A multi-objective optimization model for logistic planning in the crisis response phase

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mahdi Ershadi ◽  
Hossein Shams Shemirani
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Crisis Response ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Different Types ◽  
Response Phase ◽  
Logistic Planning

PurposeProper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.Design/methodology/approachThe main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.FindingsThe performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.Practical implicationsThe proposed methodology can be applied to find the best response plan for all crises.Originality/valueIn this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.

Logistic planning for pharmaceutical supply chain using multi-objective optimization model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mahdi Ershadi ◽  
Mohamad Sajad Ershadi
Keyword(s):  
Supply Chain ◽  
Objective Function ◽  
Optimization Model ◽  
Multi Objective Optimization ◽  
Pharmaceutical Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Different Types ◽  
Logistic Planning

Purpose Appropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of pharmaceuticals, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified requests. Design/methodology/approach The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized requests for pharmaceuticals in the network. Besides, the total transportation activities of different types of vehicles and related costs are considered as other objectives. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize the second objective function while maintaining the optimality of the first objective function. The third objective function is optimized based on the optimality of other objective functions, as well. A non-dominated sorting genetic algorithm II-multi-objective particle swarm optimization heuristic method is designed for this aim. Findings The performances of the proposed model were analyzed in different cases and its results for different problems were shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors. Practical implications The proposed methodology can be applied to find the best logistic plan in real situations. Originality/value In this paper, the authors have tried to use a multi-objective optimization model to guide and correct the pharmaceutical supply chain to deal with the related requests. This is important because it can help managers to improve their plans.

A partner selection model for collaborative product innovation from the viewpoint of knowledge collaboration

Kybernetes ◽  
2019 ◽  
Vol 49 (6) ◽  
pp. 1623-1644 ◽  
Author(s):  
Jie Jian ◽  
Milin Wang ◽  
Lvcheng Li ◽  
Jiafu Su ◽  
Tianxiang Huang
Keyword(s):  
Optimization Model ◽  
Product Innovation ◽  
Partner Selection ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Np Hard Problem ◽  
Selection For ◽  
Knowledge Collaboration

Purpose Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration. Design/methodology/approach First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection. Findings A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance. Originality/value Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

scholarly journals Multi-Objective Optimization for Healthcare Waste Management Network Design with Sustainability Perspective

2021 ◽  
Vol 13 (15) ◽  
pp. 8279
Author(s):  
Ali Ebadi Torkayesh ◽  
Hadi Rezaei Vandchali ◽  
Erfan Babaee Tirkolaee
Keyword(s):  
Waste Management ◽  
Objective Function ◽  
Optimization Model ◽  
Social Aspects ◽  
Multi Objective Optimization ◽  
Healthcare Waste ◽  
Multi Objective ◽  
Social Risks ◽  
Proposed Model ◽  
Healthcare Waste Management

Healthcare Waste Management (HWM) is considered as one of the important urban decision-making problems due to its potential environmental, economic, and social risks and damages. The network of the HWM system involves important decisions such as facility locating, inventory management, and transportation management. Moreover, with growing concerns towards sustainable development objectives, HWM systems should address its environmental and social aspects as well as its economic and technical characteristics. In this regard, this paper formulates a novel multi-objective optimization model to empower companies in making optimized decisions considering the economic, environmental, and social aspects. Within the proposed model, the first objective function aims to minimize the transportation costs, processing costs, and establishment costs. The second objective function aims to minimize environmental risks and emissions related to the transportation of waste between facilities. The third objective function aims to maximize job creation opportunities. Formulating these three functions, an Improved Multi-Choice Goal Programing (IMCGP) approach is proposed to solve the multi-objective optimization model, which is then compared with the Goal Attainment Method (GAM). Finally, to show the applicability and feasibility of the proposed model, an illustrative example of healthcare waste management is analyzed, and the results are discussed.

scholarly journals Multi-Objective Optimization Model for P + R and K + R Facilities’ Collaborative Layout Decision

2020 ◽  
Vol 12 (21) ◽  
pp. 8833
Author(s):  
Wei Wang ◽  
Zhentian Sun ◽  
Zhiyuan Wang ◽  
Yue Liu ◽  
Jun Chen
Keyword(s):  
Optimization Model ◽  
Road Traffic ◽  
Supply And Demand ◽  
Optimal Decision ◽  
Multi Objective Optimization ◽  
Service Capacity ◽  
Location Selection ◽  
Multi Objective ◽  
Proposed Model ◽  
Collaborative Decision

In order to reduce the pressure on urban road traffic, multi-modal travel is gradually replacing single-modal travel. Park and ride (P + R) and kiss and ride (K + R) are effective methods to integrate car transportation and rail transit. However, there is often an imbalance between supply and demand in existing car occupant transfer facilities, which include both P + R and K + R facilities. Therefore, we aim to conduct a research on P + R and K + R facilities’ collaborative decision. It first classifies car occupant transfer facilities into types and levels and sets the service capacity of each category. On the premise of ensuring the occupancy of parking spaces, our model aims to maximize the intercepted vehicle mileage and transfer utility and establishes an optimal decision model for car occupant transfer facilities. The model collaboratively decides the facilities in terms of location selection, layout arrangement, and overflow demand conversion to balance the supply and demand. We choose Chengdu as an example, apply the multi-objective optimization model of car occupant transfer facilities, give improved schemes, and further explore the influence of the quantity of facilities on the optimization objectives. The results show that the scheme obtained by the proposed model is significantly better than the existing scheme.

