scholarly journals Signal Timing Optimization Based on Fuzzy Compromise Programming for Isolated Signalized Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Dexin Yu ◽  
Xiujuan Tian ◽  
Xue Xing ◽  
Shutao Gao
Keyword(s):  
Fuzzy Programming ◽  
Compromise Programming ◽  
New Method ◽  
Signalized Intersection ◽  
Exhaust Emission ◽  
Programming Approach ◽  
Signal Timing ◽  
Traffic Capacity ◽  
Delay Cycle ◽  
Weight Coefficients

In order to optimize the signal timing for isolated intersection, a new method based on fuzzy programming approach is proposed in this paper. Considering the whole operation efficiency of the intersection comprehensively, traffic capacity, vehicle cycle delay, cycle stops, and exhaust emission are chosen as optimization goals to establish a multiobjective function first. Then fuzzy compromise programming approach is employed to give different weight coefficients to various optimization objectives for different traffic flow ratios states. And then the multiobjective function is converted to a single objective function. By using genetic algorithm, the optimized signal cycle and effective green time can be obtained. Finally, the performance of the traditional method and new method proposed in this paper is compared and analyzed through VISSIM software. It can be concluded that the signal timing optimized in this paper can effectively reduce vehicle delays and stops, which can improve traffic capacity of the intersection as well.

Designing a sustainable supply chain network for producing high-value products from waste glass

2021 ◽  
pp. 0734242X2199466
Author(s):  
Naeme Zarrinpoor
Keyword(s):  
Supply Chain ◽  
Greenhouse Gas Emission ◽  
Fuzzy Programming ◽  
Solution Procedure ◽  
Waste Glass ◽  
Worker Safety ◽  
Programming Approach ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Proposed Model

This paper aims to design a supply chain network for producing double glazed glass from the recycling of waste glass. All three pillars of sustainability are taken into consideration. The economic objective tries to maximize total profits. The environmental objective considers the energy consumption, the generated waste, the greenhouse gas emission, the water consumption, and the fuel consumption of vehicles. The social objective addresses created job opportunities, the worker safety, the regional development, the worker benefit, and training hours. To solve the model, a two-stage framework based on the group best-worst method and an interactive fuzzy programming approach is developed. The proposed model is validated through a real case study based on waste glass management in the city of Shiraz. It is revealed that when sustainable development goals are approached, a great degree of improvement will be attained in environmental and social aspects without a significant decrease in the economic sustainability. The results also demonstrate that the locations of glass recycling centres are different under economic, environmental, and social pillars, and the proposed model yields an optimal system configuration with a proper satisfaction degree of all objectives. Moreover, applying the proposed solution procedure enables system designers to obtain the most desirable trade-off between different aspects of sustainability.

scholarly journals Bi-objective load balancing multiple allocation hub location: a compromise programming approach

2020 ◽  
Vol 296 (1-2) ◽  
pp. 363-406 ◽  
Author(s):  
Rahimeh Neamatian Monemi ◽  
Shahin Gelareh ◽  
Anass Nagih ◽  
Dylan Jones
Keyword(s):  
Spatial Distribution ◽  
Transportation Cost ◽  
Compromise Programming ◽  
Mixed Integer ◽  
Programming Approach ◽  
Hub Location ◽  
Hub And Spoke ◽  
Classical Models ◽  
Linear Programming Formulation ◽  
Multiple Allocation

AbstractIn this paper we address unbalanced spatial distribution of hub-level flows in an optimal hub-and-spoke network structure of median-type models. Our study is based on a rather general variant of the multiple allocation hub location problems with fixed setup costs for hub nodes and hub edges in both capacitated and uncapacitated variants wherein the number of hub nodes traversed along origin-destination pairs is not constrained to one or two as in the classical models.. From the perspective of an infrastructure owner, we want to make sure that there exists a choice of design for the hub-level sub-network (hubs and hub edges) that considers both objectives of minimizing cost of transportation and balancing spatial distribution of flow across the hub-level network. We propose a bi-objective (transportation cost and hub-level flow variance) mixed integer non-linear programming formulation and handle the bi-objective model via a compromise programming framework. We exploit the structure of the problem and propose a second-order conic reformulation of the model along with a very efficient matheuristics algorithm for larger size instances.

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