Solving the hazmat transport network design problem

In this paper, we consider the problem of network design for hazardous material transportation where the government designates a network, and the carriers choose the routes on the network. We model the problem as a bilevel network flow formulation and analyze the bilevel design problem by comparing it to three other decision scenarios. The bilevel model is difficult to solve and may be ill-posed. We propose a heuristic solution method that always finds a stable solution. The heuristic exploits the network flow structure at both levels to overcome the difficulty and instability of the bilevel integer programming model. Testing on real data shows that the linearization of the bilevel model fails to find stable solutions and that the heuristic finds lower risk networks in less time. Further testing on random instances shows that the heuristically designed networks achieve significant risk reduction over single-level models. The risk is very close to the least risk possible. However, this reduction in risk comes with a significant increase in cost. We extend the bilevel model to account for the cost/risk trade-off by including cost in the first-level objective. The biobjective–bilevel model is a rich decision-support tool that allows for the generation of many good solutions to the design problem.     

A solution process for simulation-based multiobjective design optimization with an application in the paper industry

In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, we consider the integrated design and control problem in paper mill design where the aim is to decrease the investment cost and enhance the quality of paper on the design level and, at the same time, guarantee the smooth performance of the production system on the operational level. In the first stage of the three-stage solution process, a set of solutions involving different trade-offs is generated with a method suited for computationally expensive multiobjective optimization problems using parallel computing. Then, based on the generated solutions an approximation method is applied to create a computationally inexpensive surrogate problem for the design problem and the surrogate problem is solved in the second stage with an interactive multiobjective optimization method. This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem. In the third stage, the solution best corresponding that of stage two is found for the original problem.     

Cell-related location area planning for 4G PCS networks with variable-order Markov model

Location management is a critical issue in personal communication service (PCS) networks, tracking the location of user equipment (UE) with the goal of minimizing total signaling cost. Previous work can be classified into two categories: static and dynamic. Static schemes partition networks into fixed size LAs. However, these schemes are inefficient because they do not take UEs’ mobility and the call arrival rate into account. On the other hand, focusing on individual UEs, dynamic schemes have minimized the location management cost. However, they are difficult to implement because recording the individual information of numerous UEs and planning each of their LAs consume uncontrollable cost. Because of these reasons, we propose a cell-based scheme between static and dynamic schemes. Considering people usually stay in specific zones for long periods and the movement of UEs usually presents a strong moving direction in areas, this study presents a distributed algorithm by employing variable-order Markov models to find the mobility characteristic shared by UEs to plan better LAs with lower location management cost. When the order of Markov model is set to 1, our method is equal to a pure cell-centric LAP scheme; while the order of Markov model is high, it is more like a profile-based dynamic scheme. So, the setting of the order actually is a trade-off problem between the overall location management cost and the computing complexity. We present to retrieve a balance by using the expected location management cost and the number of total states of Markov models. In simulations, the origin–destination matrix (O–D matrix) from the Taipei Rapid Transit Corporation is used for representing the association between two cells. Simulation results demonstrate that the proposed scheme achieves good performance.     

Multi-objective shape optimization of transonic airfoil sections using swarm intelligence and surrogate models

In this article, the optimization problem of designing transonic airfoil sections is solved using a framework based on a multi-objective optimizer and surrogate models for the objective functions and constraints. The computed Pareto-optimal set includes solutions that provide a trade-off between maximizing the lift-to-drag ratio during cruise and minimizing the trailing edge noise during the aircraft’s approach to landing. The optimization problem was solved using a recently developed multi-objective optimizer, which is based on swarm intelligence. Additional computational intelligence tools, e.g., artificial neural networks, were utilized to create surrogate models of the objective functions and constraints. The results demonstrate the effectiveness and efficiency of the proposed optimization framework when applied to simulation-based engineering design optimization problems.     

Design and Analysis of Supply Chain Networks with Low Carbon Emissions

Low carbon supply chain network design is a multi-objective decision-making problem that involves a trade-off between low carbon emissions and cost. This study calculates the carbon footprint, wherein the greenhouse gases (GHGs) emissions data are based on carbon footprint standards. Many firms have redesigned their supply chain networks to reduce their GHG emissions. Furthermore, the production capacities and costs are collected and evaluated by using Pareto optimal solutions. In order to achieve the optimal solutions, a normal constraint method is used to formulate a mathematical model to meet two objectives: low carbon emissions and low cost. A case study is also presented to demonstrate the predictive ability of this model. The result shows that it is possible to reduce carbon emissions and lower cost simultaneously.     

