This dissertation aims to provide models which will help companies make sustainable logistics management and transportation decisions. These models are extensions of the economic lot sizing model with the availability of multiple replenishment modes. The objective of the models is to minimize total replenishment costs and emissions. The study provides applications of these models on contemporary supply chain problems. Initially, the impact of carbon regulatory mechanisms on the replenishment decisions are analyzed for a biomass supply chain under fixed charge replenishment costs. Then, models are extended to consider multiple-setups replenishment costs for age dependent perishable products. For a cost minimization objective, solution algorithms are proposed to solve cases where one, two or multiple replenishment modes are available. Finally, using a bi-objective model, tradeoffs in costs and emissions are analyzed in a perishable product supply chain.

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<p>Many types of services are provided using some equipment or machines, e.g. transportation systems using vehicles. Designs of these systems require capacity decisions, e.g., the number of vehicles. In my dissertation, I use many-server and conventional heavy-traffic limit theory to derive asymptotically optimal capacity decisions, giving the desired level of delay and availability performance with minimum investment. The results provide near-optimal solutions and insights to otherwise analytically intractable problems.</p> <p>The dissertation will comprise two essays. In the first essay, &ldquoAsymptotic Analysis of Delay-based Performance Metrics and Optimal Capacity Decisions for the Machine Repair Problem with Spares,&rdquo I study the M/M/R machine repair problem with spares. This system can be represented by a closed queuing network. Applications include fleet vehicles' repair and backup capacity, warranty services' staffing and spare items investment decisions. For these types of systems, customer satisfaction is essential; thus, the delays until replacements of broken units are even more important than delays until the repair initiations of the units. Moreover, the service contract may include conditions on not only the fill rate but also the probability of acceptable delay (delay being less than a specified threshold value).</p> <p>I address these concerns by expressing delays in terms of the broken-machines process; scaling this process by the number of required operating machines (or the number of customers in the system); and using many-server limit theorems (limits taken as the number of customers goes to infinity) to obtain the limiting expected delay and probability of acceptable delay for both delay until replacement and repair initiation. These results lead to an approximate optimization problem to decide on the repair and backup-capacity investment giving the minimum expected cost rate, subject to a constraint on the acceptable delay probability. Using the characteristics of the scaled broken-machines process, we obtain insights about the relationship between quality of service and capacity decisions. Inspired by the call-center literature, we categorize capacity level choice as efficiency-driven, quality-driven, or quality- and efficiency-driven. Hence, our study extends the conventional call center staffing problem to joint staffing and backup provisioning. Moreover, to our knowledge, the machine-repair problem literature has focused mainly on mean and fill rate measures of performance for steady-state cost analysis. This approach provides complex, nonlinear expressions not possible to solve analytically. The contribution of this essay to the machine-repair literature is the construction of delay-distribution approximations and a near-optimal analytical solution. Among the interesting results, we find that for capacity levels leading to very high utilization of both spares and repair capacity, the limiting distribution of delay until replacement depends on one type of resource only, the repair capacity investment.</p> <p>In the second essay, &ldquoDiffusion Approximations and Near-Optimal Design of a Make-to-Stock Queue with Perishable Goods and Impatient Customers,&rdquo I study a make-to-stock system with perishable inventory and impatient customers as a two-sided queue with abandonment from both sides. This model describes many consumer goods, where not only spoilage but also theft and damage can occur. We will refer to positive jobs as individual products on the shelf and negative jobs as backlogged customers. In this sense, an arriving negative job provides the service to a waiting positive job, and vice versa. Jobs that must wait in queue before potential matching are subject to abandonment. Under certain assumptions on the magnitude of the abandonment rates and the scaled difference between the two arrival rates (products and customers), we suggest approximations to the system dynamics such as average inventory, backorders, and fill rate via conventional heavy traffic limit theory.</p> <p>We find that the approximate limiting queue length distribution is a normalized weighted average of two truncated normal distributions and then extend our results to analyze make-to-stock queues with/without perishability and limiting inventory space by inducing thresholds on the production (positive) side of the queue. Finally, we develop conjectures for the queue-length distribution for a non-Markovian system with general arrival streams. We take production rate as the decision variable and suggest near-optimal solutions.</p> / Dissertation

