Meta-heuristic approach for high-demand facility locations considering traffic congestion and greenhouse gas emission

Large facilities in urban areas, such as storage facilities, distribution centers, schools, department stores, or public service centers, typically generate high volumes of accessing traffic, causing congestion and becoming major sources of greenhouse gas (GHG) emission. In conventional facility-location models, only facility construction costs and fixed transportation costs connecting customers and facilities are included, without consideration of traffic congestion and the subsequent GHG emission costs. This study proposes methods to find high-demand facility locations with incorporation of the traffic congestion and GHG emission costs incurred by both existing roadway traffic and facility users into the total cost. Tabu search and memetic algorithms were developed and tested with a conventional genetic algorithm in a variety of networks to solve the proposed mathematical model. A case study to determine the total number and locations of community service centers under multiple scenarios in Incheon City is then presented. The results demonstrate that the proposed approach can significantly reduce both the transportation and GHG emission costs compared to the conventional facility-location model. This effort will be useful for decision makers and transportation planners in the analysis of network-wise impacts of traffic congestion and vehicle emission when deciding the locations of high demand facilities in urban areas

Concierge Service Problem for Location -Based Services: Combined-Cost and Multi Objective Approaches

110 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.In this research, we defined and formulated CSP for both general and simple cases, and developed exact and heuristic solution algorithms for both combined-cost and multi-objective approaches. The exact algorithms include the adaptive dynamic programming with or without multiple alternative solutions. The heuristic solution algorithms vary from the Euclidean heuristic, the modified K-th shortest path method, the single minimum cost POI method, and Genetic Algorithms. The computational performance of suggested algorithms has been evaluated for its efficiency and validated by implementing them using large size metropolitan networks; Chicago, Illinois and Seoul, Korea for various service scenarios.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

The traveling purchaser problem with stochastic prices: Exact and approximate algorithms

The paper formulates an extension of the traveling purchaser problem where multiple types of commodities are sold at spatially distributed locations with stochastic prices (each following a known probability distribution). A purchaser's goal is to find the optimal routing and purchasing strategies that minimize the expected total travel and purchasing costs needed to purchase one unit of each commodity. The purchaser reveals the actual commodity price at a seller upon arrival, and then either purchases the commodity at the offered price, or rejects the price and visits a next seller. In this paper, we propose an exact solution algorithm based on dynamic programming, an iterative approximate algorithm that yields bounds for the minimum total expected cost, and a greedy heuristic for fast solutions to large-scale applications. We analyze the characteristics of the problem and test the computational performance of the proposed algorithms. The numerical results show that the approximate and heuristic algorithms yield near-optimum strategies and very good estimates of the minimum total cost.Traveling purchaser problem Stochastic price Dynamic programming Approximation Heuristic

Heuristic Algorithm for Solving a Multimodal Location-Based Concierge Service Problem

As a location-based services problem, the concierge service problem is to find a minimum total cost for purchasing predetermined types and quantities of items with the shortest paths connecting the locations where the items are available. These locations are defined as points of interest (POIs). The total cost includes purchasing and stopping costs at POIs as well as the travel cost from origin to destination. To respond to a request for such a service, a search algorithm should be reasonably fast in computational time and high in accuracy. A heuristic search algorithm was developed by a Euclidean distance approach using a geographic information system. The algorithm was implemented with 1,248 POIs in the Seoul, South Korea, metropolitan area as a case study site. The road network is composed of 52,915 nodes and 77,339 links, and the existing subway network, and all existing bus routes were used for the implementation. Four scenarios were examined: a request for a service in peak and off-peak periods with and without turning restrictions. The results indicate that the computational time in each case is less than 4 s with the transit networks only and less than 30 s if the road network is used. The paper also evaluates the solutions generated from the algorithm