Dual problem of sorptive barrier design with a multiobjective approach

Sorptive barrier technology is a recently developed tool to separate hazardous contaminants from friendly environment. The design of sorptive barrier refers to configuring different amendments with sorptive ability of organic pollutant, which is an integer programming problem and a relatively time consuming problem as well. In this paper, sorptive barrier design is newly modeled in a biobjective optimization approach, in which the dual problem of sorptive barrier design is deduced. The objectives are to minimize the financial cost and the amount of pollutant leaking through barriers. Then an opposition-based adaptive multiobjective differential evolution algorithm (MODEA-OA) is applied to handle the proposed model. The Pareto optimal front obtained by MODEA-OA spreads accurately and evenly in all three instances tested. To select extreme optimal solutions, the original and dual sorptive barrier design problems can be solved simultaneously. This study suggests that modeling barrier design as a multiobjective optimization problem is an effective approach.

     

Comparing Restoration Concepts Using Optimal Network Configurations with Integrated Hardware and Routing Decisions

We investigate the impact of link and path restoration on the cost of telecommunication networks. The main observation is that the cost of an optimal network configuration is almost independent of the restoration concept if (i) the installation of network elements (ADMs, DXCs, or routers) and interface cards, (ii) link capacities, and (iii) working and restoration routings are simultaneously optimized.We present a mixed-integer programming model which integrates all these decisions. Using a branch-and-cut algorithm (with column generation to deal with all admissible routing paths), we solve structurally different real-world based problem instances and show that the cost of optimal solutions is almost independent of the used restoration concept.In addition, we optimize spare capacities based on predetermined shortest working paths with respect to different link weights. On our test instances, the additional cost of solutions obtained with this sequential approach, compared to simultaneous optimization of working and restoration routings, varies between 0 and 164%.Sebastian Orlowski has studied mathematics at the Technical University of Berlin, Germany. Since June 2003, he has been a research assistant at the Zuse Institute Berlin (ZIB), where he works on the design of cost-efficient telecommunication networks under survivability constraints. The current focus of his research within the DFG Research Center MATHEON is multi-layer network planning.Roland Wessäly graduated from the Technical University of Berlin with a Masters Degree in Computer Science. Since 1994 he has been a member of the optimization group at the Zuse Institute Berlin (ZIB). He developed optimization methods for the design of survivable capacitated telecommunication networks as part of his PhD Thesis in Mathematics (finished in April 2000). In 2001 he received the Vodafone Innovations award for his scientific work on network design. Since 2000 he has been managing director of the ZIB spin-off atesio GmbH, a company specialized on planning and optimization algorithms for telecommunication network operators.     

An extension of duality to a game-theoretic framework

This paper extends some duality results from a standard optimization setup to a noncooperative (Nash) game framework. A Nash game (NG) with coupled constraints is considered. Solving directly such a coupled NG requires coordination among possibly all players. An alternative approach is proposed based on its relation to a special constrained optimization problem for the NG-game cost function, with respect to the second argument that admits a fixed-point solution. Specific separability properties of the NG-game cost are exploited and duality results are developed. This duality extension leads naturally to a hierarchical decomposition into a lower-level NG with no coupled constraints, and a higher-level system optimization problem. In the second part of the paper these theoretical results are applied to a coupled NG with coupled constraints as encountered in optical networks.     

云计算环境中面向DAG任务的多目标调度算法

为了实现任务执行效率与执行代价的同步优化,提出了一种云计算环境中的DAG任务多目标调度优化算法。算法将多目标最优化问题以满足Pareto最优的均衡最优解集合的形式进行建模,以启发式方式对模型进行求解;同时,为了衡量多目标均衡解的质量,设计了基于hypervolume方法的评估机制,从而可以得到相互冲突目标间的均衡调度解。通过配置云环境与三种人工合成工作流和两种现实科学工作流的仿真实验测试,结果表明,比较同类单目标算法和多目标启发式算法,算法不仅求解质量更高,而且解的均衡度更好,更加符合现实云的资源使用特征与工作流调度模式。     

