Corrigendum to “Simultaneous location of a service facility and a rapid transit line” [Computers & Operations Research 38(2) (2011) 525–538]

In this paper we provide a finite set of candidates to be one of the endpoints of an optimal rapid transit line for the problem of locating simultaneously a service facility and a rapid transit line.     

An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem

We model and solve the Railway Rapid Transit Network Design and Line Planning (RRTNDLP) problem, which integrates the two first stages in the Railway Planning Process. The model incorporates costs relative to the network construction, fleet acquisition, train operation, rolling stock and personnel management. This implies decisions on line frequencies and train capacities since some costs depend on line operation. We assume the existence of an alternative transportation system (e.g. private car, bus, bicycle) competing with the railway system for each origin–destination pair. Passengers choose their transportation mode according to the best travel times. Since the problem is computationally intractable for realistic size instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm, which can simultaneously handle the network design and line planning problems considering also rolling stock and personnel planning aspects. The ALNS performance is compared with state-of-the-art commercial solvers on a small-size artificial instance. In a second stream of experiments, the ALNS is used to design a railway rapid transit network in the city of Seville.     

On solving unreliable planar location problems

In this paper, we generalize conventional P-median location problems by considering the unreliability of facilities. The unreliable location problem is defined by introducing the probability that a facility may become inactive. We proposed efficient solution methods to determine locations of these facilities in the unreliable location model. Space-filling curve-based algorithms are developed to determine initial locations of these facilities. The unreliable P-median location problem is then decomposed to P 1-median location problems; each problem is solved to the optimum. A bounding procedure is used to monitor the iterative search, and to provide a consistent basis for termination. Extensive computational tests have indicated that the heuristics are efficient and effective for solving unreliable location problems.Scope and purposeThis paper addresses an important class of location problems, where p unreliable facilities are to be located on the plane, so as to minimize the expected travel distance or related transportation cost between the customers and their nearest available facilities. The unreliable location problem is defined by introducing the probability that a facility may become inactive. Potential application of the unreliable location problem is found in numerous areas. The facilities to be located can be fire station or emergency shelter, where it fails to provide service during some time window, due to the capacity or resource constraints. Alternatively, the facilities can be telecommunication posts or logistic/distribution centers, where the service is unavailable due to breakdown, repair, shutdown of unknown causes. In this paper, we prescribed heuristic procedures to determine the location of new facilities in the unreliable location problems. The numerical study of 2800 randomly generated instances has shown that these solution procedures are both efficient and effective, in terms of computational time and solution quality.     

A multi-objective simulated annealing for the multi-criteria dial a ride problem

The paper describes a multi-objective mathematical model for Dial a Ride Problem (DRP) and an application of Multi-Objective Simulated Annealing (MOSA) to solve it. DRP is to take over the passenger from a place of departure to a place of arrival. In the DRP, customers send transportation requests to an operator. A request consists of a specified pickup location and destination location along with a desired departure or arrival time. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. The DRP is classified as NP-hard problem that's why most research has been concentrated on the use of approximate methods to solve it. Indeed the DRP is a multi-criteria problem, the proposed solution of which aims to reduce both route duration in response to a certain quality of service provided. In this work, we offer our contribution to the study and solving the DRP in the application using the MOSA algorithm. Tests show competitive results on (Cordeau and Laporte, 2003a) benchmark datasets while improving processing times.     

Integrated approach to network design and frequency setting problem in railway rapid transit systems

This work presents an optimization-based approach to simultaneously solve the Network Design and the Frequency Setting phases on the context of railway rapid transit networks. The Network Design phase allows expanding existing networks as well as building new ones from scratch, considering infrastructure costs. In the Frequency Setting phase, local and/or express services are established considering transportation resources capacities and operation costs. Integrated approaches to these phases improve the transit planning process. Nevertheless, this integration is challenging both at modeling and computational effort to obtain solutions. In this work, a Lexicographic Goal Programming problem modeling this integration is introduced, together with a solving strategy. A solution to the problem is obtained by first applying a Corridor Generation Algorithm and then a Line Splitting Algorithm to deal with multiple line construction. Two case studies are used for validation, including the Seville and Santiago de Chile rapid transit networks. Detailed solution reports are shown and discussed. Conclusions and future research directions are given.     

