The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.     

Scheduling trucks in cross-docking systems: A robust meta-heuristics approach

In a cross-docking system, trucks must be scheduled to minimize the total flow time of the system. This problem is NP-hard, and this study proposes two hybrid meta-heuristics—hybrid simulated annealing and hybrid variable neighborhood search—to solve it by achieving the best sequence of truck pairs. The Taguchi method serves to reveal the best robustness of these algorithms. To demonstrate the effectiveness of the proposed methods, especially for large-sized problems, this study solves various test problems, and the computational results clearly reveal that the proposed methods outperform previous approaches.     

Challenging the shipper's location problem in port studies: An analysis of French AOC wine shipments to the US

One of the major issues in port choice and hinterland studies is the identification of the inland origins of containerized shipments. Firms' headquarters, intermediary cross-docking facilities, and the real places of origin of cargo are used interchangeably. This confusion may lead to a wrong perception of the factors affecting port choice decisions. This paper proposes a solution to overcome this problem, by focusing on shipments of French AOC wines (Appellation d'Origine Contrôlée) for which the geographical origin is strictly controlled. The paper also contributes to the port choice and hinterlands literatures by testing three new variables related to the logistical organization of shipments. A conditional logit model is employed to estimate the probability of choosing a port taking in account heterogeneity in the characteristics of shipments and decision makers. The results confirm the importance of inland distance and maritime connectivity, and bring new insight on the impact of logistical organization of shipments.     

Consequences of logistics sprawl: Order or chaos? - the case of a parcel service company in Paris metropolitan area

Logistics sprawl is the phenomenon driving warehouses and freight terminals further and further away from city centers, for reasons that are mostly dependent on urban planning, field price and economies of scale. In the past, studies have already described logistics sprawl in Paris Metropolitan area. These studies have concluded that logistic facilities far from Paris city center increased the distance travelled, and by extension, CO₂ emissions. This study does not question logistics sprawl in Paris metropolitan area but questions its consequences.This study presents a point of view on the location of cross-docking terminals. One main parcel service carrier was studied through a database strong of 600,000 inputs including pick-ups and deliveries for two months in the Paris metropolitan area. This study is not intended to be representative of all merchandise flows but makes it possible to understand the running of one significant actor. This project aims at determining the optimum terminals location according to two criteria: minimization of the total distance as calculated by the Euclidean distance (as the crow flies) between terminals and points of pick-up/ delivery, and the number of authorized terminals. The number of terminals is modified to study its relationship with the spatial distribution of its clients.Interestingly, we found that optimized locations for this parcel service carrier are located outside of Paris city center. Those locations minimize the total distance to the clients, and thus, are the best places to limit pollution. The reason is that the carrier manages this perimeter through a polycentric organization. Paris Metropolitan area is viewed as several sectors and not as a whole for this carrier. It is therefore unknown how the carrier's performance has evolved over time, but its current organization is close to the optimal situation.     

A consolidated model of trip distribution

This work analyzes and compares various trip distribution models with spatial aggregation within a common theoretical framework for formulating and solving multi-objective optimization problems. A new model is designed that incorporates the main characteristics of existing ones. These models are then calibrated with a single database at different spatial aggregation levels using maximum likelihood. The results show that with aggregated data the various models differ little, but with disaggregated data the differences are considerable. It is also demonstrated that changing the level of data aggregation can significantly alter the models’ parameter values.     

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.     

Modeling freight distribution using artificial neural networks

Studies about freight distribution modeling are limited due to the limitations in data availability. Existing studies in this subject, generally either use the conventional gravity models or the regression based models as modeling techniques. The present study, using the 1993 US Commodity Flow Survey Data, models inter-regional commodity flows for 48 continental states of the US with three different artificial neural networks (ANN). The results are compared with those of Celik and Guldmann's (2002) Box–Cox Regression Model. The ANN using conventional gravity model variables provides a slight improvement with respect to this Box–Cox model. However, the ANNs using theoretically relevant variables provide surprising improvements in comparison to the Box–Cox model. It is concluded that ANN architecture is a very promising technique for predicting short-term inter-regional commodity flows.     

