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Increased transportation costs and tense competition necessitate that carriers coordinate with the other members of the supply chain for realizing potential savings due to channel coordination. We address such coordination issues in an operational setting where a carrier is to ship the items within a two-echelon supply channel consisting of a supplier (e.g., manufacturer) and a buyer (e.g., retailer) operating under the assumptions of the traditional economic order quantity (EOQ) problem setting. We consider the case where the supplier-buyer pair is operated in a centralized fashion, potentially under a Vendor-Managed Inventory (VMI) agreement, and, hence, referred to as the supplier-buyer partnership, whereas the carrier is a separate entity. We examine the optimal solutions of the parties under various freight schedules, i.e., transportation pricing mechanisms, including freight discounts, fixed charges for each shipment, and fixed charges for each truck used in the delivery. Our goal is to identify alternative transportation pricing contracts serving the carrier and supplier-buyer partnership from the carrier’s perspective and coordinating the supply chain system at hand. We find that freight discounts and per shipment fixed charges may not offer an effective contractual mechanism, and sometimes it is better for the carrier to offer negative discounts or fixed rewards per shipment. We also show that asking for a fixed charge per truck would always motivate the supplier-buyer partnership to operate at the centralized solution if the carrier sets the fixed charge per truck equal the fixed cost per truck reflecting its own operating costs in the asking price and achieving positive profits through the additional per unit freight rate. In an extensive numerical study, we benchmark the cost efficiency of centralized versus decentralized channels and illustrate that the benefits of the three transportation pricing mechanisms considered in this paper.

We model a disaster preparedness problem for the supply of a commodity.We consider an integrated pre- and post-disaster preparedness planning model.The use of multiple supply points reduces supply deprivations at the demand points.Deprivation cost structure significantly affect the proportion of pre- and post-disaster allocations. In this paper, a three-echelon network model is proposed for integrated emergency preparedness and response planning for the distribution of emergency supplies. The model minimizes the social cost to identify a set of potential supply points (SPs) at the highest echelon, where supply items are consolidated and sent to the prepositioning facilities. The sum of logistics and deprivation costs incurred by the population due to the lack of access to goods or services, is considered as the social cost in this model. The deprivation cost is assumed to increase exponentially with the deprivation time. The model also considers pre-disaster and post-disaster purchasing decisions at the SPs, and allows direct shipments from SPs and prepositioned facilities to the demand points. Numerical analysis shows that multiple supply sources can ensure efficient distribution of the supplies and reduce the deprivation costs. The results also indicate that partial prepositioning and post-disaster purchasing can reduce the shortage in emergency supplies.

In order to compare the performances of alternative shipment consolidation policies, we develop closed-form expressions of the distribution of the maximum waiting time and the average order delay. We examine the tradeoff between the expected delivery frequency, as measured by the expected shipment consolidation cycle length, and the average order delay. The previous analytical results regarding shipment consolidation are aimed at optimizing the performances of alternative policies using cost-based criteria whereas our results are useful under service-based criteria.

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.AcknowledgementThis research was made possible by a NPRP award NPRP 5 - 200 - 5 – 027 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.

We examine a joint inventory replenishment and shipment scheduling problem that arises in the context of a vendor-managed inventory (VMI) arrangement. Since a temporal shipment consolidation policy is being implemented, the inventory requirements at the vendor are affected by the timing and quantity of shipment release. The vendor’s problem is to determine an integrated policy for inventory replenishment and shipment release and to set its parameters. We develop analytical models for computing such integrated policies where it is economical to use common carriage for outbound transportation. We propose algorithmic approaches to set the optimal policy parameter values.

The logistics literature reports that three different types of shipment consolidation policies are popular in current practice. These are time-based, quantity-based and Time-and-Quantity (TQ)-based consolidation policies. Although time-based and quantity-based policies have been studied via analytical modeling, to the best of the authors knowledge, there is no exact analytical model for computing the optimal TQ-based policy parameters. Considering the case of stochastic demand/order arrivals, an analytical model for computing the expected long-run average cost of a consolidation system implementing a TQ-based policy is developed. The cost expression is used to analyze the optimal TQ-based policy parameters. The presented analytical results prove that: (i) the optimal TQ-based policy outperforms the optimal time-based policy; and (ii) the optimal quantity-based policy is superior to the other two (i.e., optimal time-based and TQ-based) policies in terms of cost. Considering the expected maximum waiting tim...

This paper investigates the impact of alternative outbound dispatch policies on integrated stock replenishment and transportation decisions. The logistics literature reports that two different types of such policies are popular in current practice. These are time-based and quantity-based dispatch policies. Considering the case of stochastic demand, the paper presents analytical and numerical results showing that the cost savings obtained through quantity-based policies can be substantial. However, under a quantity-based policy, a specific delivery time cannot be quoted when the customer places an order. Hence, the paper also investigates the cost and customer waiting time implications of hybrid policies and demonstrates that hybrid policies are superior to time-based policies in terms of the resulting costs. Furthermore, although hybrid policies are not superior to quantity-based policies in terms of the resulting costs, they are superior in terms of a service measure which is quantified by the long-run average cumulative waiting time.

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) qscience

This project aims at identifying and validating the performance measurements of container logistics at Qatalum Smelter. The project investigates two different cases that Qatalum has implemented to manage the empty container logistics using simulation. The first case is to keep a limited empty container buffer; and the second case is to provide empty containers in a just-in-time fashion. Accordingly, a field research was conducted at Qatalum to collect required data. Quantitative and informative data were collected though meetings, on-site observations, and time and motion measurements to develop spaghetti diagrams that show all possible flows and flow charts of container logistics in order to develop the simulation models. Both situations were simulated using Arena software. Optimal buffer size of empty containers inside the cast house was determined, in which different empty containers buffer sizes were tried to choose the optimum buffer size. The vehicle fleet utilization in both cases was determined and scenario testing was implemented, in which the effect of adding additional resources was observed. The results show that the optimal buffer size of empty containers inside the cast house is 14 containers. The vehicle fleet utilization in the current case is less than its utilization in sand case and the most utilized vehicles in both cases are trucks and the container mover. The impact of additional resources is also studied via simulation.

Natural gas is distributed in the form of liquefied natural gas (LNG) when transported across long distances. Delivery planning of LNG offers challenging multi-origin vehicle routing problems, with vessels of various capacities and fuel performance. There are also unique challenges due to the nature of LNG trading and shipping. We propose a framework design for this planning problem by considering the multiple objectives and unique characteristics of the problem to develop efficient solutions. We aim to study the planning problems at various levels by using mixed-integer programming solution techniques, heuristics, and meta-heuristics. A critical analysis of the existing literature for similar problems was conducted and the formulations for the LNG delivery problems of Qatar are presented. The LNG inventory and routing problems are fairly complicated problems due to their unique features. The formulations developed are an attempt at solving the large LNG distribution challenges in the case of Qatargas and Rasgas in Qatar.