A metaheuristic for the capacity-pricing problem in the car rental business

A atenção ao problema de capacidade-preço no aluguer de carros tem vindo a aumentar à medida que as empresas começaram a investir em ferramentas avançadas de apoio à decisão para essas questões críticas. Ao planear um período de vendas, uma empresa deve decidir o número e o tipo de veículos necessários na sua frota de forma a atender à procura. A procura pelos veículos para aluguer é altamente sensível ao preço e, portanto, as decisões de capacidade e preço estão intimamente ligadas. Além disso, como os produtos são alugados, a capacidade "volta". Isso cria uma ligação entre a capacidade, a mobilização da frota e outras ferramentas que permitem à empresa atender à procura, tal como upgrades, transferência de veículos entre locais ou aluguer temporário de veículos adicionais. O impacto da solução desse complexo problema no lucro de uma empresa já foi estimado e avaliado, mas quando são tidos em conta os problemas do mundo real, o tamanho e a complexidade do problema tornam os métodos existentes lentos e inadequados para fornecer soluções num prazo razoável. O principal objetivo deste projeto é então selecionar, projetar e desenvolver uma meta-heurística eficiente que forneça boas soluções em curtos períodos de tempo.The capacity-pricing problem in car rental has increasingly been stepping in the spotlight as companies began investing in advanced decision-support tools for these critical issues. When planning a sales period, a company must decide the number and type of vehicles needed in its fleet in order to meet demand. The demand for rental vehicles is particularly price-sensitive and therefore capacity and pricing decisions are closely linked. In addition, as the products are rented, the capacity "returns". This creates an association between capacity, fleet mobilization and other tools that allow the company to meet demand, such as upgrades, transferring vehicles between locations or the temporary leasing of additional vehicles. The impact of solving this complex problem on a company's profit has already been estimated and evaluated, but when real-world problems are taken into account, the size and complexity of the problem makes existing methods slow and inadequate to provide solutions within a reasonable time. Therefore, the main objective of this dissertation is then to select, design and develop an efficient metaheuristic that provides similar or better results than the ones obtained in the literature

Hybrid iterated local search algorithm for optimization route of airplane travel plans

The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the Tabu-SA algorithm