scholarly journals Uncertain Optimization of Discrete Supply Networks with Order Delivery Disruption and Risk Preference in the Postepidemic Era

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yang Song ◽  
Yan-qiu Liu ◽  
Qi Sun ◽  
Hai-tao Xu ◽  
Ming-fei Chen
Keyword(s):  
Optimization Model ◽  
Risk Preference ◽  
Firm Risk ◽  
Order Allocation ◽  
Supply Networks ◽  
Uncertain Optimization ◽  
Biobjective Optimization ◽  
Subjective Utility ◽  
Network Disruption ◽  
Distribution Problems

Epidemic blockade leads to increased uncertainty and dynamic supply network disruption. This study considers an uncertain optimization of dynamic supply networks with risk preference and order delivery disruption. Taking the subjective utility of downstream enterprises as a reference point for the utility measurement of order delivery disruption and risk preference, this study constructs a biobjective optimization model with the goal of maximizing the downstream firm’s subjective utility and minimizing the manufacturer’s cost. The influence of each parameter in the downstream firm’s subjective utility function on the integrated optimization was analysed. The research found that the uncertain optimization model with the risk preference of downstream firms for order delivery disruption better controls the actual manufacturer’s order allocation and distribution problems when considering the downstream firms’ behaviour preference characteristics under bounded rationality. When allocating orders, manufacturers should consider that changes in order delivery disruption will cause changes in the subjective utility of downstream enterprises. In the process of multiperiod cooperation between manufacturers and downstream firms, they can obtain downstream firm risk preferences through repeated investigations.

scholarly journals Enhancing the Robustness of the Wireless Power Transfer System to Uncertain Parameter Variations Using an Interval-Based Uncertain Optimization Method

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2032
Author(s):  
Yanting Luo ◽  
Yongmin Yang ◽  
Xisen Wen ◽  
Ming Cheng
Keyword(s):  
Optimization Model ◽  
Wireless Power Transfer ◽  
Optimization Method ◽  
Uncertain Parameter ◽  
System Structure ◽  
Power Transfer ◽  
Wireless Power ◽  
Uncertain Optimization ◽  
Wide Range ◽  
Parameter Variations

Uncertainty commonly exists in the wireless power transfer (WPT) systems for moving objects. To enhance the robustness of the WPT system to uncertain parameter variations, a modified WPT system structure and an interval-based uncertain optimization method are proposed in this paper. The modified WPT system, which includes two Q-type impedance matching networks, can switch between two different operating modes. The interval-based uncertain optimization method is used to improve the robustness of the modified WPT system: First, two interval-based objective functions (mean function and variance function) are defined to evaluate the average performance and the robustness of the system. A double-objective uncertain optimization model for the modified WPT system is built. Second, a bi-level nested optimization algorithm is proposed to find the Pareto optimal solutions of the proposed optimization model. The Pareto fronts are provided to illustrate the tradeoff between the two objectives, and the robust solutions are obtained. Experiments were carried out to verify the theoretical method. The results demonstrated that using the proposed method, the modified WPT system can achieve good robustness when the coupling coefficient, the operating frequency, the load resistance or the load reactance varies over a wide range.

scholarly journals Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 137 ◽  
Author(s):  
Sonia Mari ◽  
Muhammad Memon ◽  
Muhammad Ramzan ◽  
Sheheryar Qureshi ◽  
Muhammad Iqbal
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Fuzzy Optimization ◽  
Order Allocation ◽  
Supply Networks ◽  
Supply Chain Resilience ◽  
Fuzzy Environment ◽  
Selection Decisions ◽  
Low Probability ◽  
Disruption Risks

Modern supply chains are vulnerable to high impact, low probability disruption risks. A supply chain usually operates in such a network of entities where the resilience of one supplier is critical to overall supply chain resilience. Therefore, resilient planning is a key strategic requirement in supplier selection decisions for a competitive supply chain. The aim of this research is to develop quantitative resilient criteria for supplier selection and order allocation in a fuzzy environment. To serve the purpose, a possibilistic fuzzy multi-objective approach was proposed and an interactive fuzzy optimization solution methodology was developed. Using the proposed approach, organizations can tradeoff between cost and resilience in supply networks. The approach is illustrated using a supply chain case from a garments manufacturing company.

A Bi-Objective Order Allocation Optimization Model for Horizontal Manufacturing Collaborative Alliance

2010 ◽  
Vol 102-104 ◽  
pp. 836-840 ◽  
Author(s):  
Fang Qi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimal Solution ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a bi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

A One to Multiple Order Allocation Optimization Model of Horizontal Manufacturing Collaborative Alliance

2012 ◽  
Vol 201-202 ◽  
pp. 996-999
Author(s):  
Jin Gao
Keyword(s):  
Optimization Model ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Order Allocation ◽  
Nsga Ii ◽  
Manufacturing Enterprises ◽  
Proposed Model ◽  
Optimal Solution Set ◽  
Simulation Results ◽  
Enterprise Community

Horizontal manufacturing collaborative alliance is a dispersed enterprise community consisting of several enterprises which produce the same kind of products. To correctly assign order among member companies of horizontal manufacturing collaborative alliance is one of the most important ways to improve the agility and competitiveness of manufacturing enterprises. For the order allocation problem, a multi-objective optimization model is developed to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimization functions. The optimal solution set of Pareto is obtained. The simulation results indicate that the proposed model and algorithm is able to obtain satisfactory solutions.

Uncertain optimization model for multi-echelon spare parts supply system

2017 ◽  
Vol 56 ◽  
pp. 646-654 ◽  
Author(s):  
Meilin Wen ◽  
Qiao Han ◽  
Yi Yang ◽  
Rui Kang
Keyword(s):  
Optimization Model ◽  
Spare Parts ◽  
Supply System ◽  
Uncertain Optimization

scholarly journals Optimization Model Based on Reachability Guarantee for Emergency Facility Location and Link Reinforcement

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wuyang Yu ◽  
Jijun Liu
Keyword(s):  
Facility Location ◽  
Optimization Model ◽  
Location Problem ◽  
Flow Problem ◽  
Transportation Network ◽  
Operating Cost ◽  
Facility Location Problem ◽  
Demand Point ◽  
Limited Budget ◽  
Biobjective Optimization

The reasonable location of emergency facilities plays an important role in both predisaster service and postdisaster relief. Moreover, damage to the transportation network often affects the accessibility of demand points, which can seriously hamper timely rescue operations. Reasonable location of emergency facilities and reinforcement of fragile roads are two important strategies to improve the reachability of demand points. In this paper, we proposed a biobjective optimization model to determine locations of emergency facilities and links to be reinforced given a limited budget. Each demand point is allocated a primary facility and a backup facility, the former can provides normal service, and the latter is prepared for postdisaster relief. One goal of the model is to minimize the operating cost of normal services, and another goal is to maximize the reachability guarantee of demand points. The novelty and contribution of this paper are that we defined the reachability by introducing damage tolerance instead of link failure probability. Based on this, we defined the reachability guarantee to deal with the worst scenario of disasters. By embedding the max-flow problem of the reachability guarantee into the emergency facility location problem, the locations of emergency facilities and links to be reinforced can be determined simultaneously. The methodology is applied to a simplified Sioux Falls transportation network. Results such as the trade-off curve of two goals, budget efficiency, and the effect of reinforcement demonstrated the effectiveness of the model.

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