Improved approximation algorithms for cumulative VRP with stochastic demands

Gaur, Daya Ram; Mudgal, Apurva; Singh, Rishi Ranjan

doi:10.1016/j.dam.2018.01.012

Cited by 18 publications

(7 citation statements)

References 16 publications

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“…Fazayeli et al [36] set time windows and fuzzy demand in a multimodal LRP. Gaur et al [37], Mendoza et al [38] and Gutierrez et al [39] solved cumulative VRPs with stochastic demand by metaheuristic algorithms.…”

Section: Fuzzy Planning Of Routing Problemmentioning

confidence: 99%

“…The next nodes could be a DHS or a DMS. Constraints (26) to (37) show the time constraints in highway transport.…”

Section: ) Constraints Of Fuzzy Timementioning

confidence: 99%

“…Constraints (26) to (31) indicate the time relationship between the front and back arcs when fuzzy loading time exists in the station i ∈ V DHS . Constraints (32) to (37) indicate the time relationship between the front and back arcs when fuzzy unloading time exists in the station i ∈ V DHS . As with the previous constraints, triangular fuzzy numbers are used.…”

Section: ) Constraints Of Fuzzy Timementioning

confidence: 99%

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A Fuzzy Intercontinental Road-Rail Multimodal Routing Model With Time and Train Capacity Uncertainty and Fuzzy Programming Approaches

Lang

Sun

et al. 2020

IEEE Access

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This paper investigates a fuzzy intercontinental multimodal routing problem with uncertainties in time and train capacity. The transport network has characteristics of a long distance across continents and a road-rail multimodal routing, with four kinds of nodes and three kinds of arcs. Based on a variant of the vehicle routing problem, the tractor and semi-trailer routing problem is considered for the freight collection part for intercontinental trains. Additionally, the rail routing problem includes domestic direct trains, and intercontinental trains with hard time windows of departure. To make this problem more applicable to realworld circumstances, we describe two types of uncertainty parameters, including time and train uncertainties. Based on the transport conditions of stations, the time uncertainty is considered. Due to the multimodal transport stations' operating capacity and container collection methods, train capacity uncertainty is taken into account. Furthermore, we use solution methods based on the defuzzification approach to solve a fuzzy mixed integer linear programming model and generate a series of instances to verify the fuzzy model. We perform sensitivity analyses of the parameters. The results show that different quantities of intercontinental trains can change the performance by 10% to 20%. The objective function may decrease by more than 20% when the service level increases by 0.1. A sensitivity analysis of the time satisfaction confidence level also shows the trends of fuzzy time and the objective function. These analyses can give reference results about timeliness, transport resource allocation and other suggestions for the intercontinental multimodal transport routing problem. INDEX TERMS Intercontinental road-rail multimodal transport, routing problem, time uncertainty, capacity uncertainty, fuzzy programming.

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Section: Fuzzy Planning Of Routing Problemmentioning

confidence: 99%

“…The next nodes could be a DHS or a DMS. Constraints (26) to (37) show the time constraints in highway transport.…”

Section: ) Constraints Of Fuzzy Timementioning

confidence: 99%

Section: ) Constraints Of Fuzzy Timementioning

confidence: 99%

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A Fuzzy Intercontinental Road-Rail Multimodal Routing Model With Time and Train Capacity Uncertainty and Fuzzy Programming Approaches

Lang

Sun

et al. 2020

IEEE Access

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“…However, stochastic programming gains wide application in the vehicle routing problem with demand uncertainty, an optimization problem that also belongs to the operational level of transportation planning [40]. Various studies on the vehicle routing problem with stochastic demands can be found, e.g., Gaur et al [41], Gutierrez et al [42], Mendoza et al [43], and Bianchi et al [44].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows

Sun

Liang

Li³

et al. 2019

Symmetry

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Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications.

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“…The CCVRP was introduced by Ngueveu et al [2] as a generalization of the well known k-Traveling Repairmen Problem (k-TRP), with the objective of addressing problems in which customer metrics reflect the need for fast, equity and fair services. Since then, a diverse number of scenarios have been addressed: where a single vehicle can travel multiple trips [3,4], considering stochastic demand and split/unsplit deliveries [5], or when multiple depots are available [6]. Specifically, the CCVRP with priorities arises in the context of modeling situations in which customers have a differentiated level of attention and has attracted the interest of researchers over the past years due to its applicability in fields such as emergency logistics (i.e., level of damage), delivery logistics (i.e., delivery of different perishable items), personnel transportation, school bus transportation, machine scheduling considering due dates, maintenance operations including delivery dates, receiver wireless communications, etc.…”

Section: Introductionmentioning

confidence: 99%

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

Nucamendi-Guillén¹,

Flores-Díaz²,

Olivares-Benítez³

et al. 2020

Applied Sciences

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This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved.

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Improved approximation algorithms for cumulative VRP with stochastic demands

Cited by 18 publications

References 16 publications

A Fuzzy Intercontinental Road-Rail Multimodal Routing Model With Time and Train Capacity Uncertainty and Fuzzy Programming Approaches

A Fuzzy Intercontinental Road-Rail Multimodal Routing Model With Time and Train Capacity Uncertainty and Fuzzy Programming Approaches

A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

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