Application of multi-objective optimization based on the integration of linear physical programming within collaborative optimization

2012 ◽  
Author(s):  
Zhao Leisheng ◽  
Li Lei
Keyword(s):  
Collaborative Optimization ◽  
Multi Objective Optimization ◽  
Physical Programming ◽  
Multi Objective ◽  
Linear Physical Programming

Collaboration Strategy for a Decentralized Supply Chain Using Linear Physical Programming

2020 ◽  
Vol 14 (5) ◽  
pp. 723-733
Author(s):  
Tomoaki Yatsuka ◽  
Aya Ishigaki ◽  
Surendra M. Gupta ◽  
Yuki Kinoshita ◽  
Tetsuo Yamada ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Discount Rate ◽  
Reduction Rate ◽  
Multi Objective Optimization ◽  
Price Discount ◽  
Physical Programming ◽  
Multi Objective ◽  
Linear Physical Programming ◽  
The Cost

In recent years, the environment surrounding companies has become more challenging. It has become more difficult for many companies in the manufacturing industry to possess all the skills they need, such as production, warehousing, and retailing, so they need to outsource certain skills. In supply chains with several companies, each has an optimal strategy. Specifically, supply chains where the solution is decided through negotiations with their partners are defined as “decentralized supply chains.” In such situations, collaborative relationships are important. One possible approach is replenishment contracts between vendors and buyers under the condition that demand for each buyer is constant. In a buyer-dominated supply chain, because the vendor cannot choose solutions that lower the satisfaction of buyers, it is difficult to change the replenishment intervals. The common replenishment epochs (CRE) strategy is one of the methods used to address this issue. The vendor integrates the buyers’ replenishment timings using CRE and provides a price discount on the products to compensate for the increase in the cost to the buyers. The price discount rate is calculated based on the worst reduction rate in the costs incurred by the buyers based on the economic order quantity (EOQ) model. The optimal CRE and discount rate are decided such that the cost incurred by vendor is minimized. The increased emphasis on the worst reduction rates can potentially lead to biases in buyer satisfaction, and the price discount rate is overestimated. Then, the cost of the vendor increases. Hence, through the negotiations with less satisfied buyers, the vendor changes the CRE so that their satisfaction is improved and the price discount is lower. As a result, the vendor can reduce its cost. This study develops a model to find an improved solution after the negotiations. If satisfaction of multiple players is regarded as multi-objective, a solution of multi-player decision-making is obtained using multi-objective optimization. Linear physical programming (LPP) has been applied as a form of multi-objective optimization, and it is possible to determine the weight coefficients using the preference ranges of the objective functions. In addition, by considering the buyers’ preference levels, the constraints of the discount rates are relaxed and the vendor’s cost can be reduced. Therefore, this study develops a model based on the CRE strategy using LPP.

scholarly journals Physical programming for preference driven evolutionary multi-objective optimization

2014 ◽  
Vol 24 ◽  
pp. 341-362 ◽  
Author(s):  
Gilberto Reynoso-Meza ◽  
Javier Sanchis ◽  
Xavier Blasco ◽  
Sergio García-Nieto
Keyword(s):  
Multi Objective Optimization ◽  
Physical Programming ◽  
Multi Objective

scholarly journals Multi-performance Target Collaborative Optimization Methods for Battery Electric Vehicle

2021 ◽  
Author(s):  
Chen Yawei ◽  
Chen Qian ◽  
Liu Jurui ◽  
Hao Xixiang ◽  
Yuan Chenheng
Keyword(s):  
Power Consumption ◽  
Ride Comfort ◽  
Simulation Software ◽  
Collaborative Optimization ◽  
Pareto Optimum ◽  
Stability Factor ◽  
Multi Objective Optimization ◽  
Analysis Model ◽  
Multi Objective ◽  
Target Values

Abstract The present studies on battery electric vehicles (BEVs) has mainly focused on the single-objective or weighted multi-objective optimization based on energy management, which can not manifest the coupling relationship among the vehicle performance objectives essentially. To optimize the handling stability, ride comfort and economy of BEV, this paper built the stability dynamics analysis model, ride comfort simulation half-car model and power consumption calculation model of BEV, as well as two-point virtual random excitation model on Level B road and proposed related evaluation indexes, including vehicle handling stability factor, weighted acceleration root-mean-square (RMS) value of vertical vibration at the driver’s seat and power consumption per 100 m at a constant speed. The Pareto optimum principle–based multi-objective evolutionary algorithm (MOEA) of BEV was also designed, which was encoded with real numbers and obtained the target values of all optional schemes via MATLAB/Simulink simulation software. The merits and demerits of alternative schemes could be judged according to the Pareto dominance principle, so that alternative schemes obtained after optimization were realizable. The results of simulation experiment suggest that the proposed algorithm can perform the multi-objective optimization on BEV, and obtain a group of Pareto optimum solutions featured by high handling stability, favorable ride comfort and low energy consumption for the decision-makers.

scholarly journals Linear Physical Programming Iteration Method of Multi-Player Multi-Objective Decision Making Supply Chain

2019 ◽  
Vol 39 ◽  
pp. 1649-1657
Author(s):  
Tomoaki Yatsuka ◽  
Aya Ishigaki ◽  
Yuki Kinoshita ◽  
Tetsuo Yamada ◽  
Masato Inoue
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Iteration Method ◽  
Physical Programming ◽  
Multi Objective ◽  
Linear Physical Programming

Multi-Objective Collaborative Optimization of Spring

2014 ◽  
Vol 599-601 ◽  
pp. 362-367
Author(s):  
Yun Peng ◽  
Ai Min Gong ◽  
Hai Yan Huang
Keyword(s):  
Natural Vibration ◽  
Optimization Problems ◽  
Collaborative Optimization ◽  
System Level ◽  
Natural Vibration Frequency ◽  
Multi Objective Optimization ◽  
Design Environment ◽  
Multi Objective ◽  
Relative Value ◽  
Structural Mass

A framework for solving the multi-objective optimization problems of spring in multidisciplinary design environment is advised in this paper. Based on the collaborative optimization (CO) algorithm, a new system level objective function is advised to minimize relative value among the structural mass, the height and the natural vibration frequency. The proposed models were demonstrated with a multi-objective optimization problem of a spring. The optimal design of the spring obtained indicates the great potential of decreasing structural mass and vibration level and increasing natural frequency reserve under the constraints. The analysis progress and results show that the model is feasible and well-suited for using in actual optimization problems of spring design.

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