.png){kind=icon}
.png){kind=icon}
Return
Modeling and evaluating network reliability for time-dependent stochastic logistics network under the customer commitment time constraint
.png){kind=icon}
PRE
AI
DOI：10.1080/02533839.2025.2557238.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In real-world logistics, the downtime of logistics vehicles increases with transportation time due to breakdowns, maintenance, or repairs, which introduce uncertainty in vehicle availability. This paper models such uncertainty using a time-dependent stochastic logistics network (TSLN), where nodes are connected by arcs representing transportation routes. The arc capacities, defined as the number of available logistics vehicles, are modeled as stochastic and are influenced by transportation time. The Weibull distribution is employed to characterize the time-dependent nature of vehicle failures, and a procedure is developed to compute the time-related capacity and its corresponding probability. Given that customer commitment time must be satisfied in the TSLN, flows are analyzed with respect to delivery times. In this context for a TSLN, network reliability, defined as the probability of satisfying demand within the customer commitment time, is evaluated as a key performance indicator. An algorithm is proposed to identify the Lowest Loading States (LLS), representing minimal capacity vectors derived from minimal paths (MPs), to compute network reliability efficiently. A case study is presented to demonstrate the practical applicability and computational effectiveness of the proposed approach in a real-world logistics setting.
Keywords:
Time-dependent stochastic logistics network (TSLN)
network reliability
Weibull distribution
customer commitment time
lowest loading state (LLS)
Journal
J
IF:
1.2
Papers:
151
Citations:
1.1K
Organization
No organization information available