Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision ***,...
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Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision ***,the existing weapon system-of-systems(SoS)is tightly *** of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon *** the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important *** paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio ***,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping *** service collaboration network to calculate the flexibility and connectivity of each service portfolio is then *** multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.
We consider the constrained multi-objective optimization problem of finding Pareto critical points of difference of convex functions. The new approach proposed by Bento et al. (SIAM J Optim 28:1104-1120, 2018) to stud...
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We consider the constrained multi-objective optimization problem of finding Pareto critical points of difference of convex functions. The new approach proposed by Bento et al. (SIAM J Optim 28:1104-1120, 2018) to study the convergence of the proximal point method is applied. Our method minimizes at each iteration a convex approximation instead of the (non-convex) objective function constrained to a possibly non-convex set which assures the vector improving process. The motivation comes from the famous Group Dynamic problem in Behavioral Sciences where, at each step, a group of (possible badly informed) agents tries to increase his joint payoff, in order to be able to increase the payoff of each of them. In this way, at each step, this ascent process guarantees the stability of the group. Some encouraging preliminary numerical results are reported.
Based on the uncertain conditions such as uncertainty in blood demand and facility disruptions, and also, due to the uncertain nature of blood products such as perishable lifetime, distinct blood groups, and ABO-Rh(D)...
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Based on the uncertain conditions such as uncertainty in blood demand and facility disruptions, and also, due to the uncertain nature of blood products such as perishable lifetime, distinct blood groups, and ABO-Rh(D) compatibility and priority rules among these groups, this paper aims to contribute blood supply chains under uncertainty. In this respect, this paper develops a bi-objective two-stage stochastic programming model for managing a red blood cells supply chain that observes above-mentioned issues. This model determines the optimum location-allocation and inventory management decisions and aims to minimize the total cost of the supply chain includes fixed costs, operating costs, inventory holding costs, wastage costs, and transportation costs along with minimizing the substitution levels to provide safer blood transfusion services. To handle the uncertainty of the blood supply chain environment, a robust optimization approach is devised to tackle the uncertainty of parameters, and the TH method is utilized to make the bi-objective model solvable. Then, a real case study of Mashhad city, in Iran, is implemented to demonstrate the model practicality as well as its solution approaches, and finally, the computational results are presented and discussed. Further, the impacts of the different parameters on the results are analyzed which help the decision makers to select the value of the parameters more accurately.
Managing platelets supply chain network has proved challenging. Besides stochastic demand, the high perishability of platelets and the diversity of their demands make the management more intricate. As a motivation to ...
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Managing platelets supply chain network has proved challenging. Besides stochastic demand, the high perishability of platelets and the diversity of their demands make the management more intricate. As a motivation to conduct this paper, we investigate a real-world case study facing a variety of platelet demands to satisfy. Being the first-ever study, we contribute a practical method for the efficient design and planning of a multiple platelet-derived products supply chain network. As the most perishable blood product, platelets need to be maintained fresh. Thus, we suggest a bi-objective model make a tradeoff relationship between the network costs and platelets' freshness. Further, we account for two realistic features that the products are categorized into three main types with respect to their application and lifetime, and hospitals are prioritized based on their specialty and the population of patients they cover. To cope with the uncertainty and objectivemultiplicity, we develop a mixed approach. The network robustness under uncertainty is controlled by a robust method and the Pareto solutions of the conflicting objectives are obtained via an interactive approach. Further, we take into account real-world scenarios that the network facilities may face disruptions and utilize a robust scenario-based approach to deal with the disruption scenarios. The results demonstrate that although simultaneous demand fluctuation and disruption increase both logistics costs and delivery time, the proposed model is capable of achieving robust solutions that a little increase in the logistic costs obtains a considerable reduction in the level of relative regret. Further, the network will benefit from a favorable saving in the logistic costs only by a little increase in the storage time.
In this study, a closed-loop supply chain network is designed. The solution for the applied model is determined in two phases. First, third-party logistics (3PLs) and external suppliers are selected by using grey theo...
