Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.     

A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach

In this paper, sustainability of airports is considered through a multi-perspective, multi-system, and multi-process operation. It is explored how an extension of fuzzy dynamic network performance measurement approach helps to determine the efficiency performance of an airport system. In this way, a three-pronged approach is intended which considers the internal functions of an airport, functions affecting the community and functions related to the passengers simultaneously. This novel combination makes it possible to have a comprehensive evaluation of airports' performance. Besides, fuzzy extension of SBM dynamic network approach makes it possible to deal better with the vagueness of variables during analysis. So, this extension is valuable from both technical and conceptual aspects which in turn provide useful information and insights for the future design of holistic strategies connected with sustainable development of airports where ever they are.     

Measuring the perceived container leasing prices in liner shipping network design with empty container repositioning

This paper aims to measure the perceived container leasing prices at different ports by presenting a two-stage optimization method. In stage I, we propose a practical liner shipping network design problem with empty container repositioning. The proposed problem further considers the use of foldable containers and allows the mutual substitution between empty containers to decrease the number of empty containers to be repositioned. In stage II, the inverse optimization technique is used to determine the perceived container leasing prices at different ports, based on the solution obtained in stage I. Based on a set of candidate liner shipping service routes, a mixed-integer nonlinear programming model is built for the proposed problem in stage I. The nonlinear terms are linearized by introducing the auxiliary variables. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of our two-stage optimization method.     

Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application

In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system.     

Exact and heuristic methods to solve the berth allocation problem in bulk ports

In this paper, we study the dynamic hybrid berth allocation problem in bulk ports with the objective to minimize the total service times of the vessels. We propose two exact methods based on mixed integer programming and generalized set partitioning, and a heuristic method based on squeaky wheel optimization, explicitly considering the cargo type on the vessel. The formulations are compared through extensive numerical experiments based on instances inspired from real bulk port data. The results indicate that the set partitioning method and the heuristic method can be used to obtain near-optimal solutions for even larger problem size.     

铁路客运站客流监测及疏散辅助决策系统的相关研究

铁路运输在综合运输系统中扮演着越来越重要的角色，而铁路客运站客流的监测和疏散一直是困扰中国这个人口大国的难题。根据现状以及客流特点．尝试设计一套铁路客运站客流监测及疏散辅助决策系统，这套软件集实时旅客监测系统、及时应急预警系统以及智能化旅客疏散系统于一体，旨在加快旅客运转，提高铁路运输效率，加强旅客运输安全性。     

Real-time mobilization decisions for multi-priority emergency response resources and evacuation groups: Model formulation and solution

This paper presents a model formulation and solution for simultaneous mobilization destination, traffic assignment, and departure schedule for multi-priority groups (SMDTS-MPG) for real-time emergency response in no-notice disasters. The presented approach addresses the decision context in which multiple emergency responses and evacuation flow groups with different destinations and varying priorities coexist in the same traffic network, within which simultaneous mobilization strategies must consider this requirement. The proposed modeling technique and the cell transmission model (CTM)-based linear-programming model provides a mechanism to accomplish this goal in an intuitive and coherent manner. The proposed matrix formulation of the SMDTS-MPG model permits the rapid deployment of the model to large networks. Model formulation and numerical examples are presented in detail in this paper.     

End-of-life aircraft treatment in the context of sustainable development,lean management,and global business

Dealing with retired aircraft taking into account the environmental, social, and economic impacts is emerging as an air transportation problem for the near future. The players involved in the problem wish to solve this challenge in a systematic way to benefit from the value extracted from the core activities of the end-of-life (EoL) aircraft treatment, decrease the environmental impacts, and at the same time maximize the social value. This paper proposes a holistic approach to EoL aircraft treatment considering lean management, sustainable development, and the global business environment. An integrated optimization framework is proposed to support decision-making at both the strategic and managerial levels. The flexibility of the framework makes it possible to compare different business models, dismantling strategies, and network structures that impact the efficiency, stability, and leanness of the recovery network. A solution methodology based on the joint application of fuzzy interactive approach and genetic algorithm is introduced. The application perspective and guidelines for pilot study are also provided. To the best of our knowledge, this is the first time that recovery of EoL aircraft is considered based on three aspects of sustainability, lean, and global business. Therefore, this study provides many promising perspectives in this context to enrich the literature of EoL aircraft as a new aviation industry challenge and opportunity.     

A hierarchical clustering and routing procedure for large scale disaster relief logistics planning

We describe a hierarchical cluster and route procedure (HOGCR) for coordinating vehicle routing in large-scale post-disaster distribution and evacuation activities. The HOGCR is a multi-level clustering algorithm that groups demand nodes into smaller clusters at each planning level, enabling the optimal solution of cluster routing problems. The routing problems are represented as capacitated network flow models that are solved optimally and independently by CPLEX on a parallel computing platform. The HOGCR preserves the consistency among parent and child cluster solutions obtained at consecutive levels. We assess the performance of the algorithm by using large scale scenarios and find satisfactory results.     

