Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

The Future of Retail Operations

Retailing consists of all the activities associated with the selling of goods to the final consumer. In this article, we review the research on retail operations published in Manufacturing & Service Operations Research (M&SOM) since 1999. We then discuss the current retail landscape and the new research directions it offers, in which M&SOM can play a prominent role

Online Joint Assortment-Inventory Optimization under MNL Choices

We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the realized demands about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effectively balance the exploration and exploitation in the online decision-making of assortment and inventory. Our algorithm builds on a new estimator for the MNL attraction parameters, a novel approach to incentivize exploration by adaptively tuning certain known and unknown parameters, and an optimization oracle to static single-cycle assortment-inventory planning problems with given parameters. We establish a regret upper bound for our algorithm and a lower bound for the online joint assortment-inventory optimization problem, suggesting that our algorithm achieves nearly optimal regret rate, provided that the static optimization oracle is exact. Then we incorporate more practical approximate static optimization oracles into our algorithm, and bound from above the impact of static optimization errors on the regret of our algorithm. At last, we perform numerical studies to demonstrate the effectiveness of our proposed algorithm

A review of choice-based revenue management : theory and methods

Over the last fifteen years, the theory and practice of revenue management has experienced significant developments due to the need to incorporate customer choice behavior. In this paper, we portray these developments by reviewing the key literature on choice-based revenue management, specifically focusing on methodological publications of availability control over the years 2004–2017. For this purpose, we first state the choice-based network revenue management problem by formulating the underlying dynamic program, and structure the review according to its components and the resulting inherent challenges. In particular, we first focus on the demand modeling by giving an overview of popular choice models, discussing their properties, and describing estimation procedures relevant to choice-based revenue management. Second, we elaborate on assortment optimization, which is a fundamental component of the problem. Third, we describe recent developments on tackling the entire control problem. We also discuss the relation to dynamic pricing. Finally, we give directions for future research

Heuristiken im Service Operations Management

This doctoral thesis deals with the application of operation research methods in practice. With two cooperation companies from the service sector (retailing and healthcare), three practice-relevant decision problems are jointly elicited and defined. Subsequently, the planning problems are transferred into mathematical problems and solved with the help of optimal and/or heuristic methods. The status quo of the companies could be significantly improved for all the problems dealt with.Diese Doktorarbeit beschäftigt sich mit der Anwendung von Operation Research Methoden in der Praxis. Mit zwei Kooperationsunternehmen aus dem Dienstleistungssektor (Einzelhandel und Gesundheitswesen) werden drei praxisrelevante Planungsprobleme gemeinsam eruiert und definiert. In weiterer Folge werden die Entscheidungsmodelle in mathematische Probleme transferiert und mit Hilfe von optimalen und/oder heuristischen Verfahren gelöst. Bei allen behandelten Problemstellungen konnte der bei den Unternehmen angetroffene Status Quo signifikant verbessert werden

Aplicación del método de entropía cruzada al problema de optimización dinámica de surtido

This work considers an assortment optimization problem, under capacity constraint and unknown demand, where a retailer offers an assortment and observes the sale of one of the products according to a multinomial logit choice model. In this problem, named as the dynamic assortment optimization problem (DAOP), the retailer must offer different assortments in each period to learn the customer preferences. Therefore, the trade-off between exploration of new assortments and the exploitation of the best known assortment must be balanced. Similarities between sampling and exploration are established in order to apply the cross-entropy method as a policy for the solution of the DAOP. The cross-entropy method finds a probability distribution that samples an optimal solution by minimizing the cross-entropy between a target probability distribution and an arbitrarily selected probability distribution. This requires the DAOP to be formulated as a knapsack problem with a penalty for offering assortments that exceed capacity. The results are compared with adaptive exploration algorithms and, experimentally, the cross-entropy method shows competitive results. These results suggest that the cross-entropy method can be used to solve other sequential decision-making problems.Este trabajo considera un problema de optimización de surtido, bajo restricción de capacidad y demanda desconocida, donde un vendedor ofrece un surtido y observa la venta de un producto según un modelo de elección logit multinomial. En este problema, llamado como el problema de optimización dinámica de surtido (PODS), el vendedor debe ofrecer diferentes surtidos en cada per´ıodo para aprender las preferencias del consumidor. Por lo tanto, el trade-off entre la exploraci´on de nuevos surtidos y la explotación del mejor surtido conocido debe ser equilibrado. Se estableció similitudes entre el muestreo y la exploración con el fin de aplicar el método de entrop´ıa cruzada como política para la solución del PODS. El método de entropía cruzada encuentra una distribución de probabilidad que muestrea una solución óptima al minimizar la entropía cruzada entre una distribución de probabilidad objetivo y una distribución de probabilidad seleccionada arbitrariamente. Esto requiere que el PODS se formule como un problema de la mochila con una penalización por ofrecer surtidos que superan la capacidad. Los resultados se comparan con algoritmos de exploración adaptativa y, experimentalmente, el método de entropía cruzada muestra resultados competitivos. Estos resultados sugieren que el método de entropía cruzada se puede utilizar para resolver otros problemas de toma de decisiones secuenciales