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

One of the serious concerns in network design is creating an efficient and appropriate network capable of efficiently migrating the passenger’s mode of transportation from private to public. The main goal of this study is to present an improved model for combining the feeder bus network design system and the railway transit system while minimizing total cost. In this study, the imperialist competitive algorithm (ICA) and the water cycle algorithm (WCA) were employed to optimize feeder bus and railway services. The case study and input data were based on a real transit network in Petaling Jaya, Kuala Lumpur, Malaysia. Numerical results for the proposed model, including the optimal solution, statistical optimization results and the convergence rate, as well as comparisons are discussed in detail

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

One of the serious concerns in network design is creating an efficient and appropriate network capable of efficiently migrating the passenger’s mode of transportation from private to public. The main goal of this study is to present an improved model for combining the feeder bus network design system and the railway transit system while minimizing total cost. In this study, the imperialist competitive algorithm (ICA) and the water cycle algorithm (WCA) were employed to optimize feeder bus and railway services. The case study and input data were based on a real transit network in Petaling Jaya, Kuala Lumpur, Malaysia. Numerical results for the proposed model, including the optimal solution, statistical optimization results and the convergence rate, as well as comparisons are discussed in detail

Biofuel refinery location and supply chain planning under traffic congestion

This research focuses on planning biofuel refinery locations where the total system cost for refinery investment, feedstock and product transportation and public travel is minimized. Shipment routing of both feedstock and product in the biofuel supply chain and the resulting traffic congestion impact are incorporated into the model to decide optimal locations of biofuel refineries. A Lagrangian relaxation based heuristic algorithm is introduced to obtain near-optimum feasible solutions efficiently. To further improve optimality, a branch-and-bound framework (with linear programming relaxation and Lagrangian relaxation bounding procedures) is developed. Numerical experiments with several testing examples demonstrate that the proposed algorithms solve the problem effectively. An empirical Illinois case study and a series of sensitivity analyses are conducted to show the effects of highway congestion on refinery location design and total system costs.Biofuel refinery location Supply chain planning Traffic congestion Mixed-integer program Lagrangian relaxation Branch-and-bound

Optimal operations of transportation fleet for unloading activities at container ports

This paper presents mathematical models that optimize the size of transportation fleet (cranes and trucks) for unloading operations at container terminals. A cyclic queue model is used to study the steady-state port throughput, which then yields the optimum fleet size for long-term operations. This model allows for stochastic operations such as exponentially distributed crane service times. In order to allow for generally distributed crane service times and truck travel times, an approach based on Markovian decision process is also proposed. This model provides dynamic operational policies for fleet management. Both models are implemented and examined with empirical data from the Port of Balboa, Panama. These models are also extended to unloading operations that involve multiple berths.

Differential Dynamics of Transit Use Resilience During the COVID-19 Pandemic Using Multivariate Two-Dimensional Functional Data Analysis

This study delves into the nuanced patterns of shock and recovery in transit ridership during and after the COVID-19 pandemic, aiming to illuminate the resilience exhibited by various geographic areas. This resilience is measured by the ability of transportation systems to withstand, adapt to, and bounce back from unforeseen shocks. In this research, smart card big data were exploited to track real-time mobility dynamics and economic activity within the city of Seoul, Korea. The approach employed multivariate two-dimensional functional data analysis and a hierarchical clustering method to examine both boarding and alighting patterns, taking into account multi-scalar temporal units, monthly and hourly demand fluctuations. The findings present distinct varied shock-and-recovery patterns across areas in transit ridership based on the socioeconomic characteristics of specific areas. These characteristics encompass factors such as industry and land-use composition, income levels, population density, and proximity to points of interest. Additionally, this methodology proves effective in identifying abnormal surges in demand linked to local large-scale development projects

Analysis on taxi operation using digital tachograph and meter data in Korea

The purpose of this study is to analyze the taxi operation data from Digital Tachograph (DTG) and taxi meter and to provide a support for the government policy decision and taxi company operation. Data from DTG include speed, acceleration, direction, GPS coordinates, engine condition, etc. From taxi meter, meter on-off information and real-time fare data are collected automatically. By combining these two types of data, we can perform 1) demand-supply analysis, 2) driving pattern analysis and 3) illegal operation analysis. For this study, one day data from 2,263 taxis in City of Daejeon are used