Risk approaches for delivering disaster relief supplies

We consider the problem of designing the logistic system to assure adequate distribution of relief aid in a post-natural-disaster situation, when damages to infrastructure may disrupt the delivery of relief aid. The problem is formulated as a multi-objective optimization problem, encompassing three objective functions of central interest in such problems. The first objective function is a measure of risk (various forms of such risk are analyzed). The second objective function measures the coverage provided by the logistic system in the distribution of relief aid to disaster victims. The third objective function represents total travel time. We focus on the risk of delivery tours for relief supplies, where risk here captures the threat that potential tours become impassable after the natural hazard event. In order to cope with a range of different natural disasters and policy objectives, we develop five approaches emphasizing different measures of tour-dependent risk. To cover both earthquake and flood risks, we consider correlated as well as uncorrelated risk measures. We develop a two-phase solution approach to reflect the dictates of real-world disaster relief motivating this analysis. The first phase generates potentially Pareto-optimal solutions to the overall multi-objective logistic design problem with respect to three objectives. For any given risk measure, the first-phase design problem is formulated as a multi-objective integer program and a memetic algorithm is proposed as the solution approach. The second phase is an enrichment procedure to generate a broader range of potentially Pareto-optimal alternatives. The suggested approach is tested on real-world data from the province of Manabí in Ecuador and the results associated with the different risk measures are analyzed to illustrate the value of the proposed approach for the design of disaster relief networks.     

Identification of Constitutive Parameters using Hybrid ANN multi-objective optimization procedure

This paper deals with the identification of material parameters for an elastoplastic behaviour model with isotropic hardening using several experimental tests at the same time. But, these tests are generally inhomogeneous and finite element simulations are necessary for their analysis. Therefore an inverse analysis is carried out and the identification problem is converted into a multi-objective optimization where prohibitive computing time is required. We propose in this work a hybrid approach where Artificial Neural Networks (ANN) are trained by finite element results. Then, the multi objective procedure calls the ANN function in place of the finite element code. The proposed approach is exemplified on the identification of non-associative Hill’48 criterion and Voce parameters model of the Stainless Steel AISI 304.     

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.     

外卖配送路径多目标实时优化研究

随着外卖行业的不断发展,外卖配送的路径优化问题已引起学者们的广泛关注。但现有研究未将骑手的目标考虑在内,且未考虑动态场景下多目标如何设定权重的问题。因此,本文对外卖配送路径的多目标实时优化进行深入研究。建立多目标外卖配送路径优化模型。该模型不仅考虑订单履行时间、平台利润和骑手服务质量3个常用的目标,另外增加骑手等待时间和骑手空驶距离这两个目标,充分将外卖平台、顾客和骑手的目标综合考虑。设计动态调整权重的多目标外卖配送路径启发式算法,解决动态场景下多目标权重如何设定的问题。通过外卖配送的实时数据进行算例分析。结果表明,本文提出的算法可以有效对多目标的外卖配送问题进行实时路径优化,且订单的密集程度对骑手等待时间和订单履行时间有直接的影响。     

基于成本/收益权衡的多元需求FIP选址研究

需求种类分析对选址决策至关重要,考虑了消费者需求种类为多元化,且各类需求间有影响的网络上截流(FIP)选址问题,引入路径上需求损失函数,建立了收益最大和总的建设成本最小的双目标的多元需求FIP(mDFIP)的新模型。所建立的模型为NP-hard问题,设计了多目标演化算法求解模型,最后通过具体的算例与基于ε约束法的分支定界法进行比较,结果表明在求解效率上多目标演化算法具有很大优势。     

