Optimization–Simulation Model for Planning Supply Transport to Large Infrastructure Public Works Located in Congested Urban Areas

This article proposes an optimization–simulation model for planning the transport of supplies to large public infrastructure works located in congested urban areas. The purpose is to minimize their impact on the environment and on private transportation users on the local road network. To achieve this goal, the authors propose and solve an optimization problem for minimizing the total system cost made up of operating costs for various alternatives for taking supplies to the worksite and the costs supported by private vehicle users as a result of increased congestion due to the movement of heavy goods vehicles transporting material to the worksite. The proposed optimization problem is a bi-level Math Program model. The upper level defines the total cost of the system, which is minimized taking into account environmental constraints on atmospheric and noise pollution. The lower level defines the optimization problem representing the private transportation user behavior, assuming they choose the route that minimizes their total individual journey costs. Given the special characteristics of the problem, a heuristic algorithm is proposed for finding optimum solutions. Both the model developed and the specific solution algorithm are applied to the real case of building a new port at Laredo (Northern Spain). A series of interesting conclusions are obtained from the corresponding sensitivity analysis.     

Location of infrastructure in urban railway networks

The location of the infrastructure of the Rapid Transit Network considers at upper level a list of potential transit corridors and stations on the basis of its own constraints, so the network design is modelled on a discrete space of alternatives. At lower level the alternatives are evaluated based on the route and mode user decisions. The optimization objectives are to maximize the transit demand and minimize the private travel time, considering the user's behavior, the physical network, budget constraints and the network design constraints. The design criteria are considered as location constraints, they have been traditionally defined by node and link compatibilities defining lines, but we define a new location constraints based in to minimize the number of routing intersections. The demand mode splitting constraints may be defined by all or nothing mode assignment or like it is proposed by us in this paper by Logit distribution. The models defined with the above alternatives are large integer multicommodity flow network design problems with side constraints. These sets of alternatives are computationally studied to obtain conclusions. The experiments are presented using two networks, a small network and a medium size network, simulating the city of Seville.     

弹性需求下公交网络系统票价结构的优化

对具有弹性需求的城市公交网络系统票价的合理设定问题进行了研究分析.考虑到公 交收费结构的变化会影响乘客的出行需求量和乘客对路径选择行为,将这一问题描述为一个两 级数学规划问题.上一级问题是寻求收益最大的优化问题,下一级问题是估计乘客在网络上的 流量分布的具有弹性需求的随机用户平衡分配模型.鉴于两级规划问题的非凸性,提出基于灵 敏度分析的启发式算法.最后,给出一个仿真算例说明提出的模型和算法的合理性.     

Endogenous Effects of Hubbing on Flow Intensities

Location of hub facilities and the allocation decisions in transport networks endogenously affect both the flow intensities and the transportation costs. Since the introduction of the hub location problem to the operations research literature in mid-1980s, many researchers investigated different ways of modelling the effects of hub facilities on the transportation costs. On the other hand, there has been very limited research on their effect on the flow intensities. This study proposes a new approach, inspired by the Bass diffusion model, to forecast the change in the demand patterns generated at different locations as a result of the placement of new hubs. This new model is used in the context of the uncapacitated single allocation p-hub median problem to investigate the effects of endogenous attraction, caused by the spatial interaction of present hubs, on future hub location decisions. Computational results indicate that the location and allocation decisions may be greatly affected when these forecasts are taken into account in the selection of future hub locations.     

Design of Stochastic Distribution Networks Using Lagrangian Relaxation

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.     

Integrating the logistics network design with order quantity determination under uncertain customer demands

This paper aims to design an optimal logistics network including suppliers and retailers by taking into account the order quantity of products under uncertain consumer demand pattern. This research proposes a mixed-integer bi-level programming model and employs the iterative-optimization method. In the bi-level programming, the upper model is the logistics network design (LND) problem, which is designed for suppliers and consists of the hub locations, wholesale price of the products as well as the transportation flow of the commodity. The lower model is the order quantity determination (OQD) problem for retailers. It processes a special case of inventory problem in which the customer demand is stochastic and follows a series of assumed probability distributions. By applying the proposed methodology in a computational experiment, this research shows that if there were a large number of suppliers in the logistics system, retailers could order the product with relatively low price and the largest profit belongs to the retailer who could sell the commodity at the highest price.     