基于基因表达式编程的TSP问题求解

利用遗传算法求解组合优化问题时,需要特有的遗传算子,才能在候选解空间中有效搜索和进化。基因表达式编程（GEP）是进化计算家族的新成员。旅游商问题（TSP）是典型的组合优化问题,得到了广泛的研究,它的研究成果将对求解NP类问题产生重要影响。基于基因表达式编程（GEP）来解决TSP问题,引入适用组合优化的遗传算子：逆串,基因串的删/插等,最后进行了实验,展示GEP解决TSP问题的方法。实验表明GEP能有效解决TSP问题,设计的系统是强壮健康,其求解速度快且解的质量好。     

Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator

Most controllers optimization and design problems are multiobjective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Instead of aiming at finding a single solution, the multiobjective optimization methods try to produce a set of good trade-off solutions from which the decision maker may select one. Several methods have been devised for solving multiobjective optimization problems in control systems field. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Recently, Multiobjective Evolutionary Algorithms (MOEAs) have been applied to control systems problems. Compared with mathematical programming, MOEAs are very suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of solutions and find a number of Pareto optimal solutions in a single run of algorithm. Starting from a set of initial solutions, MOEAs use iteratively improving optimization techniques to find the optimal solutions. In every iterative progress, MOEAs favor population-based Pareto dominance as a measure of fitness. In the MOEAs context, the Non-dominated Sorting Genetic Algorithm (NSGA-II) has been successfully applied to solving many multiobjective problems. This paper presents the design and the tuning of two PID (Proportional–Integral–Derivative) controllers through the NSGA-II approach. Simulation numerical results of multivariable PID control and convergence of the NSGA-II is presented and discussed with application in a robotic manipulator of two-degree-of-freedom. The proposed optimization method based on NSGA-II offers an effective way to implement simple but robust solutions providing a good reference tracking performance in closed loop.     

一种全终端网络可靠性多目标优化模型及求解

分析了全终端网络可靠性设计问题,针对单目标优化存在的不足,建立了一种更加贴近工程实际的极大化可靠度,同时极小化投资成本的多目标优化模型,并利用模拟退火算法对其进行了优化求解。计算机仿真实例表明:模拟退火算法在求解此问题时,无论是在Pareto解的数量上还是在Pareto解的范围上都能得到较好的优化效果。     

Design of reinforced concrete road vaults by heuristic optimization

This paper aims at the automatic design and cost minimization of reinforced concrete vaults used in road construction. This paper presents three heuristic optimization methods: the multi-start global best descent local search (MGB), the meta-simulated annealing (SA) and the meta-threshold acceptance (TA). Penalty functions are used for unfeasible solutions. The structure is defined by 49 discrete design variables and the objective function is the cost of the structure. All methods are applied to a vault of 12.40 m of horizontal free span, 3.00 m of vertical height of the lateral walls and 1.00 m of earth cover. This paper presents two original moves of neighborhood search and an algorithm for the calibration of SA-TA algorithms. The MGB algorithm appears to be more efficient than the SA and the TA algorithms in terms of mean results. However, the SA outperforms MGB and TA in terms of best results. The optimization method indicates savings of about 10% with respect to a traditional design.     

Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems

Owing to the increasing number of vehicles in vehicular cyber-physical systems (VCPSs) and the growing popularity of various services or applications for vehicles, cellular networks are being severely overloaded. Offloading mobile data traffic through Wi-Fi or a vehicular ad hoc network (VANET) is a promising solution for partially solving this problem because it involves almost no monetary cost. We propose combination optimization to facilitate mobile data traffic offloading in emerging VCPSs to reduce the amount of mobile data traffic for the QoS-aware service provision. We investigate mobile data traffic offloading models for Wi-Fi and VANET. In particular, we model mobile data traffic offloading as a multi-objective optimization problem for the simultaneous minimization of mobile data traffic and QoS-aware service provision; we use mixed-integer programming to obtain the optimal solutions with the global QoS guarantee. Our simulation results confirm that our scheme can offload mobile data traffic by up to 84.3% while satisfying the global QoS guarantee by more than 70% for cellular networks in VCPSs.     

