Stochastic and robust optimization algorithms for the inventory-routing problem and its extensions

Door verschillende factoren zoals de loonkosten en het opleidingsniveau van arbeiders tonen meer en meer productieketens een grote geografische verspreiding. Voor de leefbaarheid van zo’n geglobaliseerde productieketen is het efficiënt organiseren van de logistieke taken van vitaal belang. Een gekende strategie om de globale logistieke kosten te drukken is het synchroniseren van transport en voorraadbeheer tussen de verschillende stappen van het productieproces. In deze dissertatie onderzoeken we hoe de modellen die ondersteuning bieden bij het optimaliseren van deze synchronisatie efficiënt opgelost kunnen worden. Hiervoor analyseren we de wiskundige structuur en eigenschappen van deze modellen. Op basis van onze bevindingen ontwerpen we specifieke optimalisatiealgoritmen zodat de problemen binnen aanvaardbare rekenkundige tijd opgelost kunnen worden. Daarnaast onderzoeken we ook hoe deze modellen aangepast kunnen worden wanneer een deel van de gegevens, bijvoorbeeld de vraag van een klant, onzeker is. We definiëren verschillende niveaus van onzekerheid op basis van de beschikbare informatie en stellen voor elk niveau een efficiënte oplossingsmethode voor. Ten slotte vergelijken we de verschillende oplossingen en evalueren hoe bijkomende informatie tot een betere oplossing kan leiden

An effective sample average approximation algorithm for the Inventory-Routing problem with Transshipment under stochastic demand

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An adjusted branch-and-bound algorithm for solving cyclical long-term inventory routing problems

The SV-CIRP is an optimization problem consisting of finding a recurring distribution plan, from a single depot to a selected subset of retailers, that maximizes the collected rewards from the visited retailers while minimizing transportation and inventory costs. It appears as fundamental building block for all variants of the cyclic inventory routing problem (CIRP). One of the main complications in developing solution methods for the SV-CIRP is the non-convexity of the objective function. We demonstrate how the problem can be reformulated so that its continuous relaxation is a convex optimization problem. We propose an adjusted branch-and-bound algorithm that solves the SV-CIRP more effectively

Stochastic optimization of a large-scale inventory-routing problem with transshipment through introduction of effective simulation steps

cited By 0International audienceIn this paper, we consider the Inventory-Routing Problem with Transshipment (IRPT) under stochastic demand. Traditional methods that optimize the decisions of stochastic problems often approximate the probability distribution by a set of scenarios. Although for small instances this approach often results in good quality solutions, the computational requirements make it unsuited for the optimization of large-scale problems. We investigate how such a sample average approximation method can be adjusted so that large instances of the stochastic IRPT can be solved within reasonable time. For this purpose we intersperse the optimization steps with a simulation phase that eliminates uninteresting solutions. We also develop a sequential simulation procedure to effectively select the optimal solution in the final stage of the algorithm. The experimental results show that the adjusted sample average approximation algorithm is able to solve instances with up to 35 retailers within reasonable time. © 2018 EUROSIS. All rights reserved

The Inventory-Routing problem with uncertain travel times

International audienceThe central problem studied in this work is the Inventory-Routing Problem (IRP), a combined inventory management and routing problem. One of the main assumptions of this model is that the travel times are predictable and fixed. In this work this assumption is dropped and the IRP is studied subject to uncertain travel times. The only available information is that these travel times are independent and symmetric random variables which can take some value from their support interval

Stochastic optimization of a large-scale inventory-routing problem with transshipment through introduction of effective simulation steps

cited By 0International audienceIn this paper, we consider the Inventory-Routing Problem with Transshipment (IRPT) under stochastic demand. Traditional methods that optimize the decisions of stochastic problems often approximate the probability distribution by a set of scenarios. Although for small instances this approach often results in good quality solutions, the computational requirements make it unsuited for the optimization of large-scale problems. We investigate how such a sample average approximation method can be adjusted so that large instances of the stochastic IRPT can be solved within reasonable time. For this purpose we intersperse the optimization steps with a simulation phase that eliminates uninteresting solutions. We also develop a sequential simulation procedure to effectively select the optimal solution in the final stage of the algorithm. The experimental results show that the adjusted sample average approximation algorithm is able to solve instances with up to 35 retailers within reasonable time. © 2018 EUROSIS. All rights reserved

An effective column generation algorithm for the Inventory-Routing Problem with Transshipment

International audienc

An efficient algorithm for the cyclic inventory routing subproblem

International audienceThe Single-Vehicle Cyclic Inventory Routing Problem (SVCIRP) is an optimization problem that provides a cyclic logistical plan, which maximizes the collected rewards while minimizing transportation and inventory costs, for the distribution of a product to a selected subset of retailers using one vehicle. It appears as a fundamental building block when columngeneration based methods are used to solve the cyclic inventory routing problem (CIRP).The current formulations for this problem all use a non-convex objective function. The presence of this non-convex objective function is a main complication in solving the SVCIRP efficiently, which in turn hinders the development of efficient solution methods for the CIRP. We examined the structure and mathematical properties of the SVCIRP and reformulated the problem so that its continuous relaxation is a convex problem. We proposed an adjustedbranch-and-bound algorithm that solves the SVCIRP efficiently. Compared to the benchmark instances for this problem we were able to find new 23 out of 50 new best solutions and proved optimality of 22 other instances