Modeling and Simulation of Bus Priority Oriented Urban Road Network Elastic Equilibrium

In this study, we consider solution approaches used to solve the proposed bilevel linear programming model for an Industrial Symbiosis network. We first solve this model with the well-known Karush-Kuch-Tucker (KKT) approach. However, transforming a bilevel programming model with the KKT approach increases the number of variables and constraints. For this reason, we use the fuzzy programming approach and the fuzzy goal programming approaches as alternatives to the KKT approach. Next, we compare the results of the KKT approach with these methods and examine the suitability of these approaches to solve our bilevel problem. Unlike previous studies, which claimed that fuzzy approaches mostly outperform the KKT approach, in our case, the best solution is obtained with the KKT approach. This is most probably because these approaches ignore the hierarchical nature of the problem. We believe that more research on fuzzy approaches is needed to evaluate the suitability of these approaches for solving bilevel programming problems.KeywordsIndustrial symbiosisBilevel programmingFuzzy programming approachFuzzy goal programming

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.

Starts from the analysis of the different levels of service constraints impact to the network design, this paper combines the indicators in the levels of network service and road service, and then builds the bi-level programming model of road network design based on the level of service. And the paper solves the bi-level programming model based on the simulated annealing to verify that the saturation constraint values of the level of link service can be relaxed under the premise of ensuring the overall level of network service, by which road resources are be used fully with little impact of the road network.

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.

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

By handling the travel cost function artfully, the authors formulate the transportation mixed network design problem (MNDP) as a mixed-integer, nonlinear bilevel programming problem, in which the lower-level problem, comparing with that of conventional bilevel DNDP models, is not a side constrained user equilibrium assignment problem, but a standard user equilibrium assignment problem. Then, the bilevel programming model for MNDP is reformulated as a continuous version of bilevel programming problem by the continuation method. By virtue of the optimal-value function, the lower-level assignment problem can be expressed as a nonlinear equality constraint. Therefore, the bilevel programming model for MNDP can be transformed into an equivalent single-level optimization problem. By exploring the inherent nature of the MNDP, the optimal-value function for the lower-level equilibrium assignment problem is proved to be continuously differentiable and its functional value and gradient can be obtained efficiently. Thus, a continuously differentiable but still nonconvex optimization formulation of the MNDP is created, and then a locally convergent algorithm is proposed by applying penalty function method. The inner loop of solving the subproblem is mainly to implement an all-or-nothing assignment. Finally, a small-scale transportation network and a large-scale network are presented to verify the proposed model and algorithm.

Big city external passenger terminals layout is one of the important contents of the urban master planning and the regional comprehensive transportation planning. Based on the analysis of the traits of external passenger terminals, this paper optimizes the urban external passenger terminals layout by the bi-level programming model from the viewpoints of the decision-makers and the user in the external passenger terminals programming. The upper level programming model optimizes the external passenger terminals layout from the traffic planners’ point of view to achieve shorted overall travel time and the lower level programming model describes the user's travel rules and arranges the travel lines in reason by the stochastic user equilibrium model from the user’s point of view. The upper level model adopts genetic algorithm and the lower level model adopts method of successive averages (MSA). Example results show that the model can effectively solve the external passenger terminals layout optimization problems so as to provide scientific basis for the programming decision-makers.

In this paper, a bi-level programming model based on OD pair travel time reliability is developed to study road network capacity reliability. A user equilibrium traffic assignment model is proposed with a lower problem describing travelers' route choice behavior and an upper problem that maximizes the basic reference OD demand multiplier subject to the corresponding prescribed travel time threshold. An approximation algorithm is introduced to estimate the road network capacity reliability. The algorithm treats the capacity of each link as a discrete random variable and uses the road network most probable states generation method in conjunction with the sensitivity analysis technique to solve the bi-level programming model. Finally, a numerical example of a small road network is used to demonstrate the application of the proposed model and algorithm.

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.

Optimization for operation scheme of express and local trains in suburban rail transit lines based on station classification and bi-level programming

Although a wide range of literature has investigated the network-level highway maintenance plans and policies, few of them focused on the maintenance scheduling problem. This study proposes a methodology framework to model and compare two different maintenance scheduling strategies for highway networks, i.e., minimal makespan strategy (MMS) and minimal increased travel delay strategy (MITDS). We formulate MMS as a mixed integer linear programming model subject to the constraints of the quantity of manpower and the worst-first maintenance sequence. A bi-level programming model is proposed to quantify and optimize MITDS. The upper level model determines the optimal scheduling to minimize the increased traffic delays during the maintenance makespan. In the lower level, a modified day-to-day traffic assignment model is put forward to reflect the traffic evolution dynamics by simulating travelers’ route choice behaviors. A simulated annealing algorithm and augmented Lagrange algorithm are employed to solve the two proposed models, respectively. Finally, a numerical example using a highway network is developed. The two proposed strategies are tested considering different traffic demands, numbers of engineering teams, and travelers’ sensitivities to traffic congestion. The experiment results reveal that compared with MMS, MITDS extends makespan by 2 days though, it reduces the total increased travel delays by 4% and both MMS and MITDS can obtain the minimum total increased travel delays when the number of engineering teams is 6. The sensitivity analysis indicates that both the two strategies have the maximum and minimum total increased travel delays when the weight of prediction in travelers’ perception is 0.3 and 0.7, respectively. The proposed framework has the potential to provide reference in implementing highway maintenance activities reasonably.

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

To ensure the equity of exclusive bus lane (EBL) allocation under multimodal traffic conditions, a bi-level programming model is first constructed. The upper-level model is the minimum total system cost considering the Gini coefficient and the lower-level model constructed a stochastic user equilibrium (SUE) model based on logit loading. Secondly, a heuristic algorithm combining an improved genetic algorithm (GA) and a method of the successive average method (MSA) is designed. Finally, the Nguyen and Dupuis networks are used as examples to verify and analyze the effectiveness, superiority and sensitivity of the model and algorithm. The results show that the method can effectively obtain the optimal solution of the upper-level model as 15,004 RMB, the Gini coefficient is 0.31, and the equity is at a relatively reasonable level. Compared with the different allocation schemes, the proposed scheme has a higher bus sharing rate and lower Gini coefficient. At the same time, when the actual demand is twice the basic demand, the bus share rate is the largest, 65%, and the Gini coefficient is the smallest at 0.3. The bus share rate decreases with the increase in the proportion of high time value travelers, which fully verifies the sensitivity of the model to the type of traveler.

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