Temporal stability of shipment size decisions related to choice of truck type

The choice of shipment size is a vital decision in logistics and has a strong indirect influence on freight transport demand, via the choice of mode and truck type choice. Through time, shipment sizes can change as a result of new decisions in the logistics process or due to conditions external to the supply chain. This study investigates the temporal stability of shipment size choices, relating these to the choice of truck types. It uses repeated cross-sectional data for the years 2015, 2017, and 2019 collected from cordon and business establishment surveys in Addis Ababa city, Ethiopia. The integrated choice and latent variables (ICLV) and latent growth (LG) models were used to assess the time-dependent patterns of choosing shipment sizes, both at the level of the entire freight system as well as the specific truck types. The model results reveal that shipment size decisions are temporally unstable where, in our case, shipment sizes exhibited a declining trend

A Multi-echelon Network Distribution Model For Emergency Resource Planning

Resource planning in emergency management is a challenging task as it involves disaster situations where the demands are rapidly increasing and the resources are scarce. Conventional planning involves a centralized network structure where resources are distributed through a few prepositioning facilities located near to the disaster regions. In this research, we develop a novel multi-echelon network distribution structure for emergency resource planning. The structure at its highest echelon consists of a set of potential Supply Points (SPs), where resources are purchased and consolidated which is more practical in comparison to the conventional centralized structure. SPs are considered as typically large facilities in metropolitan cities in and around the potential disaster region from where the resources are distributed to the prepositioning facilities in order to be able to supply the materials immediately after the disaster in the area. The proposed structure also allows direct shipment of resources from SPs to the disaster regions in the response stage which is more close to the reality. Under the network structure, we formulate a new two-stage stochastic mixed integer programming model for an integrated emergency preparedness and response planning. The objective is to obtain the optimal allocations of the resources along with locations of the SPs and prepositioning facilities to satisfy the demand of disaster victims in a timely and cost-effective manner. We assume demand for supplies in the disaster hit areas are aggregated at locations called Aggregated Demand Points (ADPs). For the current study, the demands at the ADPs are obtained with a set of disaster scenarios each with a probability of occurrence. To develop the resource allocation model, we consider two distribution stages that are decided simultaneously: pre-disaster and post-disaster stages. In the pre-disaster stage, the analysis provides the location of SPs and the pre-disaster purchasing amounts to be acquired at the SPs. All or part of the purchased resources are positioned in prepositioning facilities located at selected ADPs. In the post-disaster stage, detail distribution of the resources to satisfy demands following the disaster event is considered. The demands in the post-disaster stage are met through pre-positioned resources at the prepositioning facilities and additionally, if required, through the direct shipments of resources from the SPs. We consider limited post-disaster purchasing opportunities at SPs as the quantities are to be purchased during a short and chaotic period. The optimization model proposed in this research consists of logistics and deprivation costs. The logistics costs include cost of provisioning, prepositioning, and delivering the resources. The deprivation costs represent the cost of not providing or delays in providing the supplies at the point of demand. The model is tested in a network for numerical analysis. The result shows that multiple SPs in the proposed network distribution structure helps to overcome the possible resource disruption that occurs with single sourcing in the centralized structure, resulting decrease of the demand shortage. Sensitivity of the model with different pre-disaster and post-disaster purchasing conditions at SPs are also discussed in order to represent realistic disaster scenarios. (Acknowledgement: Qatar/QNRF/NPRP Project: 5-200-5-027)qscienc