The application of inventory transshipment modeling to air cargo revenue management

This paper applies an inventory transshipment modeling approach to investigate the air cargo revenue management problem for an airline operating in a two-segment network. Building upon an extension of the classic two-location inventory transshipment model, we develop a framework to optimize an airline’s cargo overbooking decisions in a two-segment network setting. We find consistent evidence indicating that network-based global optimization always leads to greater expected profits than does local (i.e., market by market) optimization. Further, the magnitude of profit improvement is found to be most significant when local shipments have a relatively higher freight yield compared to flow-through shipments. Finally, our results indicate that global optimization contributes to greater profit improvement as offloading penalty costs become higher.     

A robust optimization approach for the road network daily maintenance routing problem with uncertain service time

This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.     

高速铁路动车组检修资源布局优化研究

高速铁路网的日趋完善,高速列车开行数量的进一步增加,对高速铁路动车组检修资源布局提出新的要求。为合理优化动车组检修资源布局,提高动车组检修效率,在分析我国动车段和动车运用所管理模式及布局方案的基础上,将动车组检修资源布局优化问题转化为基于多基地车辆路径运用的基地选址问题,基于"备选运用所-运输需求-动车组运用"间的关联关系构建动车组运用检修接续网络,建立动车组检修资源布局优化模型。以某区域的铁路网为例,利用近远期规划优化动车组检修资源布局,验证模型有效性,为今后动车组检修资源布局优化提供参考。     

Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

A novel discrete network design problem formulation and its global optimization solution algorithm

Conventional discrete transportation network design problem deals with the optimal decision on new link addition, assuming the capacity of each candidate link addition is predetermined and fixed. In this paper, we address a novel yet general discrete network design problem formulation that aims to determine the optimal new link addition and their optimal capacities simultaneously, which answers the questions on whether a new link should be added or not, and if added, what should be the optimal link capacity. A global optimization method employing linearization, outer approximation and range reduction techniques is developed to solve the formulated model.     

突发事件下高速铁路列车运行调整研究

列车运行调整属于大规模的组合优化问题,其约束条件多,求解复杂,而突发事件下运行调整难度进一步加大。基于高速铁路采用的准移动闭塞制式,综合考虑列车区间运行时间、发车时间、停站时间、车站到发线数量以及追踪间隔时间等约束,以所有列车的总延误时间最小为目标,建立突发事件下的列车运行调整优化模型,利用具有动态变化惯性权重的粒子群算法求解模型。分析表明:突发事件下的列车运行调整优化模型及算法可以满足突发事件下高速铁路列车运行调整的时效性要求;动态变化惯性权重的粒子群算法具备较快的收敛速度与较强的局部挖掘能力。     

A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning

This paper contributes to the existing research on freight transportation, spatial and land use planning by investigating an improved spatial aggregation technique to delineate desirable freight traffic analysis zones. Zoning is a process of spatially aggregating several predefined basic spatial units (BSUs) into multiple zones. It plays a vital role in the transportation planning and decision-making process and is well-documented as the modifiable areal unit problem (MAUP). MAUP involves aggregating BSUs to obtain optimal zones satisfying specific criteria and constraints. This paper proposes an improved spatial aggregation methodology to develop a freight traffic analysis zone system by applying the multiobjective optimization concept using a genetic algorithm. The decision variables, namely, (i) Freight trip density; (ii) Number of establishments; (iii) Employment density; and (iv) Compactness, are chosen to represent the elements of freight, passenger traffic, and land use. The problem is formulated as a multiobjective network partitioning problem. The four objectives aim to create zones with better homogeneity and compactness. It is solved using a genetic algorithm with a weighted distance metric approach to prioritize the four objectives. Results show that zones resulting from the improved methodology are superior to the existing zones in terms of homogeneity of decision variables and compactness. The findings are expected to help the decision-making process of urban, transportation, and land-use planners in selecting appropriate freight traffic zone delineation strategies for a given region.     

Berth scheduling by customer service differentiation: A multi-objective approach

In this paper the discrete and dynamic berth allocation problem is formulated as a multi-objective combinatorial optimization problem where vessel service is differentiated upon based on priority agreements. A genetic algorithms based heuristic is developed to solve the resulting problem. A number of numerical experiments showed that the heuristic performed well in solving large, real life instances. The heuristic provided a complete set of solutions that enable terminal operators to evaluate various berth scheduling policies and select the schedule that improves operations and customer satisfaction. The proposed algorithm outperformed a state of the art metaheuristic and provided improved results when compared to the weighted approach.     

The time constrained multi-commodity network flow problem and its application to liner shipping network design

The multi-commodity network flow problem is an important sub-problem in several heuristics and exact methods for designing route networks for container ships. The sub-problem decides how cargoes should be transported through the network provided by shipping routes. This paper studies the multi-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time is essential to offer customers a competitive product.     

An integrated modeling framework for design of logistics networks with expedited shipment services

This paper studied an integrated logistics network problem that determines optimal supplier locations, assignments of these suppliers to terminal facilities, expedited shipment configurations, and inventory management strategies in an uncertain environment. We studied the problem structure and proposed mathematical models to determine the optimal network design that minimizes the expected total system cost. We developed a customized solution approach based on Lagrangian relaxation that can solve these models efficiently and accurately. Numerical examples are conducted to draw managerial insights into how problem settings and key parameter values affect the optimal design results.