Insight to the express transport network

The express delivery industry is developing rapidly in recent years and has attracted attention in many fields. Express shipment service requires that parcels be delivered in a limited time with a low operation cost, which requests a high level and efficient express transport network (ETN). The ETN is constructed based on the public transport networks, especially the airline network. It is similar to the airline network in some aspects, while it has its own feature. With the complex network theory, the topological properties of the ETN are analyzed deeply. We find that the ETN has the small-world property, with disassortative mixing behavior and rich club phenomenon. It also shows difference from the airline network in some features, such as edge density and average shortest path. Analysis on the corresponding distance-weighted network shows that the distance distribution displays a truncated power-law behavior. At last, an evolving model, which takes both geographical constraint and preference attachment into account, is proposed. The model shows similar properties with the empirical results.     

Hybrid genetic algorithm for multi-time period production/distribution planning

In this paper, we deal with a production/distribution problem to determine an efficient integration of production, distribution and inventory system so that products are produced and distributed at the right quantities, to the right customers, and at the right time, in order to minimize system wide costs while satisfying all demand required. This problem can be viewed as an optimization model that integrates facility location decisions, distribution costs, and inventory management for multi-products and multi-time periods. To solve the problem, we propose a new technique called spanning tree-based genetic algorithm (hst-GA). In order to improve its efficiency, the proposed method is hybridized with the fuzzy logic controller (FLC) concept for auto-tuning the GA parameters. The proposed method is compared with traditional spanning tree-based genetic algorithm approach. This comparison shows that the proposed method gives better results.     

Capacitated Refueling Station Location Problem with Traffic Deviations Over Multiple Time Periods

In this paper, we consider the problem of locating refueling stations in a transportation network via mathematical programming. The proposed model is applicable for several alternative fuel types and is particularly suitable for hydrogen fuel. We assume that a central planner, a hydrogen manufacturer or a government agency, determines the locations of the refueling stations for a given intra-city transportation network while accounting for multi-period travel demand, nonlinear refueling station operational cost, and the deviations of travelers from their shortest routes to refuel. Incorporating demand patterns over multiple periods allows us to account for both short- and long-term variation in hydrogen refueling demand (the former due to time of day, and the latter future hydrogen fuel cell vehicle growth). It also helps us model the changes in user preferences (station and route choices) and traffic conditions over different periods. To account for refueling station operational cost in making investment decisions, we introduce a staircase marginal cost function. In addition, the model explicitly considers station and route choices of travelers as they may deviate from their original paths to refuel, incurring additional costs and affecting the number and locations of refueling stations. We formulate this problem as a multi-period, mixed-integer model with constant link travel time and staircase operational cost at refueling stations. We applied two well-known solution algorithms, branch-and-bound and Lagrangian relaxation, to solve the problem. Our analysis shows that although we are able to solve the refueling station location problem to optimality with branch-and-bound, the Lagrangian relaxation approach provides very good results with less computational time. Additionally, our numerical example of Mashhad, Iran demonstrates that locating refueling stations with considering multi-period traffic patterns (as opposed to single-period) results in minimum network-wide traffic congestion and lower user and agency costs over a planning horizon.     

Efficient model and heuristic for the intermodal terminal location problem

A multimodal transportation system transports freight using at least two transportation modes. Among available transportation modes, intermodal freight transportation transports freight in an intermodal container or conveyance without handling the freight itself when changing modes. The locations of intermodal terminals constitute the foundation of an intermodal transportation network. The intermodal terminal location problem therefore aims to determine terminal locations and routes within a transportation network in order to minimize the total transportation and operation costs through collaborations of unimodal road transport and intermodal transport chains. Relevant research includes that of Arnold et al., who first presented mathematical programming models for the problem. Sörensen et al. recently proposed a standard model for the same problem. However, these models are complex and time consuming. Some decision variables and constraints of Sörensen et al.׳s model are proven to be redundant. A modified mixed integer programming model is then developed to increase computation efficiency. The modified model finds more optimal solutions to the benchmark problems than current approaches do, within a reasonable time. Furthermore, two matheuristics are presented to solve the problem more efficiently while obtaining near optimal solutions.     