scholarly journals A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

2019 ◽  
Vol 11 (24) ◽  
pp. 6969 ◽  
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu
Keyword(s):  
Optimization Model ◽  
Optimization Method ◽  
High Energy ◽  
Line Balancing ◽  
Multi Objective Optimization ◽  
Automotive Engine ◽  
Multi Objective ◽  
Proposed Model ◽  
Disassembly Line Balancing ◽  
Disassembly Line

Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

Study on an airport gate assignment method based on improved ACO algorithm

Kybernetes ◽  
2018 ◽  
Vol 47 (1) ◽  
pp. 20-43 ◽  
Author(s):  
Wu Deng ◽  
Meng Sun ◽  
Huimin Zhao ◽  
Bo Li ◽  
Chunxiao Wang
Keyword(s):  
Optimization Model ◽  
Optimal Solution ◽  
Ant Colony ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Assignment Method ◽  
Local Optimal Solution ◽  
Gate Assignment ◽  
Update Strategy

Purpose This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement. Design/methodology/approach In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period. Findings In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed. Practical implications The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment. Originality/value An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.

Application of multi-objective optimization techniques to improve the aerodynamic performance of a tunnel ventilation jet fan

2014 ◽  
Vol 229 (1) ◽  
pp. 91-105 ◽  
Author(s):  
Joon-Hyung Kim ◽  
Jin-Hyuk Kim ◽  
Joon-Yong Yoon ◽  
Young-Seok Choi ◽  
Sang-Ho Yang
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Optimization Model ◽  
Forward Direction ◽  
Aerodynamic Performance ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Tunnel Ventilation ◽  
Multi Objective ◽  
Jet Fan

This paper describes the design optimization of a tunnel ventilation jet fan through multi-objective optimization techniques. Four design variables were selected for design optimization. To analyze the performance of the fan, numerical analyses were conducted, and three-dimensional Reynolds-averaged Navier–Stokes equations with a shear stress transport turbulence model were solved. Two objective functions, the total efficiency of the forward direction and the ratio of the reverse direction outlet velocity to the forward direction outlet velocity, were employed, and multi-objective optimization was carried out to improve the aerodynamic performance. A response surface approximation surrogate model was constructed for each objective function based on numerical solutions obtained at specified design points. The non-dominated sorting genetic algorithm with a local search procedure was used for multi-objective optimization. The tradeoff between the two objectives was determined and described with respect to the Pareto-optimal solutions. Based on the analysis of the optimization results, we propose an optimization model to satisfy the objective function. Finally, to verify the performance, experiments with the base model and the optimization model were carried out.

Multi-Objective Optimization Model of Export and Transit Containers Storage in a Transshipment Port Yard

2012 ◽  
Vol 220-223 ◽  
pp. 272-278 ◽  
Author(s):  
Bin Wang ◽  
Tao Yang
Keyword(s):  
Integer Programming ◽  
Objective Function ◽  
Optimization Model ◽  
Storage Capacity ◽  
Multi Objective Optimization ◽  
Planning Period ◽  
Multi Objective ◽  
Numerical Example ◽  
Decision Variables ◽  
Operational Capacity

To effectively improve the competitiveness of port enterprises, container yard stacking optimization is an important way to raise their benefit. A multi-objective optimization model for containers stacking in the storage yard based on 0-1mixed integer programming is built to improve its efficiency. The objective function is to minimize the number of yard cranes used in the storage yard and balance the workload among different blocks during the planning period. The decision variables include the number of transit and export containers assigned to yard-bits, yard cranes distributed to blocks, yard-bits with high and low workload in a block. The constraints include meeting the shipping requirement, storage capacity and operational capacity of yard cranes. A numerical example is given and solved by Lingo9.0. The simulation is done to recover the relation between workload level and the number of yard crane used and the workload balance. The model can be used to yard stacking management and lift its level for a transshipment port.

Study on Optimizing Collaborative Manufacturing Chain

2012 ◽  
Vol 201-202 ◽  
pp. 971-974
Author(s):  
Fang Qi Cheng ◽  
Hao Wu
Keyword(s):  
Optimization Model ◽  
Time Sequence ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Collaborative Manufacturing ◽  
Sequence Constraint ◽  
Proposed Model ◽  
Manufacturing Resources ◽  
Simulation Results ◽  
Manufacturing Chain

For realizing the sharing and optimization deployment of the manufacturing resources, the concept of collaborative manufacturing chain is proposed. For acquiring the optimal collaborative manufacturing chain, a multi-objective optimization model is developed to minimize the comprehensive cost and the whole production load with time-sequence constraint. The simulation results indicate that the proposed model and algorithm are able to obtain satisfactory solutions.

Multi-Objective Optimization Model of Wind Farm and Pumped Storage Power Station

2014 ◽  
Vol 602-605 ◽  
pp. 2897-2900
Author(s):  
Si Qing Sheng ◽  
Xiao Xia Sun
Keyword(s):  
Wind Power ◽  
Optimization Model ◽  
Wind Farm ◽  
Multi Objective Optimization ◽  
Power Station ◽  
Joint System ◽  
Multi Objective ◽  
Proposed Model ◽  
Wind Rate ◽  
Pumped Storage

This paper presents a multi-objective optimization model to solve the volatility and anti load characteristic of wind power. The pumped storage power station is introduced in this paper. Though pumping and watering, the pumped storage power station can cut peak down to smooth the wind power. Given to the economical efficiency of wind farm, operation efficiency of the wind storage joint system is investigated. In order to improve the wind rate, minimize abandon wind is employed in objective function. To demonstrate the effectiveness of the proposed model, a practical example is tested. The result shows that this model is feasible and effective.

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