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We review and develop different tractable approximations to individual chance-constrained problems in robust optimization on a variety of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance-constrained problems and provide a new formulation that improves upon the standard approach. Our approach builds on a classical worst-case bound for order statistics problems and is applicable even if the constraints are correlated. We provide an application of the model on a network resource allocation problem with uncertain demand.

In this paper, we study the properties of a recently proposed class of semiparametric discrete choice models (referred to as the marginal distribution model (MDM)), by optimizing over a family of joint error distributions with prescribed marginal distributions. Surprisingly, the choice probabilities arising from the family of generalized extreme value models of which the multinomial logit model is a special case can be obtained from this approach, despite the difference in assumptions on the underlying probability distributions. We use this connection to develop flexible and general choice models to incorporate consumer and product level heterogeneity in both partworths and scale parameters in the choice model. Furthermore, the extremal distributions obtained from the MDM can be used to approximate the Fisher's information matrix to obtain reliable standard error estimates of the partworth parameters, without having to bootstrap the method. We use simulated and empirical data sets to test the performance of this approach. We evaluate the performance against the classical multinomial logit, mixed logit, and a machine learning approach that accounts for partworth heterogeneity. Our numerical results indicate that MDM provides a practical semiparametric alternative to choice modeling. This paper was accepted by Eric Bradlow, special issue on business analytics .

In recent years, supply chains are more prone to disruptions. The impact on performance depends on the system's ability to discover and then recover after the disruption has occurred. In this paper, we proposed a new method to integrate probabilistic assessment of disruption risks into the Risk Exposure Index (REI) approach proposed previously by Simchi-Levi et al. and measure supply chain resiliency by analyzing the worst-case CVaR (WCVaR) of total lost sales under disruptions. We show that the optimal strategic inventory positioning strategy in this model can be fully characterized by a conic program. The optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. This information can help supply chain risk managers focus their mitigation efforts on critical suppliers and/or installations that will have greater impact on the performance of the supply chain when disrupted. A novel approach has been proposed in the literature using the time-to-recover (TTR) parameters to analyze the risk-exposure index (REI) of supply chains under disruption. This approach is able to capture the cascading effects of disruptions in the supply chains, albeit in simplified environments; TTRs are deterministic, and at most, one node in the supply chain can be disrupted. In this paper, we propose a new method to integrate probabilistic assessment of disruption risks into the REI approach and measure supply chain resiliency by analyzing the worst-case conditional value at risk of total lost sales under disruptions. We show that the optimal strategic inventory positioning strategy in this model can be fully characterized by a conic program. We identify appropriate cuts that can be added to the formulation to ensure zero duality gap in the conic program. In this way, the optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. This information can help supply chain risk managers focus their mitigation efforts on critical suppliers and/or installations that will have a greater impact on the performance of the supply chain when disrupted. The e-companion is available at https://doi.org/10.1287/opre.2018.1776 .

One of the foremost planning problems in container transshipment operation concerns the allocation of home berth (preferred berthing location) to a set of vessels scheduled to call at the terminal on a weekly basis. The home berth location is subsequently used as a key input to yard storage, personnel, and equipment deployment planning. For instance, the yard planners use the home berth template to plan for the storage locations of transshipment containers within the terminal. These decisions (yard storage plan) are in turn used as inputs in actual berthing operations, when the vessels call at the terminal. In this paper, we study the economical impact of the home berth template design problem on container terminal operations. In particular, we show that it involves a delicate trade-off between the service (waiting time for vessels) and cost (movement of containers between berth and yard) dimension of operations in the terminal. The problem is further exacerbated by the fact that the actual arrival time of the vessels often deviates from the scheduled arrival time, resulting in last-minute scrambling and change of plans in the terminal operations. Practitioners on the ground deal with this issue by building (capacity) buffers in the operational plan and to scramble for additional resources if needs be. We propose a framework to address the home berth design problem. We model this as a rectangle packing problem on a cylinder and use a sequence pair based simulated annealing algorithm to solve the problem. The sequence pair approach allows us to optimize over a large class of packing efficiently and decomposes the home berth problem with data uncertainty into two smaller subproblems that can be readily handled using techniques from stochastic project scheduling. To evaluate the quality of a template, we use a dynamic berth allocation package developed recently by Dai et al. (unpublished manuscript, 2004) to obtain various berthing statistics associated with the template. Extensive computational results show that the proposed model is able to construct efficient and robust template for transshipment hub operations. [PUBLICATION ABSTRACT]

This paper studies how to schedule medical appointments with time-dependent patient no-show behavior and random service times. The problem is motivated by our studies of independent datasets from countries in two continents that unanimously identify a significant time-of-day effect on patient show-up probabilities. We deploy a distributionally robust model, which minimizes the worst-case total expected costs of patient waiting and service provider’s idling and overtime, by optimizing the scheduled arrival times of patients. This model is challenging because evaluating the total cost for a given schedule involves a linear program with uncertainties present in both the objective function and the right-hand side of the constraints. In addition, the ambiguity set considered contains discrete uncertainties and complementary functional relationships among these uncertainties (namely, patient no-shows and service durations). We show that when patient no-shows are exogenous (i.e., time-independent), the problem can be reformulated as a copositive program and then be approximated by semidefinite programs. When patient no-shows are endogenous on time (and hence on the schedule), the problem becomes a bilinear copositive program. We construct a set of dual prices to guide the search for a good schedule and use the technique iteratively to obtain a near-optimal solution. Our computational studies reveal a significant reduction in total expected cost by taking into account the time-of-day variation in patient show-up probabilities as opposed to ignoring it. This paper was accepted by David Simchi-Levi, optimization.