新建机场运输通道建设决策的双层规划模型

机场运输通道是旅客进出机场的必要基础设施。为了满足航空运输快速发展的需求,伴随着新机场的建设,机场运输通道的建设规模决策问题显得至关重要。建立了双层规划模型,其中上层模型为上层交通主管部门以社会总成本最小为目标建设机场运输通道,下层模型为旅客以效用最大化为目标选择运输通道,设计了基于敏感性分析的模拟退火算法,最后通过算例仿真了算法的可行性。     

Reliability Evaluation for Stochastic and Time-dependent Networks with Multiple Parking Facilities

This paper presents a fixed-point model for evaluating the reliability of stochastic and time-dependent networks with multiple parking facilities. The proposed model combines a supply model that simulates the time-dependent attributes of road and parking supplies and their variations with a demand model that simultaneously considers heterogeneous travelers’ choices on departure time, route and parking location. In the proposed model, travelers are differentiated by their values of time, and parking locations are characterized by facility type. Schedule reliability and parking reliability are introduced as new performance indices for the evaluation of the level of service of a road network during time of day. A heuristic solution algorithm that uses a combination of the Monte Carlo simulation approach with the method of successive averages is proposed to estimate these two reliability measures. Numerical results show that the reliability performances of road networks are significantly influenced by the network congestion level and the capacities of road and parking facilities. The proposed model provides some new insights for assessing the impacts of various transport policies and infrastructure improvements at a strategic level.     

A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

In this paper, we present a new bilevel model for a biomedical supply chain network with capacity and budget constraint due to the protection and interdiction operations. The components considered in this model are biomedical devices, distribution centers (DCs), medical suppliers (MSs), and hospitals and patients as the demand points. On the other hand, two levels of decisions in the network planning is suggested: (1) the defender’s decision about protection operations of MSs and DCs, the assignment of clients to the DCs, and quantity of products shipped to DCs from MSs to minimize the demand-weighted traveling costs and transport costs and (2) the attacker’s decision about interdiction operations of MSs and DCs to maximize the capacity or service reduction and losses. Because of nondeterministic polynomial time (NP)-hardness of the problem under consideration, an efficient and fast approach based on a genetic algorithm and a fast branch and cut method (GA–FBC) was developed to solve the proposed model. Also, the efficiency via the comparison of results with the genetic algorithm based on CPLEX (GA-CPLEX) and decomposition method (DM) is investigated. In order to assess the performance of the presented GA–FBC, a set of 27 instances of the problem is used. Comprehensive analysis indicates that the proposed approach significantly solves the problem. In addition, the benefits and advantages of preference with running times and its accuracy is shown numerically. Simulation results clearly demonstrate that the defender’s objective effectively reduced and CPU time also within the large-sized instances of the problem in comparison with the GA-CPLEX and DM.     

需求不确定下的低碳物流配送中心选址

针对低碳物流配送中心选址问题,结合需求不确定的现实问题,采用随机规划理论,建立以包含碳排放成本在内的总成本最低为目标、以不确定性需求为随机约束的选址模型,通过实例运用验证模型的有效性,能够为企业在实际市场需求波动下的选址投入以及收益提供一定的参考价值.另外,对不确定性需求的置信水平和标准差进行灵敏度分析,案例分析结果显示:碳排放因素与需求的不确定性对配送中心选址结果存在较为显著的影响;选址总成本与需求的不确定程度密切相关,碳排放成本也会随着需求的变动产生较大的波动.     

需求不确定的电动汽车换电站选址鲁棒模型

在电动汽车换电站选址问题中,用户充电需求是重要的输入参数,而需求受到技术发展、电动汽车保有量、国家相关政策等因素的影响,通常较难准确预测。假设交通网络上用户需求是不确定的,利用基约束鲁棒优化方法提出基于用户路径流量不确定的换电站选址鲁棒模型,并将鲁棒模型转换为等价的线性规划。最后应用Nguyen-Dupius网络作为数值算例进行分析,结果表明当解的鲁棒水平较低时,通过适当增加选址成本,可以较大程度地提高解的鲁棒性。同时对模型的重要参数作灵敏性分析,随着鲁棒水平的提高,换电站选址成本对路径流量的波动越来越敏感,续航里程的改变对换电站选址策略影响较大。     