飞机总体设计中的一种多目标鲁棒优化方法

 提出一种基于灵敏度的多目标鲁棒优化方法。针对各维设计变量存在扰动的情况,在原约束多目标优化模型上,附加偏差目标函数,并采用最差估计法对约束条件进行鲁棒可行性调整。采用全局敏度方程方法来计算目标函数和约束函数对设计变量的敏度,进而采用Pareto遗传算法搜索约束多目标优化问题的非劣解集,设计者可以根据不同的设计准则从中选择合适的设计点。将上述方法用于飞机总体参数优化设计,并与采用常规优化方法所得的优化结果进行了分析和比较。     

An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0-1 knapsack model

In this paper, a general framework for child injury prevention and a multi-objective, multi-dimensional mixed 0-1 knapsack model were developed to determine the optimal time to introduce preventive measures against child injuries. Furthermore, the model maximises the prevention of injuries with the highest risks for each age period by combining preventive measures and supervision as well as satisfying budget limits and supervision time constraints. The risk factors for each injury, variable, and time period were based on risk priority numbers (RPNs) obtained from failure mode and effects analysis (FMEA) methodology, and these risk factors were incorporated into the model as objective function parameters. A numerical experiment based on several different situations was conducted, revealing that the model provided optimal timing of preventive measures for child injuries based on variables considered.     

采用多目标进化模型的无监督故障特征选择算法

高维故障特征数据易影响诊断的处理速度和识别率,而传统单目标特征选择算法易融入主观偏好,从而影响特征选择的质量。为此,提出一种无监督的多目标进化特征选择算法。采用熵度量作为相关度目标,采用相关系数的概念设计了冗余度目标,算法同时将这两个目标作为优化对象;利用样本在各个特征上的分布信息,设计了导向性的种群初始化过程和变异算子,以提高算法的优化能力;还利用集成的方法得到了所有特征的重要度序列。对5组UCI数据和3组往复式压缩机故障数据的测试结果表明,该算法比已有的几种特征选择算法更具优势。     

A Compromise Programming to Task Assignment Problem in Software Development Project

The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional rules. To support the decision-maker to evaluate different decision strategies, we use compromise programming to transform multi-objective optimization (MOP) into a single-objective problem. We designed a genetic algorithm scheme to solve the transformed problem. The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’ fitness function. The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives. These are achieved through a combination of non-preference and preference approaches. The experimental results show that the proposed method worked well on the tested dataset.     

A meshless Pareto-optimal method for topology optimization

A meshless Galerkin Pareto-optimal method is proposed for topology optimization of continuum structures in this paper. The compactly supported radial basis function (CSRBF) is used to create shape functions. The shape function is constructed by meshfree approximations based on a set of unstructured field nodes. Considering the Pareto-optimality theory, the initial single objective topology optimization problem is transformed into multi-objective problem. The optimum solution is traced via the Pareto-optimal frontier in a computationally effective manner. The optimal problem does not need to be solved directly. Finally, several examples are used to prove the validity and effectiveness of the proposed approach.     

Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system

A cooling tower assisted vapor compression refrigeration machine has been considered for optimization with multiple criteria. Two objective functions including the total exergy destruction of the system (as a thermodynamic criterion) and the total product cost of the system (as an economic criterion), have been considered simultaneously. A thermodynamic model based on energy and exergy analyses and an economic model according to the Total Revenue Requirement (TRR) method have been developed. Three optimized systems including a single-objective thermodynamic optimized, a single-objective economic optimized and a multi-objective optimized are obtained. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from the Pareto frontier has been presented. The exergetic and economic results obtained for three optimized systems have been compared and discussed. The results have shown that the multi-objective design more acceptably satisfies generalized engineering criteria than other two single-objective optimized designs.     

