# Browsing by Author "Janakiraman, Ganesh"

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Item Essays in Operations Management(2020-08-19) Feng, Zhichao; Dawande, Milind; Janakiraman, GaneshShow more This dissertation consists of three essays. The first essay considers a learning problem for a mobile-promotion platform. The second essay studies the identification of bottlenecks of processes in the presence of collaboration and multitasking. The third essay considers an assortment planning problem faced by an online retailer. In Chapter 2, we consider a learning problem for a mobile-promotion platform which conducts advertising campaigns for individual advertisers and operates under both supply-side and demand-side uncertainties. The platform fulfills campaigns by procuring impressions from publishers. The relationship between the bid amount and the probability of winning an impression at that bid is initially unknown to the platform, and it learns them on the fly based on the bids placed and the realized outcomes. Each acquired impression is allocated to an ongoing campaign. We propose a bidding and allocation policy and show that our policy is asymptotically tight. In Chapter 3, we study the identification of bottlenecks of processes that involves collaboration and multitasking. Collaboration occurs when at least one activity of the process requires two or more resources simultaneously, and multitasking occurs when at least one resource is needed for two or more activities of the process. We formulate and analyze graph-theoretic optimization problems that determine bottleneck structures of activities and the associated bottleneck sets of resources in deterministic, single-product processes. In Chapter 4, we consider an assortment planning problem faced by an e-tailer. When shopping online, a consumer often searches using keywords, then examines the products displayed on the webpage as a result of the search. The set of products shown by the e-tailer may trigger her interest in a specific product feature, leading the consumer to refine her search and focus only on products with this feature. Taking this into consideration, we study the e-tailer’s optimal product display decision when consumers have the option to refine their search.Show more Item Essays in Operations Management(2021-04-29) Hosseinabadi Farahani, Mehdi; Dawande, Milind; Janakiraman, GaneshShow more This dissertation consists of three main chapters focusing on operational problems in supply chain contracting, online food-ordering services, and agricultural open burning in developing countries. In Chapter 2, we analyze a contract in which a supplier, who is exposed to disruption risk, offers a supply-flexibility contract comprising of a wholesale price and a minimum-delivery fraction ("flexibility" fraction) to a buyer facing random demand. The supplier is allowed to deviate below the order quantity by at most the flexibility fraction. The supplier's regular production is subject to random disruption but she has access to a reliable expedited supply source at a higher marginal cost. We derive the supplier-led optimal contract and show that supply-chain effciency improves relative to the price-only contract. More interestingly, even though the buyer lets the supplier decide how the two share supply risk, profits of both the players increase by the introduction of flexibility into the contract. Further, supply-flexibility may be even more valuable for the buyer compared to the supplier. Interestingly, the flexibility fraction is not monotone in supplier reliability and a more reliable supplier may even prefer to transfer more risk to the buyer. The robustness of these findings is established on two extensions: one where we study a buyer-led contract (i.e., the buyer chooses the flexibility fraction) and the other where the expedited supply option is available to both the supplier and the buyer. In Chapter 3, We study the problem of managing queues in online food-ordering services where customers, who place orders online and pick up at the store, are offered a common quote time, i.e., the promised pick-up time minus the time the order is placed. The objective is to minimize the long-run average expected earliness and tardiness cost incurred by the customers. We introduce the family of static threshold policies for managing such queues. A static threshold policy is one that starts serving the first customer in the queue as soon as the server is free and the time remaining until the promised pick-up time of that customer falls below a fixed threshold. In important technical contributions for establishing the attractiveness of the optimal static threshold policy, we develop three sets of lower bounds on the optimal cost. The first set of lower bounds exploits structural properties of two special static threshold policies, while the second set utilizes the idea of a clairvoyant optimal policy by considering a decision maker who has either full or partial knowledge of the outcomes of future uncertainties. To obtain our third set of lower bounds, we develop bounds on the optimal earliness and tardiness costs by establishing lower and upper bounds on the steady-state waiting time under an optimal policy. The optimal static threshold policy is asymptotically optimal in several cases, including the heavy traffic and the light traffic regimes. We