Simulation-based optimisation approach for the stochastic two-echelon logistics problem

Liu, Ran; Tao, Yangyi; Hu, Qiaoyu; Xie, Xiaolei

doi:10.1080/00207543.2016.1201221

Cited by 30 publications

(15 citation statements)

References 37 publications

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“…Soysal and Çimen [37] combined a heuristic approach with simulation-based dynamic programming method to solve the green time dependent vehicle routing problem in a large sized logistics network. Liu et al [38] proposed a simulation-based optimization approach combined with a tabu search algorithm to study the two-echelon vehicle routing problem consisting of freight transportation through intermediate satellites. Belgin et al [39] presented a hybrid heuristic algorithm with variable neighborhood descent and local search to solve the two-echelon vehicle routing problem with simultaneous pickup and delivery.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

et al. 2019

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The sustainability and complexity of logistics networks come from the temporally and spatially uneven distributions of freight demand and supply. Operation strategies without considering the sustainability and complexity could dramatically increase the economic and environmental costs of logistics operations. This paper explores how the unevenly distributed demand and supply can be optimally matched through collaborations, and formulates and solves a Collaborative Pickup and Delivery Problem under Time Windows (CPDPTW) to optimize the structures of logistics networks and improve city sustainability and liverability. The CPDPTW is a three-stage framework. First, a multi-objective linear optimization model that minimizes the number of vehicles and the total cost of logistics operation is developed. Second, a composite algorithm consisting of improved k-means clustering, Demand-and-Time-based Dijkstra Algorithm (DTDA) and Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is devised to solve the optimization model. The clustering algorithm helps to identify the feasible initial solution to INSGA-II. Third, a method based on improved Shapley value model is proposed to obtain the collaborative alliance strategy that achieves the optimal profit allocation strategy. The proposed composite algorithm outperforms existing algorithms in minimizing terms of the total cost and number of electro-tricycles. An empirical case of Chongqing is employed to demonstrate the efficiency of the proposed mechanism for achieving optimality for logistics networks and realizing a win-win situation between suppliers and consumers.

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Section: Literature Reviewmentioning

confidence: 99%

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

et al. 2019

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“…In addition, the vehicle restriction strategy is prevalent in major cities around the world. Therefore, in the current urban distribution process, vehicles with heavy loads are often used to transport goods to the urban edge areas and transport to smaller cities by vehicles with smaller loads to meet the needs of the people's daily lives, which led to the research on the two-level dynamic vehicle routing problem [5,6]. However, this strategy often neglects some potential dynamic customers and lacks practical consideration.…”

Section: Introductionmentioning

confidence: 99%

Proactive Two-Level Dynamic Distribution Routing Optimization Based on Historical Data

Xue

Wen

2018

Mathematical Problems in Engineering

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In view of the dynamic dispersion of e-commerce logistics demand, this paper uses the historical distribution data of logistics companies to study data-driven proactive vehicle routing optimization. First, based on the classic 2E-VRP problem, a single-node/multistage 2E-VRP mathematical model is constructed. Then, a framework for solving the proactive vehicle routing optimization problem is proposed in combination with the characteristics of the proposed model, including four modules: data-driven demand forecasting methods, customer clustering methods, proactive demand quotas and replenishment strategies, and vehicle routing optimization procedure. The significant feature of the proposed solution framework is that the response to dynamic customers is proactive rather than passive. The solution is applied to the distribution practice of a large logistics company in Chongqing. The results show that the proposed method has better dynamic scene adaptability and customer response capabilities in traffic limit.

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“…The TS heuristic has successfully solved various combinatorial optimization problems, such as job shop scheduling [42], computer-aided process planning [43], and multiple yard scheduling [44]. It has solved some of VRP variants [45][46][47].…”

Section: Solution Approachmentioning

confidence: 99%

Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

Yuan

Jiang

2017

Sustainability

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Abstract:The aggravating trend of the aging population, the miniaturization of the family structure, and the increase of families with empty nesters greatly affect the sustainable development of the national economy and social old-age security system of China. The emergence of home health care or home care (HHC/HC) service mode provides an alternative for elderly care. How to develop and apply this new mobile service mode is crucial for the government. Therefore, the pertinent optimization problems regarding HHC/HC have constantly attracted the attention of researchers. Unexpected events, such as new requests of customers, cancellations of customers' services, and changes of customers' time windows, may occur during the process of executing an a priori visiting plan. These events may sometimes make the original plan non-optimal or even infeasible. To cope with this situation, we introduce disruption management to the real-time home caregiver scheduling and routing problem. The deviation measurements on customers, caregivers, and companies are first defined. A mathematical model that minimizes the weighted sum of deviation measurements is then constructed. Next, a tabu search (TS) heuristic is developed to efficiently solve the problem, and a cost recorded mechanism is used to strengthen the performance. Finally, by performing computational experiments on three real-life instances, the effectiveness of the TS heuristic is tested, and the advantages of disruption management are analyzed.

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Simulation-based optimisation approach for the stochastic two-echelon logistics problem

Cited by 30 publications

References 37 publications

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows

Proactive Two-Level Dynamic Distribution Routing Optimization Based on Historical Data

Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

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