The concept of chaining, or in more general terms, sparse process structure, has been extremely influential in the process flexibility area, with many large automakers already making this the cornerstone of their business strategies to remain competitive in the industry. The effectiveness of the process strategy, using chains or other sparse structures, has been validated in numerous empirical studies. However, to the best of our knowledge, there have been relatively few concrete analytical results on the performance of such strategies vis-á-vis the full flexibility system, especially when the system size is large or when the demand and supply are asymmetrical. This paper is an attempt to bridge this gap. We study the problem from two angles: (1) For the symmetrical system where the (mean) demand and plant capacity are balanced and identical, we utilize the concept of a generalized random walk to evaluate the asymptotic performance of the chaining structure in this environment. We show that a simple chaining structure performs surprisingly well for a variety of realistic demand distributions, even when the system size is large. (2) For the more general problem, we identify a class of conditions under which only a sparse flexible structure is needed so that the expected performance is already within optimality of the full flexibility system. Our approach provides a theoretical justification for the widely held maxim: In many practical situations, adding a small number of links to the process flexibility structure can significantly enhance the ability of the system to match (fixed) production capacity with (random) demand.

We study the stochastic transportation-inventory network design problem involving one supplier and multiple retailers. Each retailer faces some uncertain demand, and safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. Shen et al. (2003) formulated this problem as a set-covering integer-programming model. The pricing problem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization problem. In this paper, we show that by exploiting certain special structures, we can solve the general pricing problem in Shen et al. efficiently. Our approach utilizes the fact that the set of all lines in a two-dimension plane has low VC-dimension. We present computational results on several instances of sizes ranging from 40 to 500 retailers. Our solution technique can be applied to a wide range of other concave cost-minimization problems.

How should decentralized supply chains set the profit sharing terms using minimal information on demand and selling price? We develop a distributionally robust Stackelberg game model to address this question. Our framework uses only the first and second moments of the price and demand attributes, and thus can be implemented using only a parsimonious set of parameters. More specifically, we derive the relationships among the optimal wholesale price set by the supplier, the order decision of the retailer, and the corresponding profit shares of each supply chain partner, based on the information available. Interestingly, in the distributionally robust setting, the correlation between demand and selling price has no bearing on the order decision of the retailer. This allows us to simplify the solution structure of the profit sharing agreement problem dramatically. Moreover, the result can be used to recover the optimal selling price when the mean demand is a linear function of the selling price (cf. Raza 2014) [Raza SA (2014) A distribution free approach to newsvendor problem with pricing. 4OR—A Quart. J. Oper. Res. 12(4):335–358.]. The online appendix is available at https://doi.org/10.1287/opre.2017.1677 .

In this paper we investigate a stochastic appointment-scheduling problem in an outpatient clinic with a single doctor. The number of patients and their sequence of arrivals are fixed, and the scheduling problem is to determine an appointment time for each patient. The service durations of the patients are stochastic, and only the mean and covariance estimates are known. We do not assume any exact distributional form of the service durations, and we solve for distributionally robust schedules that minimize the expectation of the weighted sum of patients' waiting time and the doctor's overtime. We formulate this scheduling problem as a convex conic optimization problem with a tractable semidefinite relaxation. Our model can be extended to handle additional support constraints of the service durations. Using the primal-dual optimality conditions, we prove several interesting structural properties of the optimal schedules. We develop an efficient semidefinite relaxation of the conic program and show that we can still obtain near-optimal solutions on benchmark instances in the existing literature. We apply our approach to develop a practical appointment schedule at an eye clinic that can significantly improve the efficiency of the appointment system in the clinic, compared to an existing schedule.

In this paper, we study the distribution network design problem integrating transportation and infinite horizon multiechelon inventory cost function. We consider the trade-off between inventory cost, direct shipment cost, and facility location cost in such a system. The problem is to determine how many warehouses to set up, where to locate them, how to serve the retailers using these warehouses, and to determine the optimal inventory policies for the warehouses and retailers. The objective is to minimize the total multiechelon inventory, transportation, and facility location costs. To the best of our knowledge, none of the papers in the area of distribution network design has explicitly addressed the issues of the 2-echelon inventory cost function arising from coordination of replenishment activities between the warehouses and the retailers. We structure this problem as a set-partitioning integer-programming model and solve it using column generation. The pricing subproblem that arises from the column generation algorithm gives rise to a new class of the submodular function minimization problem. We show that this pricing subproblem can be solved in O ( n log n ) time, where n is the number of retailers. Computational results show that the moderate size distribution network design problem can be solved efficiently via this approach.