also develop a dynamic threshold policy in which the threshold depends on the queue length. Finally, through a comprehensive numerical study, we demonstrate the excellent performance of both the static and the dynamic threshold policies. In Chapter 4, we study how the government can use information-disclosure policies to minimize agricultural open burning in developing countries. Agricultural open burning, i.e., the practice of burning crop residue in harvested fields to prepare land for sowing a new crop, is well-recognized as a significant contributor to CO2 and black carbon emissions, and longterm climate change. Low-soil-tillage practices using a specific agricultural machine called Happy Seeder, which can sow the new seed without removing the previous crop residue, have emerged as the most effective and profitable alternative to open burning. However, given the limited number of Happy Seeders that the government can supply, and the fact that farmers incur a significant yield loss if they delay sowing the new crop, farmers are often unwilling to wait to be processed by the Happy Seeder and, instead, decide to burn their farms. A Happy Seeder is assigned to process a group of farms in an arbitrary order. The government knows, but does not necessarily disclose, the schedule for the Happy Seeder at the start of the sowing season. Farmers are impatient, in the sense that they incur a disutility per unit of time associated with waiting for the Happy Seeder. If the Happy Seeder processes a farm, then the farmer gains a positive utility. At the beginning of each period, each farmer decides whether to burn her farm or to wait, given the information provided by the government about the Happy Seeder's schedule. We propose a class of information-disclosure policies, which we refer to as threshold policies, that provide no information to the farmers about the schedule until a pre-specified period and then reveal the entire schedule. By obtaining the unique symmetric Markov perfect equilibrium under any threshold policy, we show that the use of an optimal threshold policy can significantly lower the number of farms burnt compared to that under the full-information and no-information disclosure policies.Show more Item Essays in Operations Management(2019-08) Guda, Harish; 0000-0002-3930-0399 (Harish, G); Dawande, Milind; Janakiraman, GaneshShow more This dissertation consists of three main chapters, each focusing on operational problems that arise in surgical suites’ operations, agricultural operations in developing economies, and operations in the on-demand economy, respectively. In Chapter 2, we study the stochastic, single-machine earliness/tardiness problem (SET), with the sequence of processing of the jobs and their due-dates as decisions and the objective of minimizing the sum of the expected earliness and tardiness costs over all the jobs. We show that the Shortest-Variance-First (SVF) rule is optimal under the assumption of dilation ordering of the processing durations. Since convex ordering implies dilation ordering (under finite means), the SVF sequence is also optimal under convex ordering of the processing durations. In Chapter 3, we consider a governmental scheme, viz., the Guaranteed Support Price (GSP) scheme, that several developing countries have adopted to support their farmers and underprivileged population. Through this scheme, the government, operating under a budget, procures a crop from farmers at a guaranteed (and attractive) price, announced ahead of the selling season, and then distributes the procured amount to the underprivileged segment of the population at a subsidized price. We offer analytically-supported insights on fundamental operational decisions of the GSP scheme. In Chapter 4, we explore problems related to workforce management in the emerging on-demand economy. A fundamental challenge for on-demand platforms (e.g., Uber, InstaCart) is to ensure that independent workers, who are not directly under the platform’s control, are available at the right time and locations to serve consumers at short notice. We study the role of surge pricing, worker incentives and information sharing to effectively manage these platforms.Show more Item Essays in Revenue Management(May 2023) Manchiraju, Seetharama Chandrasekhar 1992-; Dawande, Milind; Ryu, Young U.; Janakiraman, Ganesh; Venkataraman, Ashwin; Sohoni, MilindShow more This dissertation consists of three main chapters that develop new techniques for revenue management and also demonstrate novel practical applications. These chapters focus on developing pricing policies when prices of products are restricted to finite price points, analyzing the performance of state-independent policies in network revenue management, and optimizing pricing and capacity decisions in railways. In Chapter 2, we consider a stochastic multiproduct dynamic pricing problem in which the prices of the products are restricted to discrete and finite sets. The focus of this work is to analyze the deterministic variant of this problem (wherein customer-arrival rates are deterministic), which is a key subproblem whose solution can be used to build attractive policies for the stochastic variant. We obtain efficient and effective solutions to the deterministic problem, and prove worst-case optimality gaps for our solutions. Chapter 3 studies the canonical network