The location of a hospital in a rural region: The case of the Negev

This paper reports on the strategic planning of hospital services in the Negev region of Israel, a sparsely populated region experiencing rapid population growth. One of the questions that concerned the decision-makers was whether to increase the medical services in the area by expanding the existing hospital or by building an additional one. The suggestion to build a new hospital raised questions of location. The purpose of this paper is to address this problem. Objective and subjective approaches were employed in a three-stage procedure in order to find the best location. First the problem was analyzed using established location models to generate a set of candidate locations. In the next stages experts evaluated the alternative locations by employing a subjective multicriteria model—the Analytical Hierarchy Process (AHP). Finally, a recommended solution was presented and analyzed in light of the strategic plan.     

Adaptive Park-and-ride Choice on Time-dependent Stochastic Multimodal Transportation Network

In transportation networks with stochastic and dynamic travel times, park-and-ride decisions are often made adaptively considering the realized state of traffic. That is, users continue driving towards their destination if the congestion level is low, but may consider taking transit when the congestion level is high. This adaptive behavior determines whether and where people park-and-ride. We propose to use a Markov decision process to model the problem of commuters’ adaptive park-and-ride choice behavior in a transportation network with time-dependent and stochastic link travel times. The model evaluates a routing policy by minimizing the expected cost of travel that leverages the online information about the travel time on outgoing links in making park-and-ride decisions. We provide a case study of park-and-ride facilities located on freeway I-394 in Twin Cities, Minnesota. The results show a significant improvement in the travel time by the use of park-and-ride during congested conditions. It also reveals the time of departure, the state of the traffic, and the location from where park-and-ride becomes an attractive option to the commuters. Finally, we show the benefit of using online routing in comparison to an offline routing algorithm.

     

基于Anylogic的轨道交通换乘能力改进的研究

研究轨道交通换乘枢纽与市内其它交通方式的衔接与协调,是城市公共交通线网优化的主要内容之一。它能减少出行过程中的等待时间,缩短人们出行时间,提高公交服务质量,并保证客运交通的高效率,也能更好地促进城市轨道交通与其它交通方式的协调发展。以武汉地铁某换乘站研究对象,通过实地采集其基本设施的位置和数量、入站行人和出站行人的数量,并利用Anylogic仿真软件,建立站点的平面布置图空间模型和行人流程图。首先通过仿真软件仿真找出换乘客流瓶颈部位,其次分别对其提出改善建议并加以修改仿真模型。最终仿真结果表明,改善建议使得换乘站的换乘效率都得到提高。     

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview

In this paper, a novel multi-objective mathematical model is developed to solve a capacitated single-allocation hub location problem with a supply chain overview. Three mathematical models with various objective functions are developed. The objective functions are to minimize: (a) total transportation and installation costs, (b) weighted sum of service times in the hubs to produce and transfer commodities and the tardiness and earliness times of the flows including raw materials and finished goods, and (c) total greenhouse gas emitted by transportation modes and plants located in the hubs. To come closer to reality, some of the parameters of the proposed mathematical model are regarded as uncertain parameters, and a robust approach is used to solve the given problem. Furthermore, two methods, namely fuzzy multi-objective goal programming (FMOGP) and the Torabi and Hassini's (TH) method are used to solve the multi-objective mathematical model. Finally, the concluding part presents the comparison of the obtained results.     