Locations of Temporary Distribution Facilities for Emergency Response Planning

Resource planning in emergency response phase is challenging primarily because the resources have to be delivered to the affected regions in a timely manner and in right quantities. Disasters such as the hurricanes, epidemics and chemical explosions in general impact large regions and emergency supplies are needed for several days. Demand for the resources in one location at a period may not exist in the next period; or, a particular location may have a very high demand in the subsequent period. This dynamic change in the demand patterns adds further challenges in the planning process. The change in demand both in terms of the location and the quantity is usually tackled through allocation of resources at the prepositioned facilities. However, prepositioned facilities may be small in numbers and distribution of resources to the affected area may require additional funds for transportation and other overhead costs. In such a case, distribution of resources through a number of temporary facilities, located near the demand centers can significantly improve the distribution process thereby decreasing the supply response time. Therefore, in this paper, we propose a network flow model for emergency response planning which provides location and allocation plans of temporary distribution facilities for short distribution periods in the planning horizon. We assume that the individual demands in close vicinity are grouped at so-called aggregated demand points (ADPs). The distribution process initiates from a central supply point (CSP) which is a collection point that continuously acquires the resources and prepares them for distribution. In each distribution period, resource available at the CSP is allocated to the temporary distribution centers (TDCs) to distribute to the ADPs. The model considers periodically changing demands at the ADPs and supply availability at the CSP. Therefore, the decision on location and allocation are the dynamic decisions carried out in each distribution period, and the TDCs located in a period are functional temporarily only for the period. The model allows delayed satisfaction of demand when resources in a planning period are insufficient, and allows transfer of excess resources from a relief facility to another in the next time period. The consideration of the dynamic decision, transfer of excess resources and provision of delayed satisfaction of demand make the proposed model unique and more representative to the actual relief distribution. The objective is to minimize the total social cost which is sum of the logistics and the deprivation costs of all distribution periods. The logistics cost consists of the fixed set up costs and the transportation costs. The deprivation cost is the penalty cost associated with the delayed satisfaction of the supplies. The model is tested in a network for numerical analysis. The analysis shows that the location of TDCs in a time period influences the total cost of response. The results show that relief response can be more effective if movement of excess resources from one period to next is allowed. When such a movement is not allowed, it can increase shortage cost and eventually the total cost of emergency response. The analysis also shows solvability of the model in large and complex problem instances within a short computation time which shows the models' robustness and applicability to solve practical size distribution problems.qscienc

Characterization and analysis of metropolitan freight patterns in Medellin, Colombia

This paper seeks to pilot test a novel way to collect freight and service activity data and analyze the collected data in the metropolitan area of Medellin, Colombia. This research collects data using a multi-layer and multi-actor approach that includes surveys to receivers, suppliers, carriers, and truck drivers. The data are used by the authors to describe the overall freight patterns in the area of study and to show lessons learned. The data collection resulted in 2947 establishments (4.4% of the total establishments in the city), a cordon survey of 2950 commercial vehicles (17% of the total vehicle volume) accessing the urban area, and carrier interviews to ten companies and 130 truck drivers. The results indicate that a total of 33,274 metric tons/day enter the study area, 35,240 tons/day leave the area; while 7000 tons/day are distributed in the study area. In terms of freight trips, 6600 trips/day enter the study zone and 6600 trips/day leave it. The data collection effort enabled the analyses of freight generation patterns. The freight surveys used in the study complement each other, and provided a good depiction of the freight movements in urban areas. It was found that in the Medellin Metropolitan Area, freight-intensive sector establishments generates, on average, significantly more cargo (freight attraction plus production) than the service-intensive sectors. The analyses of the surveys allow the decision makers to understand the nature of the cargo and the generation patterns in different type of establishments. This characterization of the freight patterns is vital for the forecasting of the behavior of the cargo and it is the main input to perform freight demand modeling for city planning, especially for developing countries, where there are too many budget constraints

Direct impacts of off-hour deliveries on urban freight emissions

The most significant negative environmental impacts of urban trucking result largely from travel in congested traffic. To illustrate the potential of innovative solutions to this problem, this paper presents new research on the emission reductions associated with off-hour freight deliveries (OHD). The paper uses fine-level GPS data of delivery operations during regular-hours (6 AM to 7 PM), and off-hours (7 PM to 6 AM), to quantify emissions in three major cities in the Americas. Using second-by-second emissions modeling, the paper compares emissions under both delivery schedules for: reactive organic gases, total organic gases, carbon monoxide, carbon dioxide, oxides of nitrogen, and particulate matter. The results show that the magnitude of the emission reductions depends on the extent of the change of delivery time. In the case of the “Full” OHD programs of New York City and São Paulo—where the deliveries were made during the late night and early morning periods (7 PM to 6 AM)—the emission reductions are in the range of 45–67%. In the case of the “Partial” OHD used in Bogotá (where OHD took place between 6 PM and 10 PM), the reductions were about 13%. The emission reductions per kilometer are used to estimate the total reductions for the cities studied, and for all metropolitan areas in the world with more than two million residents. The results indicate the considerable potential of OHD as an effective—business friendly—sustainability tool to improve the environmental performance of urban deliveries. The chief implication is that public policy should foster off-hour deliveries, and all forms of Freight Demand Management, where practicable

Optimal pricing for priority service and space allocation in container ports

This paper focuses on the determination of optimal space allocation and optimal pricing for priority systems in container ports. The problem is formulated taking into account the intrinsic and logistic cargo value, and a capacity constraint that considers the various physical requirements of the containers. Prices and space allocations are found for various cases, showing explicitly the role of each element. The resulting models extend classical price differentiation theory, i.e. the inverse elasticity rule, in various directions. Finally, the implications of these results and the corresponding information requirements are clearly established.