scholarly journals A Two-Stage Stochastic Model for Maintenance and Rehabilitation Planning of Pavements

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Mahmoud Ameri ◽  
Armin Jarrahi ◽  
Farshad Haddadi ◽  
Mohammad Hasan Mirabimoghaddam
Keyword(s):  
Stochastic Model ◽  
Deterministic Model ◽  
Two Stage ◽  
Total Cost ◽  
Pavement Maintenance ◽  
Rehabilitation Planning ◽  
Maintenance And Rehabilitation ◽  
Unrealistic Assumption ◽  
Planning Problems

Pavement maintenance and rehabilitation (M&R) plan for maintaining the pavement quality in an acceptable level has direct influence on the required budget. Deterministic budgeting is an unrealistic assumption, so, in this study, a two-stage stochastic model using integer programming is developed to address uncertainty in budgeting. Another aim of this study is to develop an executive model that considers a broad range of parameters at network level maintenance and rehabilitation planning. While having too many details in planning problems makes them more complicated, some restrictions called “technical constraints” were considered to reduce solution time of solving procedure as well as improve M&R activities assignment efficiency. Comparing results of the stochastic model with a deterministic model for a case study revealed that the two-stage stochastic model led to increased total cost compared to the deterministic one due to considering probability in budgeting. However, the developed model provides several M&R plans that are compatible with budget variation.

Establishing optimal project-level strategies for pavement maintenance and rehabilitation – A framework and case study

2012 ◽  
Vol 44 (5) ◽  
pp. 565-589 ◽  
Author(s):  
Muhammad Irfan ◽  
Muhammad Bilal Khurshid ◽  
Qiang Bai ◽  
Samuel Labi ◽  
Thomas L. Morin
Keyword(s):  
Pavement Maintenance ◽  
Maintenance And Rehabilitation ◽  
Project Level

scholarly journals A two-stage stochastic optimization model for the retail multiskilled personnel scheduling problem: a k-chaining policy with $k \ge 2$

2021 ◽  
Vol 19 (1) ◽  
pp. 892-917
Author(s):  
Yessica Andrea Mercado ◽  
◽  
César Augusto Henao ◽  
Virginia I. González
Keyword(s):  
Stochastic Optimization ◽  
Service Sector ◽  
Deterministic Model ◽  
Programming Model ◽  
Simulated Data ◽  
Working Hours ◽  
Mixed Integer ◽  
Two Stage ◽  
Demand Variability

<abstract> <p>Considering an uncertain demand, this study evaluates the potential benefits of using a multiskilled workforce through a k-chaining policy with $k \ge 2$. For the service sector and, particularly for the retail industry, we initially propose a deterministic mixed-integer linear programming model that determines how many employees should be multiskilled, in which and how many departments they should be trained, and how their weekly working hours will be assigned. Then, the deterministic model is reformulated using a two-stage stochastic optimization (TSSO) model to explicitly incorporate the uncertain personnel demand. The methodology is tested for a case study using real and simulated data derived from a Chilean retail store. We also compare the TSSO approach solutions with the myopic approaches' solutions (i.e., zero and total multiskilling). The case study is oriented to answer two key questions: how much multiskilling to add and how to add it. Results show that TSSO approach solutions always report maximum reliability for all levels of demand variability considered. It was also observed that, for high levels of demand variability, a k-chaining policy with $k \ge 2$ is more cost-effective than a 2-chaining policy. Finally, to evaluate the conservatism level in the solutions reported by the TSSO approach, two truncation types in the probability density function (pdf) associated with the personnel demand were considered. Results show that, if the pdf is only truncated at zero (more conservative truncation) the levels of required multiskilling are higher than when the pdf is truncated at 5th and 95th percentiles (less conservative truncation).</p> </abstract>

BRIDGE LANE DIRECTION SPECIFICATION FOR SUSTAINABLE TRAFFIC MANAGEMENT

2006 ◽  
Vol 23 (02) ◽  
pp. 141-154
Author(s):  
JAMES R. FOULDS ◽  
L. R. FOULDS
Keyword(s):  
Dynamic Programming ◽  
Traffic Flow ◽  
Traffic Management ◽  
Deterministic Model ◽  
Special Structure ◽  
Traffic Network ◽  
Programming Formulation ◽  
Traffic Planning ◽  
Planning Problems

We present a deterministic model that specifies lane direction in a multi-laned bridge that has a movable barrier that divides the two directions of traffic flow, in order to reduce congestion. A probabilistic dynamic programming formulation for a stochastic extension of the model is also presented. Analysis of the special structure of the dynamic programming formulation provides new insights into important aspects of certain traffic planning problems and represents a useful addition to the traffic network planner's toolkit. A case study involving the lane direction management of an actual bridge is also provided.

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