Driving demand: What can gasoline refueling patterns tell us about planning an alternative fuel network?

Knowing which variables predict gasoline demand can help inform which are useful in determining future demand at an alternative fuel station such as those for bio-fuels, natural gas, hydrogen, or fast-charge electricity. This study explores the spatial distribution of demand by comparing two main classes of variables: those without a displacement component such as population in a census block group, and those that imply a vector or directionality such as vehicle kilometers traveled. The spatial distribution of these variables is compared to the spatial distribution of demand for gasoline using regression. Many models examining the transition from gasoline to an alternative fuel assume a demand pattern for fuel a priori in order to estimate potential demand at a future alternative fuel station. This paper studies not the models themselves but the variables used to predict demand. The results indicate that vehicle kilometers traveled (VKT) is the best variable to pinpoint where demand for fuel will occur. However, travel to the central business district of the metropolitan area does not appear to translate into demand for fuel in proportion to the VKT. While gasoline demand does appear to vary with population as well, the location of demand is much less specific than that predicted by VKT. The results also suggest that the route between home and the nearest freeway entrance may help predict a large portion of refueling and merits further investigation. This possible tendency can be used to create a new variable called “population-traffic” which appears to describe the spatial distribution of demand well. The good performance of this independent variable in regressions suggests that stations sited along the freeway may serve customers needs and provide the necessary concentration of demand for initial alternative fuel stations. A practical application of this work would be to help define refueling demand patterns in a rollout of alternative fueled vehicles in a neighborhood or town.     

A top-down approach and a decision support system for the design and management of logistic networks

This paper presents an original top-down approach, made of original models and solving methods, and a decision support system (DSS) for the execution of the strategic planning, the tactical planning and the operational planning in a multi-echelon multi-stage multi-commodity and multi-period production, distribution and transportation system. The DSS is a software platform useful for the design, management and control of real instances. It can efficiently supports the decision making process of logistic managers and planners of large enterprises as multi-facilities companies and production–distribution networks. A significant case study is illustrated. The results obtained by the application of different problem settings are compared and discussed.     

A top-down approach and a decision support system for the design and management of logistic networks

This paper presents an original top-down approach, made of original models and solving methods, and a decision support system (DSS) for the execution of the strategic planning, the tactical planning and the operational planning in a multi-echelon multi-stage multi-commodity and multi-period production, distribution and transportation system. The DSS is a software platform useful for the design, management and control of real instances. It can efficiently supports the decision making process of logistic managers and planners of large enterprises as multi-facilities companies and production–distribution networks. A significant case study is illustrated. The results obtained by the application of different problem settings are compared and discussed.     

Unobserved heterogeneity in transportation equity analysis: Evidence from a bike-sharing system in southern Tampa

Assessing the equity impacts of transportation systems/policies has become a crucial component in transportation planning. Existing statistical modeling approaches for transportation equity analysis have typically assumed that parameter estimates are constant across all observations and used data aggregated to certain geographic units for the analysis. Such methods cannot capture unobserved factors that are not contained in the dataset, i.e., unobserved heterogeneity, which is likely to be present in the increasingly popular disaggregated datasets. To investigate whether there is unobserved heterogeneity in transportation equity impacts, this study carries out an empirical study focusing on the distribution of individual accessibility to activity locations via bike-sharing in southern Tampa. A disaggregated dataset containing information on individual bike-sharing accessibility and socio-economic factors is modeled with a random parameters logit model that allows for the investigation of possible unobserved heterogeneity. Further, models are estimated using data aggregated to parcel- and TAZ-levels to explore the impacts of data aggregation on model estimation results. The models unveil the unobserved heterogeneity in bike-sharing accessibility among populations in different groups defined by different sociodemographic factors in southern Tampa. These results shed insights into how the inconsistent disparity direction of transportation outcomes across individuals in a population group can be measured from the heterogeneity effects. Finally, a comparison between different models show that to capture such inconsistency, the use of disaggregated data with heterogeneity models is highly recommended for transportation equity analysis.     

