scholarly journals Optimal Coordinated Strategy Analysis for the Procurement Logistics of a Steel Group

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lianbo Deng ◽  
Zhuqiang Qiu ◽  
Pengfei Liu ◽  
Wenzhong Xiao
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Quality Requirement ◽  
Strategy Analysis ◽  
Logistics System ◽  
Evaluation Result ◽  
Objective Model ◽  
Annealing Algorithm ◽  
Procurement Strategy ◽  
Single Objective

This paper focuses on the optimization of an internal coordinated procurement logistics system in a steel group and the decision on the coordinated procurement strategy by minimizing the logistics costs. Considering the coordinated procurement strategy and the procurement logistics costs, the aim of the optimization model was to maximize the degree of quality satisfaction and to minimize the procurement logistics costs. The model was transformed into a single-objective model and solved using a simulated annealing algorithm. In the algorithm, the supplier of each subsidiary was selected according to the evaluation result for independent procurement. Finally, the effect of different parameters on the coordinated procurement strategy was analysed. The results showed that the coordinated strategy can clearly save procurement costs; that the strategy appears to be more cooperative when the quality requirement is not stricter; and that the coordinated costs have a strong effect on the coordinated procurement strategy.

scholarly journals Optimizations of network layout and transport service frequencies in view of interests of transit line operators and utilizers

2019 ◽  
Vol 50 (2) ◽  
pp. 47-55
Author(s):  
Xiaojing Zhu ◽  
Xuesong Feng ◽  
Lukai Zhang ◽  
Weixin Hua
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Travel Time ◽  
Simulated Annealing Algorithm ◽  
Solution Algorithm ◽  
Operating Costs ◽  
Network Layout ◽  
Total Travel Time ◽  
Objective Model ◽  
Annealing Algorithm

In order to optimize transit network layout and service frequencies from the view point of operators and utilizers, this research constructs a multi-objective model and proposes the solution algorithm. The model is established from the perspective of operators with the goal of minimizing total operating costs for one day, and from the perspective of the utilizers to minimize the total travel time, respectively. Moreover, simulated annealing algorithm and genetic algorithm are combined to solve the proposed multi-objective model. Simulated annealing algorithm is used as the main framework of the solution algorithm to minimize operating costs, while genetic algorithm is used as the subroutine of simulated annealing algorithm to optimize total travel time. The application results of a numerical experiment verified that the proposed optimization model and the solution algorithm are able to optimize the network layout and service frequencies at the same time.

scholarly journals A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yanhui Li ◽  
Hao Guo ◽  
Lin Wang ◽  
Jing Fu
Keyword(s):  
Supply Chain ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Inventory Routing ◽  
Logistics System ◽  
Routing Problem ◽  
Problem Model ◽  
Inventory Routing Problem ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

Multi Objective Optimization of Turning Process Using Grey Relational Analysis and Simulated Annealing Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 2926-2932 ◽  
Author(s):  
Farhad Kolahan ◽  
Reza Golmezerji ◽  
Masoud Azadi Moghaddam
Keyword(s):  
Simulated Annealing ◽  
Grey Relational Analysis ◽  
Production Efficiency ◽  
Simulated Annealing Algorithm ◽  
Turning Process ◽  
Multi Objective ◽  
Relational Analysis ◽  
Objective Model ◽  
Annealing Algorithm ◽  
Grey Relational

Multi objective optimizing of machining processes is used to simultaneously achieve several goals such as increased product quality, reduced production time and improved production efficiency. This article presents an approach that combines grey relational analysis and regression modeling to convert the values of multi responses obtained from Taguchi method design of experiments into a multi objective model. The proposed approach is implemented on turning process of St 50.2 Steel. After model development, Analysis of Variance (ANOVA) is performed to determine the adequacy of the proposed model. The developed multi objective model is then optimized by simulated annealing algorithm (SA) in order to determine the best set of parameter values. This study illustrates that regression analysis can be used for high precision modeling and estimation of process variables.

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