Multi-objective mixed integer programming and an application in a pharmaceutical supply chain

Multi-objective integer linear and/or mixed integer linear programming (MOILP/MOMILP) are very useful for many areas of application as any model that incorporates discrete phenomena requires the consideration of integer variables. However, the research on the methods for the general multi-objective integer/mixed integer model has been scant when compared to multi-objective linear programming with continuous variables. In this paper, an MOMILP is proposed, which integrates various conflicting objectives. We give importance to the imprecise nature of some of the critical factors used in the modelling that can influence the effectiveness of the model. The uncertainty and the hesitation arising from estimating such imprecise parameters are represented by intuitionistic fuzzy numbers. The MOMILP model with intuitionistic fuzzy parameters is first converted into a crisp MOMILP model, using appropriate defuzzification strategies. Thereafter, the MOMILP is transformed into a single objective problem to yield a compromise solution with an acceptable degree of satisfaction, using suitable scalarisation techniques such as the gamma-connective technique and the minimum bounded sum operator technique. The proposed solution method is applied to several test problems and a multi-objective pharmaceutical supply chain management model with self generated random data.     

带软时间窗的同时取送货车辆路径问题研究

考虑软时间窗下的车辆路径问题,客户点常伴有同时取送货的双重需求。针对此类问题,通过对软时间窗、车辆在途前后时间关系及二者融合问题进行刻画,同时将车辆行驶距离、车辆使用数、违反软时间窗总时间、客户满意度等纳入综合考量,构建相应混合整数非线性规划(mixed integer nonlinear programming, MINLP)模型。设计相应多目标优化求解算法,运用理想点法对目标函数进行转化,将多目标优化问题转化为单目标优化问题。结合相应算例集,运用LINGO 17.0全局求解程序求得每组算例的全局最优解。结果表明,针对带软时间窗的同时取送货车辆路径问题(vehicle routing problem with simultaneous pick-up and delivery and soft time windows, VRPSPDSTW),所建模型及算法是有效且可行的。     

一类具有三重指标的分批排序问题

本文研究现代排序问题一具有三重指标的批容量无限制平行分批排序问题。第一指标为最大延迟,第二指标为最大完工时间,第三指标为关于工件完工时间的任意正规函数。本文通过分析前两个指标最优解的性质给出了此问题的多项式时间算法。     

Optimization in CNC end milling of UNS C34000 medimum leaded brass with multiple surface roughnesses characteristics

The present study highlights a multi-objective optimization problem by applying utility concept coupled with Taguchi method through a case study in CNC end milling of UNS C34000 medium leaded brass. The study aimed at evaluating the best process environment which could simultaneously satisfy multiple requirements of surface quality. In view of the fact, the traditional Taguchi method cannot solve a multi-objective optimization problem; to overcome this limitation, utility theory has been coupled with Taguchi method. Depending on Taguchi’s Lower-the-Better (LB) response criteria; individual surface quality characteristics has been transformed into corresponding utility values. Individual utility values have been aggregated finally to compute overall utility degree which serves as representative objective function for optimizing using Taguchi method. Utility theory has been adopted to convert a multi-response optimization problem into a single response optimization problem; in which overall utility degree serves as the representative single objective function for optimization. The study of combined utility theory and Taguchi method for predicting optimal setting. Based on Taguchi’s Signal-to-Noise ratio (S/N), analysis has been made on the overall utility degree and optimal process environment has been selected finally which corresponds to highest S/N Ratio. Optimal result has been verified through confirmatory test. The case study indicates application feasibility of the aforesaid methodology proposed for multiresponse optimization and off-line control of multiple surface quality characteristics in CNC end milling.     

Springback control of sheet metal forming based on the response-surface method and multi-objective genetic algorithm

An automated optimization method by the integration of the response-surface method and an optimization algorithm is presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, a multi-objective genetic algorithm is applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. The response-surface model is employed as a fast analysis tool to surrogate the time-consuming finite-element procedure in the iterations of the multi-objective genetic algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is more efficient than the traditional manual finite-element procedure and the ‘trial and error’ approach for springback controlling.     

