Addressing the Challenges of Uncertainty Affecting Last-Mile Distribution in Disaster Relief

The study of Disaster Relief has received increasing attention for the better part of 20 years, and particularly in the wake of high-visibility storms like Hurricanes Harvey and Irma, there is little need to provide justification for the field as an area of interest. This presentation will summarize an ongoing effort to study one particular aspect of Disaster Relief, namely last-mile distribution in the face of uncertain supply. This body of work forms the bulk of my dissertation which I completed last year along with my co-author and mentor Dr. Emmett Lodree, a full Professor at the University of Alabama

Optimal Control of Parallel Queues for Managing Volunteer Convergence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163497/2/poms13224.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163497/1/poms13224_am.pd

Pre-storm emergency supplies inventory planning

PurposePlanning inventories for emergency supplies such as bottled water, non‐perishable foods, batteries, and flashlights can be challenging for retailers situated within the projected path of a severe storm. The retailer's inventory decisions are complicated by the inherent volatility of storm forecasts and the corresponding demand predictions. The purpose of this paper is to explore both proactive and reactive inventory control policies within the context of probable pre‐storm demand surge for a fast‐moving emergency supply item, and identify the conditions that are most conducive to each strategy according to the minimax decision criterion.Design/methodology/approachThe inventory system is formulated based on an underlying economic order quantity framework. Minimax decision rules are developed analytically. Sensitivity analysis is facilitated by both analytic and numerical methods.FindingsThe conditions that are conducive to a proactive ordering strategy are limited supplier flexibility, acute demand surge, and exorbitant reorder costs; otherwise, the minimax inventory control policy is given by a reactive ordering strategy.Research limitations/implicationsThe above‐mentioned findings are based on a stylized inventory model characterized by assumptions that are consistent with the academic literature. In order to assess the implications of these results in practice, the model should be extended according to the relevance of each assumption to specific real‐world inventory systems.Social implicationsHouseholders preparing for probable evacuation or post‐storm power outages typically overwhelm grocery and home improvement stores during a brief period prior to the impact of an approaching weather system. This phenomenon triggers a temporary spike in demand for several stock keeping units, which is oftentimes accompanied by pervasive inventory shortages that proliferate community vulnerability and engender a sense of disarray throughout the local populace. Effective inventory management of emergency supply items during this period can help alleviate some of these social dilemmas.Originality/valueFew academic publications address inventory management from the perspective of humanitarian relief. Among existing studies, the emphasis has been coordination of emergency supplies for post‐disaster relief and recovery activities. This paper appears to be the first academic investigation of an inventory system driven by the pre‐storm demand surge for emergency supplies that typically occurs in the presence of an ominous and potentially devastating weather system. Additionally, this study conceivably represents the first minimax distribution free approach to inventory control within the context of humanitarian logistics and disruption management.</jats:sec

A Note On The Optimal Sequence Position For A Rate-Modifying Activity Under Simple Linear Deterioration

This paper addresses the integration of two emerging classes of scheduling problems which, for the most part, have evolved independently. These problem classes are (i) scheduling problems with time-dependent processing times and (ii) scheduling problems with rate-modifying activities (RMAs). The integration of these two concepts is motivated by human operators who experience fatigue while carrying out tasks and take rest breaks for recovery, but is also applicable to machines that experience performance degradation over time and require maintenance in order to sustain acceptable production rates. We explore a sequence-independent, single processor makespan problem with position-dependent processing times and prove that under certain conditions, the optimal policy is to schedule the RMA in the middle of the task sequence. © 2009 Elsevier B.V

Staff assignment policies for a mass casualty event queuing network

We study parallel queuing systems in which heterogeneous teams collaborate toserve queues with three different prioritization levels in the context of a mass casualty event.We assume that the health condition of casualties deteriorate as time passes and aim tominimize total deprivation cost in the system. Servers (i.e. doctors and nurses) have randomarrival rates and they are assigned to a queue as soon as they arrive. While nurses and doctorsserve their dedicated queues, collaborative teams of doctors and nurses serve a third type ofcustomer, the patients in critical condition. We model this queueing network with flexibleresources as a discrete-time finite horizon stochastic dynamic programming problem anddevelop heuristic policies for it. Our results indicate that the standard cμ rule is not anoptimal policy, and that the most effective heuristic policy found in our simulation study isintuitive and has a simple structure: assign doctor/nurse teams to clear the critical patientqueue with a buffer of extra teams to anticipate future critical patients, and allocate theremaining servers among the other two queues

Inventory decisions for emergency supplies based on hurricane count predictions

This paper addresses a stochastic inventory control problem for manufacturing and retail firms who face challenging procurement and production decisions associated with hurricane seasons. Specifically, the paper presents a control policy in which stocking decisions are based on a hurricane forecast model that predicts the number of landfall hurricanes for an ensuing hurricane season. The multi-period inventory control problem is formulated as a stochastic programming model with recourse where demand during each pre-hurricane season period is represented as a convolution of the current period's demand and an updated estimate of demand for the ensuing hurricane season. Due to the computational challenges associated with solving stochastic programming problems, recent scenario reduction techniques are discussed and illustrated through an example problem. The proposed model specifies cost minimizing inventory strategies for simultaneously meeting stochastic demands that occur prior to the hurricane season while proactively preparing for potential demand surge during the season.Disaster relief planning Humanitarian logistics Supply chain management Stochastic programming Markov chain Bayesian regression

Modeling customer impatience in a newsboy problem with time-sensitive shortages

Customers across all stages of the supply chain often respond negatively to inventory shortages. One approach to modeling customer responses to shortages in the inventory control literature is time-dependent partial backlogging. Partial backlogging refers to the case in which a customer will backorder shortages with some probability, or will otherwise solicit the supplier's competitors to fulfill outstanding shortages. If the backorder rate (i.e., the probability that a customer elects to backorder shortages) is assumed to be dependent on the supplier's backorder replenishment lead-time, then shortages are said to be represented as time-dependent partial backlogging. This paper explores various backorder rate functions in a single period stochastic inventory problem in an effort to characterize a diversity of customer responses to shortages. We use concepts from utility theory to formally classify customers in terms of their willingness to wait for the supplier to replenish shortages. Under mild assumptions, we verify the existence of a unique optimal solution that corresponds to each customer type. Sensitivity analysis experiments are conducted in order to compare the optimal actions associated with each customer type under a variety of conditions. Additionally, we introduce the notion of expected value of customer patience information (EVCPI), and then conduct additional sensitivity analyses to determine the most and least opportune conditions for distinguishing between customer behaviors.Inventory control Customer responsiveness Time-dependent partial backlogging Demand uncertainty Utility theory