Stochastic choice-based discrete planning is a broad class of decision-making problems characterized by a sequential decision-making process involving a planner and a group of customers. The firm or planner first decides a subset of options to offer to the customers who, in turn, make selections based on their utilities of those options. This problem has extensive applications in many areas, including assortment planning, product line design, and facility location. A key feature of these problems is that the firm cannot fully observe the customers’ utilities or preferences, which results from intrinsic and idiosyncratic uncertainties. Most works in the literature have studied a specific type of uncertainty, resulting in customized decision models that are subsequently tackled using ad hoc algorithms designed to exploit the specific model structure. In this paper, we propose a modeling framework capable of solving this family of sequential problems that works for a large variety of uncertainties. We then leverage an approximation scheme and develop an adaptable mixed-integer linear programming method. To speed up the solution process, we further develop an efficient decomposition approach. We show that our solution framework can yield solutions proven to be (near-)optimal for a broad class of problems. We illustrate this by applying our approach to three classical application problems: constrained assortment optimization and two facility location problems. Through extensive computational experiments, we demonstrate the performance of our approach in terms of both solution quality and computational speed, and we provide computational insights. In particular, when we use our method to solve the constrained assortment optimization problem under the exponomial choice model, it improves the state of the art. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Y. H. Lin was supported by the National Natural Science Foundation of China [Grant 72288101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0694 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0694 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The assortment problem in revenue management is the problem of deciding which subset of products to offer to consumers in order to maximise revenue. A simple and natural strategy is to select the best assortment out of all those that are constructed by fixing a threshold revenue π and then choosing all products with revenue at least π. This is known as the revenue-ordered assortments strategy. Our first contribution is an analysis of the performance of the revenue-ordered assortments strategy making only minimal assumptions about the underlying discrete choice model: We assume that consumers behave rationally, in the sense that the probability of choosing a specific product x ∈ S when given a choice set S cannot increase if S is enlarged. This rationality assumption, known as regularity, is satisfied by almost all models studied in the revenue management and choice theory literature. This includes in particular all random utility models, as well as other models introduced recently such as the additive perturbed utility model, the hitting fuzzy attention model, and models obtained using a non-additive random utility function. We provide three types of revenue guarantees for revenue-ordered assortments: If there are k distinct revenues r1, r2, ..., rk associated with the products (listed in increasing order), then revenue-ordered assortments approximate the optimum revenue to within a factor of (A) 1/k; (B) 1/(1 + ln(rk/r1)), and (C) 1/(1 + ln υ), where υ is defined with respect to an optimal assortment S* as the ratio between the probability of just buying a product and that of buying a product with highest revenue in S*. These three guarantees are in general incomparable, that is, (A), (B), or (C) can be the largest depending on the instance. We also show that the three bounds (A), (B), and (C) are exactly tight, in the sense that none of the bounds remains true if multiplied by a factor (1+ ε) for any ε > 0.

Unpredictability is often portrayed as an undesirable outcome of social influence in cultural markets. Unpredictability stems from the "rich get richer" effect, whereby small fluctuations in the market share or popularity of products are amplified over time by social influence. In this paper, we report results of an experimental study that shows that unpredictability is not an inherent property of social influence. We investigate strategies for creating markets in which the popularity of products is better-and more predictably-aligned with their underlying quality. For our study, we created a cultural market of science stories and conducted randomized experiments on different policies for presenting the stories to study participants. Specifically, we varied how the stories were ranked, and whether or not participants were shown the ratings these stories received from others. We present a policy that leverages social influence and product positioning to help distinguish the product's market share (popularity) from underlying quality. Highlighting products with the highest estimated quality reduces the "rich get richer" effect highlighting popular products. We show that this policy allows us to more robustly and predictably identify high quality products and promote blockbusters. The policy can be used to create more efficient online cultural markets with a better allocation of resources to products.

This paper introduces a hybrid algorithm for the dynamic dial-a-ride problem in which service requests arrive in real time. The hybrid algorithm combines an exact constraint programming algorithm and a tabu search heuristic. An important component of the tabu search heuristic consists of three scheduling procedures that are executed sequentially. Experiments show that the constraint programming algorithm is sometimes able to accept or reject incoming requests, and that the hybrid method outperforms each of the two algorithms when they are executed alone.