revenue management problems introduced in (Gallego and Van Ryzin, 1997) and (Liu and Van Ryzin, 2008). For all the state-independent policies developed in the literature for these problems, we show that the optimality gaps scale proportionally to ?k, where k is the scale of demand and supply. In Chapter 4, we study revenue management in railways which distinguishes itself from that in traditional sectors such as airline, hotel, and fashion retail. In railways, capacity is substantially more flexible leading to a genuine necessity for the joint optimization of prices and capacity. Discreteness in capacity and passengers traveling by standing in unreserved coaches are other features unique to railways. Motivated by our work with a major railway company in Japan, we analyze the problem of jointly optimizing pricing and capacity, and develop four asymptotically optimal policies. We demonstrate the attractive performance of our policies on test-suites of instances based on real-world operations of the high-speed “Shinkansen” trains in Japan, and also develop rich insights based on our numerical results.Show more Item Essays on Inventory Management, Capacity Management, and Resource-Sharing Systems(2017-08) Bo, Yang; 0000 0001 1561 8354 (Dawande, MW); Dawande, Milind; Janakiraman, GaneshShow more This dissertation consists of three essays, each focusing on one of the following three important topics in operations management: inventory management, capacity management, and management of resource-sharing systems. These topics are each summarized below. In the first essay, we investigate the following integrality question for inventory control on distribution systems: Given integral demands, does an integral optimal policy exist? We show that integrality holds under deterministic demand, but fails to hold under stochastic demand. In distribution systems with stochastic demand, we identify three factors that influence the gap between integral and real optimal policies: shipping cost variation across time, holding cost difference across stages, and economies of scale. We then obtain a tight worst-case bound for the gap, which captures the impact of all these factors. In the second essay, we investigate the computational complexity of determining the capacity of a process, a fundamental concept in Operations Management. We show that it is hard to calculate process capacity exactly and, furthermore, also hard to efficiently approximate it to within a reasonable factor; e.g., within any constant factor. These results are based on a novel characterization, which we establish, of process capacity that relates it to the fractional chromatic number of an associated graph. We also show that capacity can be efficiently computed for processes for which the collaboration graph is a perfect graph. The third essay addresses an important problem in resource-sharing systems. We study the minimum-scrip rule in such a system: whenever a service request arises, among those who volunteer and are able to provide service, the one with the least number of scrips (also known as coupons) is selected to provide service. Under mild assumptions, we show that everybody in the system being always willing to provide service is a Nash Equilibrium under the minimum-scrip rule. This suggests that the minimum scrip rule can lead to a high level of social welfare.Show more Item Fixed-Dimensional Stochastic Dynamic Programs: An Approximation Scheme and an Inventory Application(INFORMS) Chen, Wei; Dawande, Milind; Janakiraman, GaneshShow more We study fixed-dimensional stochastic dynamic programs in a discrete setting over a finite horizon. Under the primary assumption that the cost-to-go functions are discrete L♮-convex, we propose a pseudo-polynomial time approximation scheme that solves this problem to within an arbitrary prespecified additive error of ε > 0. The proposed approximation algorithm is a generalization of the explicit-enumeration algorithm and offers us full control in the trade-off between accuracy and running time. The main technique we develop for obtaining our scheme is approximation of a fixed-dimensional L♮-convex function on a bounded rectangular set, using only a selected number of points in its domain. Furthermore, we prove that the approximation function preserves L♮-convexity. Finally, to apply the approximate functions in a dynamic program, we bound the error propagation of the approximation. Our approximation scheme is illustrated on a well-known problem in inventory theory, the single-product problem with lost sales and lead times. We demonstrate the practical value of our scheme by implementing our approximation scheme and the explicit-enumeration algorithm on instances of this inventory problem.Show more Item Optimal Procurement Auctions under Multistage Supplier Qualification(INFORMS) Chen, W.; Dawande, Milind W.; Janakiraman, Ganesh; 0000-0001-6956-0856 (Dawande, MW); 0000-0001-7386-4318 (Jamakiraman, G); Dawande, Milind W.; Janakiraman, GaneshShow more We consider a firm that solicits bids from a fixed-sized pool of yet-to-be qualified suppliers for an indivisible contract. The contract can only be awarded to a supplier who passes a multistage qualification process. For each stage of the qualification process, the buyer incurs a fixed testing cost for each supplier she chooses to test. The buyer seeks an optimal