Application of chance-constrained programming based on multi-objective simulated annealing to solve a mineral blending problem

This paper addresses the optimal blending of different available ores in such a way that the total expected cost of buying ore is minimized while satisfying the quality specifications. The risk limitation criterion used consists of the simultaneous minimization of the variance of the total cost. The problem is solved by Chance-Constrained Programming (CCP) based on multi-objective simulated annealing. The technique is able to deal with the stochastic nature of the variables in the blending problem. In generic form the objectives are to minimize expected value and standard deviation of cost in such a way as to meet the blend requirements within the specified reliability level. The variability of each variable in each flow is quantified by semi-variograms. Each flow is simulated to reproduce the characteristics, or behaviour, of the phenomenon as observed in the available data. The expected value of each variable in each flow is calculated by averaging of the simulated values. The problem expressed in terms of CCP is transformed into an appropriate deterministic equivalent, which is a non-linear optimization problem. The new form of the problem is solved by multi-objective simulated annealing. A case study is carried out to demonstrate the technique. The problem is formulated in terms of iron, silica and alumina grades with four ore sources. Pros and cons of the technique are discussed.     

Two-stage stochastic programming with robust constraints for the logistics network post-disruption response strategy optimization

Logistics networks (LNs) are essential for the transportation and distribution of goods or services from suppliers to consumers. However, LNs with complex structures are more vulnerable to disruptions due to natural disasters and accidents. To address the LN post-disruption response strategy optimization problem, this study proposes a novel two-stage stochastic programming model with robust delivery time constraints. The proposed model jointly optimizes the new-line-opening and rerouting decisions in the face of uncertain transport demands and transportation times. To enhance the robustness of the response strategy obtained, the conditional value at risk (CVaR) criterion is utilized to reduce the operational risk, and robust constraints based on the scenario-based uncertainty sets are proposed to guarantee the delivery time requirement. An equivalent tractable mixed-integer linear programming reformulation is further derived by linearizing the CVaR objective function and dualizing the infinite number of robust constraints into finite ones. A case study based on the practical operations of the JD LN is conducted to validate the practical significance of the proposed model. A comparison with the rerouting strategy and two benchmark models demonstrates the superiority of the proposed model in terms of operational cost, delivery time, and loading rate.     

A multi-objective scatter search for the ladle scheduling problem

We consider the ladle scheduling problem, which can be regarded as a vehicle routing problem with semi-soft time windows and adjustment times. The problem concerns allocating ladles to serve molten steel based on a given steelmaking scheduling plan, and determining the modification operations for the empty ladles after the service process. In addition, combining the controllable processing time of molten steel, the other aspect of the problem is to determine the service start times taking into consideration the technological constraints imposed in practice. We present a non-linear mathematical programming model with the conflicting objectives of minimising the occupation ratio of the ladles and maximising the degree of satisfaction with meeting the soft windows. To solve the multi-objective model, we develop a new scatter search (SS) approach by re-designing the common components of SS and incorporating a diversification generator, a combination method and a diversification criterion to conduct a wide exploration of the search space. We analyse and compare the performance of the proposed approach with a multi-objective genetic algorithm and with manual scheduling adopted in practical production using three real-life instances from a well-known iron–steel production plant in China. The computational results demonstrate the effectiveness of the proposed SS approach for solving the ladle scheduling problem.     

A multi-objective model for risk mitigating in supply chain design

The goal of this study is to recognise various factors for responsive SCs that affect supply risk and model their impact on SC design and operation. We propose a conceptual model for SC responsiveness that encompasses practices such as flexibility, agility, internal integration, and visibility. This conceptual model is utilised to build up a multi-objective, multi-period SC design and operation model. A heuristic algorithm is developed to find the supplier, product, period, and production rate for the numerical problem. The improved genetic algorithm (GA) produces solutions with more accuracy in considerably less time than a traditional GA. Finally, an approach to prioritise the objective functions is developed that allows managers to focus on specific objective functions more than optimum values. This approach provides risk-averse, responsiveness-oriented, cost-effective managers the capability to set priorities based on their policies.