An Iterated Local Search Algorithm for the Cumulative Capacitated Vehicle Routing Problem

Chen, Ping; Dong, Xianguang; Niu, Yanchao

doi:10.1007/978-3-642-27711-5_76

Cited by 12 publications

(13 citation statements)

References 9 publications

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“…Given the visiting order and LIFO loading, v 3 , v 5 and v 4 contribute σ 3,1 = 8, σ 5,1 = 1, and σ 4,1 = 1 to v 1 , respectively. As a result, v 1 encounters latency amounted to 8 10 · 4 + 1 10 · (4 + 3) + 1 10 · (4 + 3 + 6) in accordance with the objective function.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Constraints (3) and (4) guarantee to visit each node exactly once and eliminate subtours, respectively. The vehicle load limits are presented in (5), where λ (i) indicates the vehicle load at v (i) along the visiting order, i.e.,…”

Section: Problem Formulationmentioning

confidence: 99%

“…Example scenarios include door-to-door freight services and humanitarian aid after natural disaster. This problem has been formulated as the cumulative capacitated vehicle routing problem (CCVRP) to minimize the total arrival time to reach customers through designated routes subject to capacity limitation [4], [5], [6], [7]. The CCVRP is closely related to the minimum latency problem (MLP) [8], [9], [10], which discards vehicle capacity and is applicable in managing the schedules for repairmen or house cleaning services.…”

Section: Introductionmentioning

confidence: 99%

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A genetic algorithm for the minimum latency pickup and delivery problem

Liao

Chien

Ting

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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The pickup and delivery problem combines vehicle routing and objects distribution to cope with logistic problems. While most research on PDP aims to minimize the transportation cost for the sake of service providers, this study proposes the minimum latency pickup and delivery problem (MLPDP) that seeks a low-latency route to transport commodities among nodes, where latency represents the sum of transportation time between demanders and the corresponding suppliers. The MLPDP is pertinent to time-sensitive services and logistics focusing on customer satisfaction. This study defines the latency of a customer as the average time elapsed aboard of goods received. The last-in-first-out loading method is employed to simulate realworld rear-loaded vehicles. This study further designs a genetic algorithm (GA) to resolve the MLPDP. In particular, we propose the edge aggregate crossover (EAC) and the reversely weighting technique to improve the performance of GA on the MLPDP. Experimental results show the effectiveness of the proposed GA. The results further indicate that EAC leads to significantly better performance than conventional crossover operators in solution quality and convergence speed on the MLPDP.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A genetic algorithm for the minimum latency pickup and delivery problem

Liao

Chien

Ting

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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“…Constraints (9) and (10) guarantee that the total demand satisfied in each period be equal to the customer's demand. Constraints (9) ensure that only one vehicle is used to go from i to j in each period. Constraints (12), (18), (19), (20), (21) are the time window constraints ensuring that the vehicle be active at the particular time interval determined by each customer.…”

Section: J Cs Jmentioning

confidence: 99%

“…Research on minimum latency problems (MLPs) has been conducted to achieve this desire from the transportation and logistics viewpoints. MLP is also known as cumulative vehicle routing problem (Chen et al [8], 2012) or Customer Centric Vehicle Routing Problem (Martinez-Salazar et al [9], 2014), which is a class of routing problems with the objective to minimize the waiting time of customers. Customer satisfaction and competitive business environment, on the one hand, and increase in the number, scale, and severity of disasters, on the other hand, are key elements for MLP to receive considerable attention from researchers.…”

mentioning

confidence: 99%