Optimizing Frequencies in a Transit Network: a Nonlinear Bi-level Programming Approach

We consider the problem of optimizing the frequencies of transit lines in an urban transportation network. The problem is formulated first as a nonlinear nonconvex mixed integer programming problem and then it is converted into a bi-level Min–Min nonconvex optimization problem. This problem is solved by a projected (sub)gradient algorithm, where a (sub)gradient is obtained at each iteration by solving the lower level problem. Computational results obtained with this algorithm are presented for the transit networks of the cities of Stockholm, Sweden, Winnipeg, Man., Canada and Portland, OR, U.S.A.     

Planning the structure of the parcel flow in a postal logistics system

This article describes a computerized simulation model that makes it possible to compare alternative centre structures for the parcel flow in a postal logistics system. The main elements of a logistics system are presented in the introduction. The parcels are subject to two major activities, transportation and sorting. Concentrating sorting activities in a few sorting centres gives large scale advantages. But on the other hand transportation costs are augmented because parcels will make a detour before reaching their destination. Given an initial centre structure, i.e. number and location of potential sorting centres, the simulation model makes use of a heuristic procedure to determine the principle of processing for each individual parcel, i.e. central or decentrai sorting. The calculations are based on a mathematical model as described in section 2. The model comprises physical characteristics, costs and goals for service levels. The heuristic principle of the model is described in section 3. The heuristic procedure starts with an initial solution for transportation and sorting that is ameliorated step by step using different saving criteria for sub-optimization of parts of the model. Finally section 4 presents the application of the simulation model.     

Identifying bus stop redundancy: A gis-based spatial optimization approach

Transit planners are often faced with a tradeoff between improving accessibility through the addition of stops while simultaneously increasing efficiency so that destinations can be reached in a reasonable amount of time. In this paper, we propose the development of an optimization framework integrated within a Geographical Information System (GIS) for addressing this specific problem. Our proposed modeling framework departs from well-known facility location coverage models by considering both the impact of walking distance from an individual residential location to a stop and the transit facility attractiveness (ease to cross, number of destinations served). Integration within a GIS environment is accomplished using a simulated annealing heuristic. An example on an inbound urban bus route illustrates the utility of the approach for transit planning, using model parameters developed in collaboration with local transit agencies.     

Transit network design by Bee Colony Optimization

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate high-quality solutions.     

Multidepot pickup and delivery problems in multiple regions: a typology and integrated model

The rapid development experienced by the transportation industry in the past decades has led to many configurations of networks and therefore to an explosion of variants in transportation problems, motivating researchers to look at broader logistic problems, beyond the basic vehicle routing problems. This work introduces a new type of problem scenario combining various attributes: a pickup and delivery problem with multiple regions, multiple depots, and multiple transportation modes. We provide definitions, a literature review, and a step‐by‐step construction of the mathematical models from a simple and well‐known scenario to the multiregion multidepot pickup and delivery problem (MR‐MDPDP). For each step the relevant literature is examined. Furthermore, we suggest possible extensions for prospective research.     

Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis)investments over time

We put forward a new data object called the public transit travel time cube and demonstrate how the cube can be used in the analysis of transit travel time changes over space and time. The travel time cube contains the shortest path transit travel time between sets of origins and destinations in the city, at all times of day. Once computed, a wide range of investigations become readily available to the transit planner or transportation researcher. We conduct three demonstrative analyses using travel time cubes for the Wasatch Front, Utah and the Portland region in Oregon. Our studies investigate how travel times were impacted by service cuts and expansions in the two regions respectively and the impact this had on jobs accessibility. We also use the travel time cube to study the last mile problem, and compute the travel time savings and the stability gained by solving the last mile problem with bicycling. The paper concludes with an expanded discussion on the merits of the travel time cube and outlines four avenues for continued research.     