基于网络编码的分布式无线光网络在线优化

最近, 一种集成骨干光传输网络、无源光网络和无线接入网的混合宽带无线光网络被提出。这种网络具有大带宽、低费用和无处不在的信息接入等特点。考虑在这种网络中的基于网络编码的多播会话的设计问题, 使得网络效用最大化, 而布网的费用最小化。这个问题被转换为一个混合的整数非线性规划问题, 精确求解极其困难。为了使得问题简化, 采用了一种两步优化方法进行求解, 交替地为多播会话选择光网络单元和网关。在每一次迭代过程中需要解决两个问题：光网络的网络编码设计问题和无线网络的用户和带宽分配问题。前者通过基于拉格朗日对偶分解的分布式方法实现; 后者通过广义Benders分解实现。通过仿真验证了所采用的方法的有效性。     

A multi-objective optimization for green supply chain network design

In this paper, we study a supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the trade-off between the total cost and the environment influence. We conduct a comprehensive set of numerical experiments. The results show that our model can be applied as an effective tool in the strategic planning for green supply chain. Meanwhile, the sensitivity analysis provides some interesting managerial insights for firms.     

Fuzzy two-stage quadratic programming for planning solid waste management under uncertainty

In this study, a fuzzy two-stage quadratic programming (FTSQP) method is developed for planning waste-management systems under uncertainty. It incorporates approaches of fuzzy quadratic programming and two-stage stochastic programming within a general optimization framework, to better reflect uncertainties expressed as probability-density and fuzzy-membership functions. The FTSQP can be used for analyzing various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. Moreover, using fuzzy quadratic terms rather than linear ones, the proposed method can improve upon the existing fuzzy linear programs through (a) more effectively optimizing the general satisfaction of the objective and constraints, (b) minimizing the variation of satisfaction degrees among the constraints and leading to more robust solutions, and (c) reflecting the trade-off between the system cost and the constraint-violation risk. The developed method is applied to a case study of municipal solid waste management. The results indicate that reasonable solutions have been generated. They will allow in-depth analyses of trade-offs between environmental and economic objectives as well as those between system cost and decision-maker's satisfaction degree.     

Weighted Constraint Aggregation in Fuzzy Optimization

Many practical optimization problems are characterized by some flexibility in the problem constraints, where this flexibility can be exploited for additional trade-off between improving the objective function and satisfying the constraints. Fuzzy sets have proven to be a suitable representation for modeling this type of soft constraints. Conventionally, the fuzzy optimization problem in such a setting is defined as the simultaneous satisfaction of the constraints and the goals. No additional distinction is assumed to exist amongst the constraints and the goals. This paper proposes an extension of this model for satisfying the problem constraints and the goals, where preference for different constraints and goals can be specified by the decision-maker. The difference in the preference for the constraints is represented by a set of associated weight factors, which influence the nature of trade-off between improving the optimization objectives and satisfying various constraints. Simultaneous weighted satisfaction of various criteria is modeled by using the recently proposed weighted extensions of (Archimedean) fuzzy t-norms. The weighted satisfaction of the problem constraints and goals are demonstrated by using a simple fuzzy linear programming problem. The framework, however, is more general, and it can also be applied to fuzzy mathematical programming problems and multi-objective fuzzy optimization.     

Capacity assignment in computer communication networks with unreliable links

This paper considers the problem of selecting the optimum capacities of the links in a computer communication network which employs unreliable links. Given the nodes, links, link probabilities, grade of service and cost functions of the network, the objective of this problem is to find the optimum link capacities that minimize the network design cost, subject to the constraint equation involving the grade of service. This is essentially a combinatorial optimization problem. A general methematical model for this problem is formulated and a set of feasible solutions is obtained using Lagrangean relaxation and subgradient optimization techniques. A simulation study has been performed to verify the model, and favourable results obtained for a variety of nontrivial networks.     

Parallel machine selection and job scheduling to minimize machine cost and job tardiness

This paper discusses the problem of simultaneously selecting and scheduling parallel machines to minimize the sum of machine holding cost and job tardiness cost. A combinatorial optimization model is developed for this purpose. Solving the developed model is NP-hard. A heuristic algorithm is developed to locate the optimal or near optimal solutions based on a Tabu search mechanism specially designed to control the search process in the solution neighborhood for jobs scheduled on specific machines. Numerical examples show that the solutions of the model lead to compromises between the system cost related to machine selection and the operational cost related to job tardiness penalties. The examples also show that the developed algorithm is effective and computationally efficient.