Vehicle routing with fuzzy time windows using a genetic algorithm

López-Castro, Luis Francisco; Montoya‐Torres, Jairo R.

doi:10.1109/cipls.2011.5953356

Cited by 11 publications

(12 citation statements)

References 25 publications

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“…Currently, Zhang et al [43] develop hard time windows to model the due dates and require that the transportation time of each order must fall into the time window. In fact, customers also accept the violation of the due date time windows to a certain degree [42,44,45]. Under this situation, soft time windows receive a lot of attention from the intermodal routing literature when discussing the formulation of due dates.…”

Section: Review On Modeling Customer Demand On Timeliness In the Intementioning

confidence: 99%

“…When formulating soft time windows and the corresponding penalty cost strategy is infeasible, it is worthwhile to try to model the service level of the intermodal routing in order to further improve the timeliness by constructing an associated constraint or objective. The most popular method of building the function of customer satisfaction associated with subjective opinions is fuzzy set theory [45]. From that viewpoint of fuzzy set theory, the due dates can be modeled as fuzzy soft time windows that are As claimed by Sun et al [42], time windows are more suitable and flexible to model the due dates by using lower and upper bounds to describe the customers' opinions on the delivery that is neither too early nor too late.…”

Section: Review On Modeling Customer Demand On Timeliness In the Intementioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncertainty

Sun

Li²

2019

Mathematics

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In this study, we systematically investigate a road-rail intermodal routing problem the optimization of which is oriented on the customer demands on transportation economy, timeliness and reliability. The road-rail intermodal transportation system is modelled as a hub-and-spoke network that contains time-flexible container truck services and scheduled container train services. The transportation timeliness is optimized by using fuzzy soft time windows associated with the service level of the transportation. Reliability is enhanced by considering multiple sources of time uncertainty, including road travel time and loading/unloading time. Such uncertainty is modelled by using fuzzy set theory. Triangular fuzzy numbers are adopted to represent the uncertain time. Under the above consideration, we first establish a fuzzy mixed integer nonlinear programming model with a weighted objective that includes minimizing the costs and maximizing the service level for accomplishing transportation orders. Then we use the fuzzy expected value model and fuzzy chance-constrained programming separately to realize the defuzzification of the fuzzy objective and use fuzzy chance-constrained programming to deal with the fuzzy constraint. After defuzzification and linearization, an equivalent mixed integer linear programming (MILP) model is generated to enable the problem to be solved by mathematical programming software. Finally, a numerical case modified from our previous study is presented to demonstrate the feasibility of the proposed fuzzy programming approaches. Sensitivity analysis and fuzzy simulation are comprehensively utilized to discuss the effects of the fuzzy soft time windows and time uncertainty on the routing optimization and help decision makers to better design a crisp transportation plan that can effectively make tradeoffs among economy, timeliness and reliability.

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Section: Review On Modeling Customer Demand On Timeliness In the Intementioning

confidence: 99%

Section: Review On Modeling Customer Demand On Timeliness In the Intementioning

confidence: 99%

Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncertainty

Sun

Li²

2019

Mathematics

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“…The customers may consider "good" if the arrival time at the destination is within the time window, while "all right" or "bad" or other personal human feelings if the arrival time is out of the range of the time window [24]. Hence, we can use trapezoidal fuzzy numbers to represent the due date, and further measure the customer satisfaction quantitatively using the fuzzy membership function [27,28].…”

Section: Fuzzy Soft Time Windowmentioning

confidence: 99%

“…However, as has been claimed in many studies as well as indicated in the practice, customer information, especially their demands, is difficult to determine during the planning period. Many studies on other transportation problems, e.g., the vehicle routing problem [22][23][24] and service network design problem [25,26], have all paid great attention to the uncertain issues from the fuzzy or stochastic viewpoint. Hence uncertain customer information is also a characteristic that should not be neglected in the multimodal routing problem.…”

Section: Introductionmentioning

confidence: 99%

On Solving the Fuzzy Customer Information Problem in Multicommodity Multimodal Routing with Schedule-Based Services

Sun

Lang

2016

Information

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Abstract:In this study, we combine the fuzzy customer information problem with the multicommodity multimodal routing with schedule-based services which was explored in our previous study [1]. The fuzzy characteristics of the customer information are embodied in the demanded volumes of the multiple commodities and the time windows of their due dates. When the schedule-based services are considered in the routing, schedule constraints emerge because the operations of block container trains should follow their predetermined schedules. This will restrict the routes selection from space-time feasibility. To solve this combinatorial optimization problem, we first build a fuzzy chance-constrained nonlinear programming model based on fuzzy possibility theory. We then use a crisp equivalent method and a linearization method to transform the proposed model into the classical linear programming model that can be effectively solved by the standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. The sensitivity of the best solution with respect to the values of the confidence levels is also examined.

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“…For any given capacity value , its fuzzy membership degree ( ) can be calculated by Equation (2) [18,24]. This function will be further used in the fuzzy simulation in the case study section.…”

Section: Fuzzy Service Capacity: Trapezoidal Fuzzy Numbers Vs Triangmentioning

confidence: 99%

How Uncertain Information on Service Capacity Influences the Intermodal Routing Decision: A Fuzzy Programming Perspective

et al. 2018

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Capacity uncertainty is a common issue in the transportation planning field. However, few studies discuss the intermodal routing problem with service capacity uncertainty. Based on our previous study on the intermodal routing under deterministic capacity consideration, we systematically explore how service capacity uncertainty influences the intermodal routing decision. First of all, we adopt trapezoidal fuzzy numbers to describe the uncertain information of the service capacity, and further transform the deterministic capacity constraint into a fuzzy chance constraint based on fuzzy credibility measure. We then integrate such fuzzy chance constraint into the mixed-integer linear programming (MILP) model proposed in our previous study to develop a fuzzy chance-constrained programming model. To enable the improved model to be effectively programmed in the standard mathematical programming software and solved by exact solution algorithms, a crisp equivalent linear reformulation of the fuzzy chance constraint is generated. Finally, we modify the empirical case presented in our previous study by replacing the deterministic service capacities with trapezoidal fuzzy ones. Using the modified empirical case, we utilize sensitivity analysis and fuzzy simulation to analyze the influence of service capacity uncertainty on the intermodal routing decision, and summarize some interesting insights that are helpful for decision makers.

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Vehicle routing with fuzzy time windows using a genetic algorithm

Cited by 11 publications

References 25 publications

Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncertainty

Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncertainty

On Solving the Fuzzy Customer Information Problem in Multicommodity Multimodal Routing with Schedule-Based Services

How Uncertain Information on Service Capacity Influences the Intermodal Routing Decision: A Fuzzy Programming Perspective

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