基于OD的区域公路交通预测系统结构与关键技术

为了简化区域公路交通预测工作,结合我国目前公路交通特征,设计并建立基于OD更新的区域公路交通预测系统非常必要。从结构和功能出发,提出系统需具备数据管理、图形管理、OD更新、需求预测和交通量预测等5个基本模块。重点分析了系统实现过程中的一些关键技术,如数据关联和融合、图形数据接口、路网检查方法、控制点选择方法、相关路段筛选等。系统的开发和应用能提高区域公路交通信息化水平,帮助制定科学合理的规划设计和管理策略。     

A Hierarchical Location Model for Locating Bank Branches in a Competitive Environment

Recent developments in the provision of banking services are expected to affect the structure and the organization of modern banks significantly. Consequently, it has become necessary for most banks to reorganize their service network. In this context we demonstrate how demand–covering models may be combined with geographical information systems (GIS) to determine the optimal location of bank branches, taking into account the various factors that characterize local conditions within the demand area. The essence of the proposed models is to employ a GIS to represent various criteria concerning the demand for banking services (geographical, social, economic, etc.) as well as the competition in each particular area. This information is then taken into account by appropriate demand–covering models which determine the locations that achieve the maximum capture of the demand. Finally, we present some initial results concerning the application of this approach by a major Greek commercial bank.     

Network infrastructure design with a multilevel algorithm

This paper presents a novel algorithm to deal with the network design problem, which optimizes the network levels considering their interdependency. The idea is to design a low cost and optimized network providing the number and the geographic location of devices as well as the links among them for each network level, while taking into account the existing dependency among them. In addition, a new database composed of real and georeferenced data is created and make available for the research community. This database contains three datasets that represent distinct projects related to geographic regions of the city of Curitiba (Brazil). The experimental results show that the proposed algorithm provides a significant cost reduction in the network design. The savings of this proposal go from 1% to 40% (depending on the network size, number of levels and demand nodes), making it attractive for companies that spend a considerable amount of resources in network projects and deployment.     

Models and a relaxation algorithm for continuous network design problem with a tradable credit scheme and equity constraints

The sustainable problems of transportation have become noticeable in the majority of cities worldwide. Many researchers are devoted themselves into traffic congestion. Generally, traffic congestion could be alleviated via increasing road capacity (supply) or reducing traffic (demand). In this paper, we model CNDP which has a tradable credit scheme and equity constraints in order to research on the way of releasing congestion by combining increasing supply and reducing demand. Firstly, the bilevel programming problem is proposed to model the CNDP with a tradable credit scheme. The upper level (the government) chooses optimal capacity enhancement for some existing links to minimize the total system costs under a budget constraint. The lower level chooses the optimal route based on considering the generalized travel cost in which both travel time and credit charging for using the link are involved. And then, considering the inequity problem in terms of equilibrium O–D travel cost and link travel time, the model is proposed by incorporating equity constraints into CNDP with a tradable credit scheme. After presenting a relaxation algorithm, the experiments on Sioux Falls network are illustrated. Finally, conclusion and some future research directions are presented.     

Where will the next emergency event occur? Predicting ambulance demand in emergency medical services using artificial intelligence

Predicting demand in emergency medical services is crucial for saving people's lives. Most studies aggregate demand prediction within a zone, failing to offer insights at a more detailed level. This study aspires to fill this gap by introducing a novel, three-level, spatial-based approach that identifies the geographical location of expected emergency events. First, the proposed methodology introduces new concepts and notions to model emergency events, as sets of interconnected points in space, that create paths over time. Second, based on these paths, an artificial neural network, optimized using a new evolutionary algorithm, predicts the location of future demand (emergencies). Third, based on the predicted demand, a location-allocation model is applied to site ambulances prior to actual emergencies occurrence, enhancing thus location planning and decision making. This method is applied to a dataset comprising 2851 emergency events in Athens, Greece, and the outcomes are evaluated based on the actual emergency events occurred. Results show that the mean distance, between an actual emergency event and the nearest ambulance, located based on the expected demand as estimated by our approach, deviates by 110 m relative to the optimal solution. This deviation, adds only a few seconds of delay to the response time of an ambulance relative to the theoretically optimal solution (post hoc location). In addition, it improves the current solution (in which ambulances are waiting in a set of fixed location throughout the year), by >1 km, decreasing significantly response time. From a policy perspective, these results indicate that assessing expected emergency events through the proposed method, would allow medical services to optimally locate ambulances in advance, reducing response time and thus increasing survival rates and public safety.