Joint Optimization of a Bilevel Logistics Network and an Arc Interdiction Location-routing Problem

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

An investment evaluation and incentive allocation model for public-private partnerships in renewable energy development projects

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

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.

With the growing presence of hazardous materials in daily life, a large number of institutions and scholars have been paying close attention to this field, providing new directions for exploring hazardous materials distribution patterns. This paper employs two fuzzy random variables, transportation cost and risk, to put forward a bi-level minimum objective programming model with a chance measure constraint within a specified chance level. The lower level is to seek minimum transportation costs and the upper level is for minimum risk. The model presented in this article simultaneously designs the hybrid algorithm, which is the combination of the fuzzy random simulation with the genetic algorithm. In the end, a smallscale instance is given to account for the efficiency of the presented model and algorithm, and the best distribution solution is presented.

With accelerating urbanization, subterranean logistics systems play a critical role in reducing traffic congestion and enhancing logistics efficiency, emphasizing the need to optimize urban underground logistics networks. This study proposes a two-stage nodal location model for metro-based logistics networks to address the optimization of urban underground logistics networks. Specifically, the model initially evaluates the importance of metro stations as candidate logistics centers using complex network theory, AHP, and TOPSIS. Subsequently, a bi-level programming model from governmental and corporate perspectives is employed to minimize total distribution costs and maximize demand coverage. Additionally, a genetic particle swarm optimization algorithm is used to solve this complex location problem for the model. To validate the model’s robustness and adaptability, a case study was conducted to identify optimal logistics nodes in Xi’an’s metro network. The results demonstrate that the network optimization methods can effectively select nodes and adjust the number of nodes based on demand.

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.

As the main body of power grid operation, county-level power supply enterprises undertake an important emission to guarantee the security of power grid operation and safeguard social power using order. The optimization of grid construction projects has been a key issue of power supply capacity and service level of grid enterprises. According to the actual situation of power grid construction project optimization of county-level power enterprises, on the basis of qualitative analysis of the projects, this paper builds a Bi-level programming model based on quantitative analysis. The upper layer of the model is the target restriction of the optimal portfolio; the lower layer of the model is enterprises’ financial restrictions on the size of the enterprise project portfolio. Finally, using a real example to illustrate operation proceeding and the optimization result of the model. Through qualitative analysis and quantitative analysis, the bi-level programming model improves the accuracy and normative standardization of power grid enterprises projects.

With the accelerating economic development and increasing urban motorization, traffic congestion is seen everywhere. The issue of vehicular exhaust emissions which leads the vehicles to accelerate and decelerate frequently as well as stay in idle state for a long time, is particularly serious. In order to reduce carbon emissions and improve road network efficiency, a bi-level programming model considering carbon emissions under the action of route guidance information is set up. The decision maker aims at reducing carbon emissions and optimizing road network efficiency. It makes road traffic capacity as a constraint condition by means of providing different types of information in its upper model. The route choice behavior of a driver is described by the user equilibrium in its lower model. The bi-level programming model of the network was analyzed and solved by a heuristic algorithm. In the end, a simple numerical example is presented in order to validate the feasibility of the model and the algorithm. On the basis of analyzing the influence of vehicle carbon emissions and road network efficiency under the route guidance information, vehicular exhaust emissions control strategies are presented.

A cooperative Stackelberg game based energy management considering price discrimination and risk assessment

Operational scheduling of intercity passenger transportation company participating in energy and reserve markets

Strategic interactions between liquefied natural gas and domestic gas markets: A bilevel model

This article investigates how to simultaneously optimize both strategic and tactical decisions in the supply chain network design. For this purpose, a bi-level programming model is developed in which supply chain network design problem is considered as a strategic decision in the upper-level model, while the lower-level model contains the assembly line balancing as a tactical decision. In addition, the problem is extended to include push–pull strategy where decisions such as production amount and inventory level of each component in manufacturers are made. Based on the special structure of the model, a heuristic method is proposed to solve the developed bi-level model. A numerical example is employed to show the performance of proposed method in terms of feasibility and convergence. Finally, computational experiments on several problem instances are presented to demonstrate the applications of developed model and the solution method.

In the current work, we considered the problem of hazardous material distribution where the distributer chooses the routes on the network, and a regulatory agency controls the behaviour of the distributer to traverse the specified routes. In these circumstances, the distributer sets to select some routes to minimise the total distributing costs. Mostly, this occurs due to selecting risky arcs in which more individuals are exposed to risk. To prevent this and increase the capability to deal with the risk of hazardous material transportation through roads, the regulatory agency obliges carriers to traverse through the most secure arcs, though imposing more distribution costs. The problem is modelled as a bi-level routing problem. The bi-level model is difficult to solve and may be ill-posed. Two meta-heuristic algorithms are proposed to solve the bi-level model, and some randomly generated problems are applied to show the applicability and efficiency of the proposed algorithms.