An interactive possibilistic programming approach for a multi-objective hub location problem: Economic and environmental design

This paper presents a new multi-objective mathematical model for a multi-modal hub location problem under a possibilistic-stochastic uncertainty. The presented model aims to minimize the total transportation and traffic noise pollution costs. Furthermore, it aims to minimize the maximum transportation time between origin-destination nodes to ensure a high probability of meeting the service guarantee. In order to cope with the uncertainties and the multi-objective model, we propose a two-phase approach, including fuzzy interactive multi-objective programming approach and an efficient method based on the Me measure. Due to the NP-hardness of the presented model, two meta-heuristic algorithms, namely hybrid differential evolution and hybrid imperialist competitive algorithm, are developed. Furthermore, a number of sensitivity analyses are provided to demonstrate the effectiveness of the presented model. Finally, the foregoing meta-heuristics are compared together through different comparison metrics.     

A multimodal semantic location service for intelligent environments: an application for Smart Hospitals

This paper presents semantic models, mechanisms and a service to locate mobile entities in Smart and Intelligent Environments. The key feature of the service is the semantic integration of different positioning systems that not only enables the environment to handle transparently such physical positioning systems, but also to reason on location information coming from different systems and to combine it to obtain higher context information. Indeed, the service relies on the use of ontologies and rules to define a uniform, unambiguous and well-defined model for the location information, independently of the particular positioning system. Moreover, the location service performs logic and reasoning mechanisms to provide both physical and semantic locations of mobile objects and to infer the finest granularity in the case when a mobile object is located by more than one positioning system. Finally, we present an application of the proposed approach to the case of a Smart Hospital.     

An integrated inventory problem with transportation in a divergent supply chain under service level constraint

This study investigates an integrated inventory problem with transportation in a single-vendor and multi-buyer divergent supply chain. The vendor manufactures a product and delivers the product to the buyers located in different locations by a fleet of vehicles of identical capacity. The external demands per unit time on the buyers are independent and normally distributed. The lead time components of the buyers, excluding transportation time, can be reduced at an added crash cost. A model has been formulated to minimize the total expected cost of the system associated with the production, inventory, transportation and lead time reduction to find the optimal production, inventory and routing decisions while satisfying the service level constraint of the buyers. We propose a coordinated two-phase iterative approach to solve the model, which has been illustrated through a numerical example.     

Integrated Inventory Control and Facility Location Decisions in a Multi-Echelon Supply Chain Network with Hubs

This paper develops mathematical models to coordinate facility location and inventory control for a four-echelon supply chain network consisting of multiple suppliers, warehouses, hubs and retailers. The hubs help in reducing transportation costs by consolidating products from multiple warehouses and directing the larger shipments to the retailer. The integrated models studied in this paper simultaneously determines three types of decisions: (i) facility location—the number and location of warehouses and hubs, (ii) allocation—assignment of suppliers to located warehouses and retailers to located warehouses via the location hubs, and (iii) inventory control decisions at each located warehouse. The goal is to minimize the facility location, transportation and the inventory costs. A mixed integer nonlinear programming formulation is first presented. The nonlinear integer programming formulation is then transformed into a conic mixed integer program and a novel and compact conic mixed integer programming formulation. Computational runs are conducted using commercial solvers to compare the performance of the different formulations. The compact conic mixed integer programming formulation was found to significantly outperform the other formulations by achieving significant computational savings. The results demonstrate that large scale instances of certain multi-echelon supply chain network design problems can be solved using commercial solvers through intelligent reformulation of the model.     

Optimizing the Location of a Production Firm

A facility needs to be located in the plane to sell goods to a set of demand points. The cost for producing an item and the actual transportation cost per unit distance are given. The planner needs to determine the best location for the facility, the price charged at the source (mill price) and the transportation rate per unit distance to be charged to customers. Demand by customers is elastic and assumed declining linearly with the total charge. For each customer two parameters are given: the demand at charge zero and the decline of demand per unit charge. The objective is to find a location for the facility in the plane, the mill price charged to customers and the unit transportation rate charged to customers such that the company’s profit is maximized. The problem is formulated and an algorithm that finds the optimal solution is designed and tested on randomly generated problems.