

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Section: Lirp Para Una Red De Distribución De Dos Escalonesunclassified

Una nueva metaheurística híbrida para resolver el problema 2eLIRP

Arias-Osorio

Camacho-Pinto

2021

Rev. UIS ing.

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“…Tanonkou et al [39] established a two-stage non-linear discrete stochastic optimization model to determine the distribution centers, and solved it by a Lagrangian relaxation based approach. Zhang et al [40] applied a hybrid genetic algorithm to solve the location-routing-inventory problem under stochastic demands. Li et al [41] designed an improved Benders decomposition algorithm to solve a model with multiple scenarios in the uncertain environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Locating Movable Parcel Lockers under Stochastic Demands

Wang

Lai

et al. 2020

Symmetry

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The movable unit equipped with a set of lockers has been recently developed as a new mode to improve the efficiency of the last mile delivery. Locating a set of movable parcel locker units appropriately is a fundamental factor to promote the merits of movable parcel lockers. However, the difficulty in determining where to locate movable parcel locker units arises from the stochastic characteristics of demands. Therefore, we propose a robust optimization approach to determine the number of movable parcel locker units and their locations simultaneously with the aim to minimize the operating cost under stochastic demands and mobility restrictions. To reduce the complexity of the optimization model, the non-linear constraints have been transformed into the linear counterparts, resulting in an integer linear programming (ILP) model that can be solved by commercially available mathematical programming solvers. The results from the numerical examples indicate the proposed approach can obtain high robustness with a small extra cost within reasonable time. In addition, it is found that if each unit is equipped with more lockers, fewer movable parcel locker units are required to accommodate the demands with less operating cost, as the demand points can be clustered into a few intensive self-pickup sites.

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“…The computational results show that integration of location and inventory decisions and taking into account transport costs had an advantage over the other models. Zhang et al, (2008) studied the problem of location-routing-inventory in a multi-period multi-depot status. They considered a single-product model where each path should be closely traveled by a vehicle and each path starts from the depot and ends to the depot.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated location inventory routing model in supply chain network designing under uncertainty

Angazi¹

2016

10.5267/j.dsl

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In this study an integrated model is proposed for the location inventory routing problem under uncertainty. This problem involves determining the location of distribution centers (DCs) in a three echelon supply chain. The DCs receive orders from the customer and according to a continuous review inventory replenishment policy place orders to the supplier. The products are directly shipped from the supplier to the DCs. The vehicles start from the DCs to fulfill the demands of the customers. Determining the routing of the vehicles is one of the decisions involved in this problem. The demands of customers are stochastically distributed and the capacity of DCs are limited. If one of the DCs undergo a disruption and is unable to fulfill the demands of the customers, shortage may occur. Moreover in the proposed model the shortage is considered as partial backlogging. This means that if shortage occurs, some of the orders result in lost sales and other orders are fulfilled in the next period. In order to optimally solve the proposed model a nonlinear integer programming (INLP) model is developed. However, since the problem is NP-hard, the mathematical formulation cannot be efficiently solved for large sized instances of the problem. Therefore an outer approximation method is developed to solve the problem more efficiently. The computational results show the efficiency of the proposed method.

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Location-Routing-Inventory Problem with Stochastic Demand in Logistics Distribution Systems

Cited by 9 publications

References 6 publications

Una nueva metaheurística híbrida para resolver el problema 2eLIRP

Una nueva metaheurística híbrida para resolver el problema 2eLIRP

Locating Movable Parcel Lockers under Stochastic Demands

An integrated location inventory routing model in supply chain network designing under uncertainty

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