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In this study, a closed-loop supply chain network is designed. The solution for the applied model is determined in two phases. First, third-party logistics (3PLs) and external suppliers are selected by using grey theory. The outputs are the weight of 3PLs and suppliers. Second, a multi-objective mixed-integer linear programming model (MOMILP) is proposed. Through this model, 3PLs and suppliers are selected, and the optimal amount of returns to 3PLs and parts purchased from suppliers are determined. The proposed model considers the simultaneous selection of 3PLs and suppliers. The contribution of this study is a novel configuration of the multi-stage, multi-period, multi-product closed-loop supply chain network and 3PL and supplier selection by using grey theory, wherein a MOMILP is developed to implement the network and partner allocation. The supplier hub is utilized in the proposed network, which has rarely been accomplished in the previous literature. Finally, a numerical example is provided.
Optimizing over the efficient set of a multi-objective optimization problem is among the difficult problems in global optimization because of its nonconvexity, even in the linear case. In this paper, we consider only ...
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Optimizing over the efficient set of a multi-objective optimization problem is among the difficult problems in global optimization because of its nonconvexity, even in the linear case. In this paper, we consider only properly efficient solutions which are characterized through weighted sum scalarization. We propose a numerical method to tackle this problem when the objective functions and the feasible set of the multi-objective optimization problem are convex. This algorithm penalizes progressively iterates that are not properly efficient and uses a sequence of convex nonlinear subproblems that can be solved efficiently. The proposed algorithm is shown to perform well on a set of standard problems from the literature, as it allows to obtain optimal solutions in all cases.
Since security return cannot be accurately estimated using past data, in this paper it is assumed to take values in a given ellipsoidal uncertainty set. This paper aims to discuss a robust multi-objective portfolio se...
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Since security return cannot be accurately estimated using past data, in this paper it is assumed to take values in a given ellipsoidal uncertainty set. This paper aims to discuss a robust multi-objective portfolio selection problem based on the minimax regret criterion under an ellipsoidal uncertainty sets, in which the two objective functions are the portfolio return to be maximized and the mean absolute deviation as a risk measure to be minimized. The robust counterpart formulation for the proposed model is firstly presented, then an algorithm based on the relaxation procedure is designed to solve the robust counterpart formulation with second-order cone constraints and infinite constraints. Finally, a practical example based on real market data is presented to illustrate the effectiveness of the proposed model and the algorithm. Compared with the traditional robust portfolio model based on minimax robustness, the robust minimax regret optimal solutions proposed in this paper have better performance on several evaluation criteria.
We present approximate solutions for the robust semi-infinite multi-objective convex symmetric cone programming problem. By using the robust optimization approach, we establish an approximate optimality theorem and ap...
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We present approximate solutions for the robust semi-infinite multi-objective convex symmetric cone programming problem. By using the robust optimization approach, we establish an approximate optimality theorem and approximate duality theorems for approximate solutions in convex symmetric cone optimization problem involving infinitely many constraints to be satisfied and multiple objectives to be optimized simultaneously under the robust characteristic cone constraint qualification. We also give an example to illustrate the obtained results in an important special case, namely the robust semi-infinite multi-objective convex second-order cone program.
This paper comprises of modelling and optimization of a production–distribution problem with the multi-product. The proposed model combined three well-known approaches, fuzzy programming, goal programming and interac...
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On-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, produc...
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On-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, production schedules and energy supply decisions need to be well investigated. In this paper, we present a two-stage, multi-objective stochastic program for flow shops with sequence-dependent setup. The first stage provides optimal schedules to minimize the total completion time. The second stage determines the energy supply decisions to minimize energy costs under a time-of-use electricity pricing scheme. The power demand of the production is met by on-site renewable generation, supply from the main grid, and energy storage system. An epsilon-constraint algorithm integrated with L-shaped method is proposed to analyze the problem. Sets of Pareto optimal solutions are provided for decision-makers. Our results show that the energy cost of setup operations is relatively high such that it cannot be ignored. Further, using solar or wind energy saves energy costs significantly. While, utilizing solar energy can reduce more.
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