Systematic comparison of trip distribution laws and models

Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning. Several approaches have been considered in the last years. One of them is the so-called gravity law, in which the number of trips is assumed to be related to the population at origin and destination and to decrease with the distance. The mathematical expression of this law resembles Newton's law of gravity, which explains its name. Another popular approach is inspired by the theory of intervening opportunities which argues that the distance has no effect on the destination choice, playing only the role of a surrogate for the number of intervening opportunities between them. In this paper, we perform a thorough comparison between these two approaches in their ability at estimating commuting flows by testing them against empirical trip data at different scales and coming from different countries. Different versions of the gravity and the intervening opportunities laws, including the recently proposed radiation law, are used to estimate the probability that an individual has to commute from one unit to another, called trip distribution law. Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models. We show that the gravity law performs better than the intervening opportunities laws to estimate the commuting flows, to preserve the structure of the network and to fit the commuting distance distribution although it fails at predicting commuting flows at large distances. Finally, we show that the different approaches can be used in the absence of detailed data for calibration since their only parameter depends only on the scale of the geographic unit.     

A review of deterministic, stochastic and hybrid vehicular exhaust emission models

Vehicles spend more time near junctions and intersections in different driving modes, i.e., queuing, decelerating or accelerating and thus generating more pollutants than at road links [Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633-1642]. As a result, the receptors in these urban corridors are prone to frequent exposures of high pollutant concentrations (episodic conditions). In order to predict such ‘episodes’, an air quality model, capable of estimating the entire range (middle and extremes) of pollutant concentration distribution is needed. Hybrid models (combining deterministic and statistical distribution models) have demonstrated the ability to predict the entire range of pollutant concentrations in such co mplex dispersion situations with reasonable accuracy [Jakeman, A., Simpson, R.W., Taylor, J.A., 1988. Modelling distributions of air pollutant concentrations-III: Hybrid modelling deterministic-statistical distributions. Atmos. Environ. 22 (1) 163-174]. The present paper reviews the relevant deterministic and stochastic based vehicular exhaust emission models that may be hybridized and thus generate a hybrid model with improved prediction accuracy. The paper also describes the implications of hybrid models in formulating the Episodic-Urban Air Quality Management Plan (e-UAQMP).     

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.     

A new zone system to analyze the spatial relationships between the built environment and traffic safety

This study aims to investigate the impacts of the built environment on traffic safety at a zonal level using a newly developed crash-related zone system. Traffic analysis zones (TAZs) have been widely employed to analyze traffic safety at a macroscopic level. However, this zone system use may present problems. Unlike previous studies, in which new zoning systems were created from aggregating TAZs, in this study the new zone system, formed by traffic safety analysis zones (TSAZs), is created from the smallest available census units. Geographically Weighted Negative Binomial Regression (GWNBR) models are used and a comparative analysis between non-spatial global crash prediction models and spatial local GWPR (Geographically Weighted Poisson Regression) and GWNBR models using the two zonal systems is presented. We find that TSAZs based models performed better than TAZs based models, especially when combined to the GWNBR technique. Our results show that several features of the built environment are significant crash predictors, and that the relationships among these features and traffic safety vary across space. By combining a crash-related zonal system with spatial GWNBR models to understand the built environment effects on traffic safety, the results of the analysis can help urban planners to consider traffic safety proactively when planning or retrofitting urban areas.     

Exploring the role of worker income and workplace characteristics on the journey to work

Traditional trip distribution processes that rely heavily on gravity models fail to capture how the characteristics of individuals or the heterogeneity in the attributes of attraction zones may influence the accessibility to jobs and, therefore, journey-to-work patterns. Different approaches, such as destination choice models, are not generally applied because of limited data availability and calibration requirements. This paper proposes an alternative approach to overcome this challenge by combining a utility-based measure of accessibility and a maximum range of commuting distance to predict the journey-to-work patterns of individual worker-agents using an open-access database. A multinomial logit model is estimated and an agent-based model is developed using data from the Census Transportation Planning Products (CTPP) 5-year database. The proposed methodology is demonstrated using a case study based on Tippecanoe County, Indiana, and the results are compared to a double-constrained gravity model. Results indicate that the utility functions derived from the CTPP database can replicate the aggregated journey-to-work patterns by income levels. Furthermore, it was found that the utility functions for low-, middle-, and high-household income groups could be different. Finally, while a calibrated gravity model could produce a trip-length distribution and average commuting distance more similar to observed data, the destination choice model provides more insights into the trip patterns across different household income groups, thereby providing a better basis for policy analysis.     

