Topology Optimization Design of Structures Based on Eigenfrequency Matching

We demonstrate the design of resonating structures using a density-based topology optimization approach, which requires the eigenfrequencies to match a set of target values. To develop a solution, several optimization modules are implemented, including material interpolation models, penalization schemes, filters, analytical sensitivities, and a solver. Moreover, common challenges in topology optimization for dynamic systems and their solutions are discussed. In this study, the objective function is to minimize the error between the target and actual eigenfrequency values. The finite element method is used to compute the eigenfrequencies at each iteration. To solve the optimization problem, we use the sequential linear programming algorithm with move limits, enhanced by a filtering technique. Finally, we present a resonator design as a case study and analyze the design process with different optimization parameters.

Research on Linear Programming Algorithm for Mathematical Model of Agricultural Machinery Allocation

The objective of this paper is to study the linear programming algorithm of the mathematical model of agricultural machinery allocation when there are many farmland projects and cross operations. In this paper, combined with the mechanization process of crops in XPCC, the linear programming algorithm of mathematical model was used to establish the allocation scheme of different scales. All equations were solved and analyzed, and the allocation schemes of different planting scales were compared. It is also observed that through the interactive conflicts in between multiple objectives a solution vector can be analyzed. The results show that the activity cost of Scheme 5 was the lowest, only 1,260 yuan per mu, which was the best way to equip agricultural machinery. The results present that it is of great significance to optimize the configuration of agricultural machinery. The experimental results present that the portion of water which is reused in comparison with the total water is gradually increasing which leads to the overall reduction in water consumption.

Collaborative E-Learning Process Discovery in Multi-tenant Cloud

With the appearance of the COVID-19 pandemic, the practice of e-learning in the cloud makes it possible to:avoid the problem of overloading the institutions infrastructure resources, manage a large number of learners and improve collaboration and synchronous learning. In this paper, we propose a new e-leaning process management approach in cloud named CLP-in-Cloud (for Collaborative Learning Process in Cloud). CLP-in-Cloud is composed of two steps: i) design general, configurable and multi-tenant e-Learning Process as a Service (LPaaS) that meets different needs of institutions. ii) to fulfill the user needs, developpe a functional and non-functional awareness LPaaS discovery module. For functional needs, we adopt the algorithm A* and for non-functional needs we adopt a linear programming algorithm. Our developed system allows learners to discover and search their preferred configurable learning process in a multi-tenancy Cloud architecture. In order to help to discover interesting process, we come up with a recommendation module. Experimentations proved that our system is effective in reducing the execution time and in finding appropriate results for the user request.

Order Fulfillment Decision under Multiwarehouse Collaborative Delivery

Aiming at the high cost of multicategory orders fulfillment under multiwarehouse collaborative distribution, comprehensively considering the fulfillment costs of different orders fulfillment strategies, an order fulfillment strategy selection model is proposed. The first step of the model uses the linear programming algorithm to solve the cost of suborder merge transportation after the order is split. The second step calculates the cost of the current “greedy algorithm” of the e-commerce platform for order split fulfillment. Then, the cost of each strategy is compared and the lowest cost one is chosen. The calculation example analysis shows that the order fulfillment strategy is closely related to the delivery location of the order and the SKU category. When the delivery location is far away and SKU categories are many in the order, the merged transportation strategy of suborders after the order is split will be significantly better than the cost of separate transportation. The multiwarehouse collaborative distribution fulfillment strategy proposed in this paper can provide a decision basis for the e-commerce platform to choose which fulfillment method.

Adaptive Linear Programming Algorithm With Parameter Learning for Managing Engineering-Design Problems

The adaptive linear programming (ALP) algorithm is an extension of the sequential linear programming algorithm where nonlinear formulations are iteratively approximated as linear formulations linearized about an iteration starting point. In the ALP algorithm, heuristics are used to determine the value of a critical parameter, namely, the reduced move coefficient (RMC). The RMC defines how far to move towards the solution of iteration “n” from the starting point for iteration “n” in specifying the starting point for iteration “n+1”. The RMC choice affects the efficacy of the approximation; however, there is no mechanism of evaluating the algorithm performance with respect to the RMC value so as to improve the design efficiency and effectiveness. This limitation is addressed in this paper. In this paper, we enhance the ALP algorithm with parameter learning (ALPPL) and generalize to make the knowledge gained reusable. We propose a three-step procedure of rule-based parameter learning leading to robust solutions. An industry-inspired problem, the integrated design of a hot rolling process chain for the production of a steel rod, is used to demonstrate the implementation. The three-step procedure could be used to improve other critical parameter determinations, especially when there is a lack of mechanisms for algorithm-performance evaluation, evaluation criteria vary with problems, or heuristics are over-used.

Approximate Pareto Set for Fair and Efficient Allocation: Few Agent Types or Few Resource Types

In fair division of indivisible goods, finding an allocation that satisfies fairness and efficiency simultaneously is highly desired but computationally hard. We solve this problem approximately in polynomial time by modeling it as a bi-criteria optimization problem that can be solved efficiently by determining an approximate Pareto set of bounded size. We focus on two criteria: max-min fairness and utilitarian efficiency, and study this problem for the setting when there are only a few item types or a few agent types. We show in both cases that one can construct an approximate Pareto set in time polynomial in the input size, either by designing a dynamic programming scheme, or a linear-programming algorithm. Our techniques strengthen known methods and can be potentially applied to other notions of fairness and efficiency as well.

Research on SKINNY Optimal Differential Trail Search

SKINNY is a tweakable lightweight block cipher algorithm. In order to test its security, this paper performs optimal differential trail search analysis on all SKINNY-64 versions under single-key setting based on the MILP (Mixed Integer Linear Programming) algorithm. Firstly, SKINNY round function is abstracted equivalently by precise constraints, and the objective function is set as the minimum number of active S-box number to optimize SKINNY-64 MILP model. Experiments show the differential trail searched by this method is not necessarily optimal. In order to directly search for the optimal differential trail, the S-box differential probability coding information is added to the optimized SKINNY-64 MILP model, the S-box differential characteristic is reconstructed, and the objective function is set to the minimum value of the probability coding information, which improves the SKINNY-64 MILP model. The results of experimental show that the improved MILP model can directly search for the optimal differential trail, and the complexity is slightly increased, but the search efficiency is significantly improved. Under single-key setting, this method has obvious advantage in searching the optimal differential trails of SKINNY-64 with low round number.