A new multi-objective bi-level programming model for energy and locality aware multi-job scheduling in cloud computing

Recycling-equilibrium strategy for phosphogypsum pollution control in phosphate fertilizer plants

As the poor applicability of rail transit and elevated road limited by economy, population and many other factors, the waste of road resources caused by bus lane, maximizing application of available road resources and bus facilities, reducing the environmental pollution is the critical issue faced by current situation of urban transportation development in China. Guided by Bus Priority Oriented, utilized comprehensive techniques, such as ABC classification, bi-level programming model, sensitivity analysis, this paper constructed a theoretical frame of elastic equilibrium based on system optimization. Taking time and environment as optimized objectives, This paper established the Bi-Level multiobjective programming model based on travel time, and programmed with MATLAB for the calculation. Finally, the case study is illustrated. the software VISSIM was adopted to realize the simulation of the volume before and after the implementation of elastic plan in urban areas of PingDingshan. The simulation results showed the validity and feasibility of the modeling and simulation of Bus Priority Oriented urban road network elastic equilibrium.

In this paper, computational models of environmental pollution and energy consumption of urban multimodal traffic network are proposed according to pertinent research and a multi-objective programming model is then developed to formulate optimization problem for such a system. Simultaneously, the main factors, such as travel time, pricing and convenience which influence travelers’ choice behaviors are all considered and a combined assignment model is proposed to simulate travelers’ mode and route choices. A bi-level programming model, in which the multi-objective optimization model is treated as the upper-level problem and the combined assignment model is processed as the lower-level problem, is then presented to solve multi-criterion system optimization problem for urban multimodal traffic network. The solution algorithms of the proposed models are also presented. Finally, the model and its algorithms are illustrated through a simple numerical example.

With rapid economic and social development, increasing water demands have made water scarcity a worldwide issue. Equitable, stable, and efficient water allocation, which includes water resources management and conflicting water demand management, needs a valid resolution. This paper develops a multiobjective bilevel model with the upper level reflecting the river basin authority’s allocation principle of equity and stability and the lower level aimed at ensuring maximal economic benefit effiency for each subarea. The Gini coefficient is introduced to seek equity in the water allocation strategies, a power index is applied to ensure stability in the water allocation solutions, and an economic efficiency function is designed to maximize the economic benefit efficiency in each subarea. The proposed multiobjective bilevel water allocation model is then applied to a case study in Qujiang River Basin in China, and demonstrates that the proposed model provides reasonable and practical water allocation st...

Optimizing train frequencies and train routing with simultaneous passenger assignment in high-speed railway network

Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that simultaneously minimizes transportation cost, carbon trading cost, and transportation time. The model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a Pareto-optimal solution set, from which the optimal solution is selected using a normalized ideal point method. Simulation-based case studies validate the feasibility and practical applicability of the proposed model. The results show that the optimized network significantly outperforms the traditional road-dominant mode. Under the baseline carbon price of 70 CNY/ton, the optimal deployment rate of EACTs reaches 25.03% and 33.87%. Sensitivity analysis reveals a distinct non-linear threshold effect: increasing the carbon price to 90 CNY/ton drives the EACT adoption rate to 32.76% and 45.38%, resulting in a 6.98% reduction in carbon emissions and a 12.75% decrease in total operational costs compared to the baseline scenario. Additionally, strict carbon quotas (e.g., 3000 tons) are found to further compel a modal shift, peaking EACT usage at 35.08% and 46.71%. These quantitative findings offer actionable insights for optimizing multimodal transport structures and refining carbon trading policies.

Stochastic multi-objective models for network design problem

The COVID-19 outbreak has forced people to stay at home to prevent the spread of the virus. In this case, social media platforms have become the main communication venue for people. Online sales platforms have also become the main field for people’s daily consumption. So, how to make full use of social media to carry out online advertising promotion, and then achieve better marketing, is one of the core issues that the marketing industry must pay attention to and solve. Therefore, this study takes the advertiser as the decision-maker, maximizes the number of full playing, likes, comments and forwarding, and minimizes the cost of advertising promotion as the decision-making goals, and Key Opinion Leader (KOL) selection as the decision vector. Based on this, a multi-objective uncertain programming model of advertising promotion is constructed. Among them, the chance-entropy constraint is proposed by combining the entropy constraint and the chance constraint. In addition, the multi-objective uncertain programming model is transformed into a clear single-objective model through mathematical derivation and linear weighting of the model. Finally, the practicability and effectiveness of the model are verified by numerical simulation, and decision-making suggestions for advertising promotion are put forward.