Modeling aggregate air-travel itinerary shares: logit model development at a major US airline

This study reports the results of aggregate air-travel itinerary share models estimated at the city-pair level for all city-pairs in the US. These models determine the factors that influence airline ridership at the itinerary level and support carrier decision-making. The models are estimated using aggregate multinomial logit methodology and use comprehensive data. Independent variables for the models measure various itinerary service characteristics: level-of-service, connection quality, carrier, carrier market presence, fares, aircraft size and type, and time of day. The results are intuitive, and validation tests indicate that the models outperform existing methods. Finally, the impacts of changing various itinerary service attributes on carrier market share are discussed.     

Level of aggregation of zoning and temporal transferability of the gravity distribution model: The case of Lyon

This study deals with the temporal transferability of the parameters of the gravity model of trip distribution and focuses on the trade-off between spatial resolution and data requirements. The models are calibrated using O–D matrices constructed from the three most recent Lyon household travel surveys (1985, 1995 and 2006) and generalised travel time data from coded transport networks for the three dates. Calibration has been conducted for three different zoning levels which have been chosen in line with common practice. The parameters obtained from model calibration are then applied to estimate O–D matrices at a later date and the results are compared using indicators that have been established for the zoning level applied in calibration, but also using indicators that have been aggregated in two different ways: aggregation to create larger zones or distance segments.Our findings confirm our initial intuition: the choice of zoning is fundamentally important. Moreover, in the best case, the parameters of the model change, but not sufficiently for the goodness-of-fit of the “predicted” model to be very different from that of the matrix obtained during calibration. It is possible to use the gravity model for forecasting purposes, but on condition that the goals of the study are compatible with the level of error in the reproduction of the observed matrices. If the zoning is either too coarse or too fine grained, forecasting performance is compromised.     

Design of a rail transit line for profit maximization in a linear transportation corridor

This paper addresses the design problem of a rail transit line located in a linear urban transportation corridor. The service variables designed are a combination of rail line length, number and locations of stations, headway and fare. Two profit maximization models, which account for the effects of different transit pricing structures (flat and distance-based fare regimes), are proposed. In the proposed models, the effects of passenger demand elasticity and population density along the urban corridor are explicitly considered. The solution properties of the proposed models are explored and compared analytically, and the indifference condition for the two fare regimes in terms of the operator’s net profit is identified. A heuristic solution algorithm to solve the proposed models is presented. Numerical examples are provided to show the effects of the fare regimes, rail capital cost and urban configuration (in terms of urban population distribution and corridor length) on the design of the rail transit line and the profitability of the rail transit operations.     

Assessing the road safety impacts of a teleworking policy by means of geographically weighted regression method

Travel demand management (TDM) consists of a variety of policy measures that affect the effectiveness of transportation systems by changing travel behavior. The primary objective of such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to simulate the traffic safety impact of conducting a teleworking scenario (i.e. 5% of the working population engages in teleworking) in the study area, Flanders, Belgium. Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models should also be developed at an aggregate level. Given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the number of crashes (including fatal, severe and slight injury crashes recorded between 2004 and 2007) and other explanatory variables. Different exposure, network and socio-demographic variables of 2200 traffic analysis zones (TAZs) are considered as predictors of crashes. An activity-based transportation model framework is adopted to produce detailed exposure metrics. This enables to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this study, several ZCPMs with different severity levels and crash types are developed to predict crash counts for both the null and the teleworking scenario. The results show a considerable traffic safety benefit of conducting the teleworking scenario due to its impact on the reduction of total vehicle kilometers traveled (VKT) by 3.15%. Implementing the teleworking scenario is predicted to reduce the annual VKT by 1.43 billion and the total number of crashes to decline by 2.6%.