The traditional air cargo supply chain is composed by the shippers, the freight forwarders and the airlines. The freight forwarders secure capacity with airlines in order to accommodate shippers' demand. They bid for capacity six to twelve months before the actual departure date of the aircraft, and confirm the needed capacity a few days before departure. We address the freight forwarders' problem of confirming needed capacity based on balancing the costs of ordering too much capacity versus ordering too little. We use a Markov decision process to model the problem. We show the value function is convex in the state variables for lead times of one and two periods. We present the structure of the optimal policy and show it is stationary. In addition we present solutions to the case with subcontracting options and order due dates. We also address the airlines' revenue management problem with respect to its cargo capacity available for free sale (after honoring committed capacity to freight forwarders), in particular the problems of (1) accepting/rejecting incoming bookings based on bid prices, and of (2) estimating the show-up rate (ratio of bookings handed in at departure over bookings on hand) with impact on overbooking. To address the lumpiness of demand, we split the cargo into two categories: small cargo, composed of mail and small packages, and large cargo, composed of the bulk of commercial cargo. The small cargo is approximated with the passenger arrival, and we propose a new algorithm to solve the traditional probabilistic nonlinear problem from the passenger side. The large cargo is solved using a dynamic program, which is decomposed at the leg level using a fare-prorating scheme. The solution from our new approach is shown via simulation to be superior to two approaches currently used: the first come first serve, and the deterministic linear program. The show-up rate is estimated using wavelets and we show that a discrete show-up rate is more suitable than the traditional Normal estimator used in practice. The new estimator results in considerable more potential revenue.

abstract: The use of Red Blood Cells (RBCs) is a pillar of modern health care. Annually, the lives of hundreds of thousands of patients are saved through ready access to safe, fresh, blood-type compatible RBCs. Worldwide, hospitals have the common goal to better utilize available blood units by maximizing patients served and reducing blood wastage. Managing blood is challenging because blood is perishable, its supply is stochastic and its demand pattern is highly uncertain. Additionally, RBCs are typed and patient compatibility is required. This research focuses on improving blood inventory management at the hospital level. It explores the importance of hospital characteristics, such as demand rate and blood-type distribution in supply and demand, for improving RBC inventory management. Available inventory models make simplifying assumptions; they tend to be general and do not utilize available data that could improve blood delivery. This dissertation develops useful and realistic models that incorporate data characterizing the hospital inventory position, distribution of blood types of donors and the population being served. The dissertation contributions can be grouped into three areas. First, simulations are used to characterize the benefits of demand forecasting. In addition to forecast accuracy, it shows that characteristics such as forecast horizon, the age of replenishment units, and the percentage of demand that is forecastable influence the benefits resulting from demand variability reduction. Second, it develops Markov decision models for improved allocation policies under emergency conditions, where only the units on the shelf are available for dispensing. In this situation the RBC perishability has no impact due to the short timeline for decision making. Improved location-specific policies are demonstrated via simulation models for two emergency event types: mass casualty events and pandemic influenza. Third, improved allocation policies under normal conditions are found using Markov decision models that incorporate temporal dynamics. In this case, hospitals receive replenishment and units age and outdate. The models are solved using Approximate Dynamic Programming with model-free approximate policy iteration, using machine learning algorithms to approximate value or policy functions. These are the first stock- and age-dependent allocation policies that engage substitution between blood type groups to improve inventory performance. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

Title: Perishable Inventory Management Solutions and Challenges of Kosovo FFRs. Avoiding Product Expiration at Retails ShelvesPurpose: In this thesis perishable inventory management solutions and challenges at Kosovo FFRs have been studied and identified. Hence, after identifying PIM solutions and challenges the research suggests ideas that will contribute to avoid the expiration of perishable products if selling them takes more time than their actual shelf life. This contributes to minimizing food waste in food supply chains and fresh food retailers. Methodology: Thesis consist of qualitative methods where multiple case studies in cooperation with Kosovo FFRs have been performed. Data collection methods included semi structured interviews, site visits and some financial data accessed from annual and government reports. Theory: Theoretical chapter has been developed from preexisting theory on perishable inventory management. Five phases of fresh food retailing inventory management have been developed and used as the basis for practical research. Moreover, part two of the theoretical chapter talks about the perishable inventory management challenges and is the basis for the second research question. Findings: The findings have shown that Kosovo FFRs use a mixture of PIM solutions with a focus on shelf life and replenishment solutions. The study also revealed that Kosovo FFRs are outdated regarding to product identification and software solutions, however, manage to perform somehow satisfactorily. Consequently, because of the lack of contemporary identification technologies Kosovo FFRs PIM challenges where found to be related to data accuracy and real time data access.