mechanism-that is, one that minimizes her total expected cost. Motivated by the buyer's urgency (or the lack of it) of time for completing the qualification process, we obtain optimal mechanisms for two testing environments: (1) simultaneous testing, where in each stage, the buyer selects a subset of those suppliers who have passed all the previous stages and tests them simultaneously; and (2) nonsimultaneous testing, where the simultaneous-testing requirement is not imposed. Under simultaneous testing, the admission policy for selecting suppliers at each stage is based on nonuniformreserve-price thresholds. Under nonsimultaneous testing, too, the admission policy is threshold based, but the selection process is sequential in nature. The relative increase in cost due to the simultaneous-testing requirement is (under a mild condition) monotonically increasing in the number of suppliers, the expected multistage testing cost, and the overall passing probability. We also study the optimal sequencing of the qualification stages and show that the buyer should schedule the stages in increasing order of the ratio of their testing cost to their failing probability. Finally, for the simpler setting of a single-stage qualification process and a single supplier, we study a two-dimensional mechanism design problem where, in addition to cost, the passing probability is also private to the supplier. Here, too, threshold-based admission remains optimal, and the buyer offers either a pooling or a separating contract. Copyright: ©2018 INFORMS.Show more Item Robustness of Order-Up-To Policies in Lost-Sales Inventory Systems(INFORMS) Bijvank, Marco; Huh, Woonghee Tim; Janakiraman, Ganesh; Kang, WanmoShow more We study an inventory system under periodic review when excess demand is lost. It is known (Huh et al. 2009) that the best base-stock policy is asymptotically optimal as the lost-sales penalty cost parameter grows. We now show that this result is robust in the following sense: Consider the base-stock level which is optimal in a backordering system (with a per-unit-per-period backordering cost) in which the backorder cost parameter is a function of the lost-sales parameter in the original system. Then there is a large family of functions (mapping the lost-sales cost parameter to the backorder cost parameter) such that the resulting base-stock policy is asymptotically optimal. We also demonstrate the robustness phenomenon through a second result. We consider the base-stock level which is optimal in a backordering system in which a unit of backorder is charged a penalty cost only once (such a system has been studied by Rosling). We show that this base-stock policy is also asymptotically optimal. Furthermore, we show that a modification suggested by Archibald of this base-stock level also results in an asymptotically optimal policy. Finally, we numerically test the performance of this heuristic policy for a wide spectrum of values for the lost-sales penalty cost parameter and illustrate the superior performance of Archibald's method.Show more Item Robustness of Order-Up-To Policies in Lost-Sales Inventory Systems(INFORMS) Bijvank, Marco; Huh, Woonghee Tim; Janakiraman, Ganesh; Kang, WanmoShow more We study an inventory system under periodic review when excess demand is lost. It is known (Huh et al. 2009) that the best base-stock policy is asymptotically optimal as the lost-sales penalty cost parameter grows. We now show that this result is robust in the following sense: Consider the base-stock level which is optimal in a backordering system (with a per-unit-per-period backordering cost) in which the backorder cost parameter is a function of the lost-sales parameter in the original system. Then there is a large family of functions (mapping the lost-sales cost parameter to the backorder cost parameter) such that the resulting base-stock policy is asymptotically optimal. We also demonstrate the robustness phenomenon through a second result. We consider the base-stock level which is optimal in a backordering system in which a unit of backorder is charged a penalty cost only once (such a system has been studied by Rosling). We show that this base-stock policy is also asymptotically optimal. Furthermore, we show that a modification suggested by Archibald of this base-stock level also results in an asymptotically optimal policy. Finally, we numerically test the performance of this heuristic policy for a wide spectrum of values for the lost-sales penalty cost parameter and illustrate the superior performance of Archibald's method.Show more Item Technical Note - On Optimal Policies for Inventory Systems with Batch OrderingHuh, Woonghee Tim; Janakiraman, GaneshShow more We study a periodically reviewed multiechelon inventory system in series such that order quantities at every stage have to be multiples of a given stage-specific batch size. The batch sizes are nested in the sense that the batch size for every stage is an integer multiple of the batch size for its downstream stage. The problem is that of determining the policy that minimizes the expected discounted sum of costs over a finite horizon. The result is that an echelon 4R1nQ5 policy is optimal when demands are independent across periods or, more generally, Markov-modulated. We also comment on algorithmic implications of our result and on extensions. © 2012 INFORMS.Show more