Optimization of Two-Stage Port Logistics Network of Dynamic Hinterland Based on Bi-level Programming Model

A multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach

Purpose – Commonly shared logistics services help manufacturing companies to cut down redundant logistics investments while enhance the overall service quality. Such service-sharing mode has been naturally adopted by group companies to form the so-called headquarter-managed centralized distribution center (HQ-CDC). The HQ-CDC manages the common inventories for the group’s subsidiaries and provides shared storage services to the subsidiaries through appropriate sizing, pricing and common replenishment. Apart from seeking a global optimal solution for the whole group, the purpose of this paper is to investigate balanced solutions between the HQ-CDC and the subsidiaries. Design/methodology/approach – Two decision models are formulated. Integrated model where the group company makes all-in-one decision to determine the space allocation, price setting and the material replenishment on behalf of HQ-CDC and subsidiaries. Bilevel programming model where HQ-CDC and subsidiaries make decisions sequentially to draw a balance between their local objectives. From the perspective of result analysis, the integrated model will develop a managerial benchmark which minimizes the group company’s total cost, while the bilevel programming model could be used to measure the interactive effects between local objectives as well as their final effect on the total objective. Findings – Through comparing the numerical results of the two models, two major findings are obtained. First, the HQ-CDC’s profit is noticeably improved in the bilevel programming model as compared to the integrated model. However, the improvement of HQ-CDC’s profit triggers the cost increasing of subsidiaries. Second, the analyses of different sizing and pricing policies reveal that the implementation of the leased space leads to a more flexible space utilization in the HQ-CDC and the reduced group company’s total cost especially in face of large demand and high demand fluctuation. Research limitations/implications – Several classical game-based decision models are to be introduced to examine the more complex relationships between the HQ-CDC and the subsidiaries, such as Nash Game model or Stackelberg Game model, and more complete and meaningful managerial implications may be found through result comparison with the integrated model. The analytical solutions may be developed to achieve more accurate results, but the mathematical models may have to be with easier structure or tighter assumptions. Practical implications – The group company should take a comprehensive consideration on both cost and profit before choosing the decision framework and the coordination strategy. HQ-CDC prefers a more flexible space usage strategy to avoid idle space and to increase the space utilization. The subsidiaries with high demand uncertainties should burden a part of cost to induce the subsidiaries with steady demands to coordinate. Tanshipments should be encouraged in HQ-CDC to reduce the aggregate inventory level as well as to maintain the customer service level. Social implications – The proposed decision frameworks and warehousing policies provide guidance for the managers in group companies to choose the proper policy and for the subsidiaries to better coordinate. Originality/value – This research studies the services sharing on the warehouse sizing, pricing and common replenishment in a HQ-CDC. The interactive decisions between the HQ-CDC and the subsidiaries are formulated in a bilevel programming model and then analyzed under various practical scenarios.

Fuzzy multi-objective decision model for calibration supplier selection problem

Planning a layout of construction temporary facilities (CTFs), a specific construction site layout problem, is crucial to the successful completion of large-scale construction projects. Despite considerable research undertaken, mutual relationships and conflicts between the layout planner and other pre-planning task stakeholders (i.e., for the scheduling of construction activities or the material purchase) are not yet addressed in the layout process. Resolving conflicts is crucial to the layout optimization and this study is among the first to plan appropriate CTF layouts by taking bi-stakeholder conflict resolution into account. A new bi-level mathematical method is developed based on the resolution of conflicts between the CTF layout planner and the material purchase stakeholder, and thus providing an example for future layouts with bi-stakeholder conflicts. In this method, a bi-level programming model is first established to model layout-related goals, constraints, and conflicts. Second, a bi-level genetic algorithm is proposed to solve the model for appropriate layouts. To demonstrate the applicability of the proposed method, the method is applied to design a CTF layout for the construction of a practical dam project. The results demonstrated that an appropriate layout and a satisfactory purchase plan were determined, and conflicts between the CTF layout and the material purchase were prevented. The contributions of the method are the ability to capture the mutual conflicts of the CTF layout and the material purchase in the bi-level model, the capacity to develop a more efficient and realistic layout, and the practicality to guide planners in other situations.

Strategic planning of power to gas energy storage facilities in electricity market

Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bi-level mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.