Fuzzy Reasoning for Fuzzy Optimization

2002 ◽  
pp. 157-205
Author(s):  
Christer Carlsson ◽  
Robert Fullér
Keyword(s):  
Fuzzy Optimization ◽  
Fuzzy Reasoning

Morphology granule segmentation algorithm based on fuzzy reasoning of image features

2011 ◽  
Vol 30 (12) ◽  
pp. 3278-3280
Author(s):  
Ming HAN ◽  
Lei-min LI ◽  
Yu-qing HUANG
Keyword(s):  
Fuzzy Reasoning ◽  
Image Features ◽  
Segmentation Algorithm

scholarly journals A Fuzzy Optimization Model for the Berth Allocation Problem and Quay Crane Allocation Problem (BAP + QCAP) with n Quays

2021 ◽  
Vol 9 (2) ◽  
pp. 152
Author(s):  
Edwar Lujan ◽  
Edmundo Vergara ◽  
Jose Rodriguez-Melquiades ◽  
Miguel Jiménez-Carrión ◽  
Carlos Sabino-Escobar ◽  
...  
Keyword(s):  
Optimization Model ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Berth Allocation ◽  
Triangular Fuzzy Numbers ◽  
Berth Allocation Problem ◽  
Arrival Times ◽  
Decision Variables ◽  
Short Time

This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times. The model optimizes the use of the quays. The BAP + QCAP, is a NP-hard (Non-deterministic polynomial-time hardness) combinatorial optimization problem, where the decision to assign available quays for each vessel adds more complexity. The imprecise vessel arrival times and the decision variables—berth and departure times—are represented by triangular fuzzy numbers. The model obtains a robust berthing plan that supports early and late arrivals and also assigns cranes to each berth vessel. The model was implemented in the CPLEX solver (IBM ILOG CPLEX Optimization Studio); obtaining in a short time an optimal solution for very small instances. For medium instances, an undefined behavior was found, where a solution (optimal or not) may be found. For large instances, no solutions were found during the assigned processing time (60 min). Although the model was applied for n = 2 quays, it can be adapted to “n” quays. For medium and large instances, the model must be solved with metaheuristics.

scholarly journals A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Li Wang ◽  
Yong Qin ◽  
Jie Xu ◽  
Limin Jia
Keyword(s):  
Optimization Model ◽  
High Speed ◽  
Fuzzy Optimization ◽  
Soft Constraints ◽  
Accurate Approximation ◽  
Total Delay ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Tolerance Approach ◽  
Rail Line

A fuzzy optimization model based on improved symmetric tolerance approach is introduced, which allows for rescheduling high-speed railway timetable under unexpected interferences. The model nests different parameters of the soft constraints with uncertainty margin to describe their importance to the optimization purpose and treats the objective in the same manner. Thus a new optimal instrument is expected to achieve a new timetable subject to little slack of constraints. The section between Nanjing and Shanghai, which is the busiest, of Beijing-Shanghai high-speed rail line in China is used as the simulated measurement. The fuzzy optimization model provides an accurate approximation on train running time and headway time, and hence the results suggest that the number of seriously impacted trains and total delay time can be reduced significantly subject to little cost and risk.

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