The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems

A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure

Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances

Recent studies in maritime logistics have introduced a general ship routing problem and a benchmark suite based on real shipping segments, considering pickups and deliveries, cargo selection, ship-dependent starting locations, travel times and costs, time windows, and incompatibility constraints, among other features. Together, these characteristics pose considerable challenges for exact and heuristic methods, and some cases with as few as 18 cargoes remain unsolved. To face this challenge, we propose an exact branch-and-price (B&P) algorithm and a hybrid metaheuristic. Our exact method generates elementary routes, but exploits decremental state-space relaxation to speed up column generation, heuristic strong branching, as well as advanced preprocessing and route enumeration techniques. Our metaheuristic is a sophisticated extension of the unified hybrid genetic search. It exploits a set-partitioning phase and uses problem-tailored variation operators to efficiently handle all the problem characteristics. As shown in our experimental analyses, the B&P optimally solves 239/240 existing instances within one hour. Scalability experiments on even larger problems demonstrate that it can optimally solve problems with around 60 ships and 200 cargoes (i.e., 400 pickup and delivery services) and find optimality gaps below 1.04% on the largest cases with up to 260 cargoes. The hybrid metaheuristic outperforms all previous heuristics and produces near-optimal solutions within minutes. These results are noteworthy, since these instances are comparable in size with the largest problems routinely solved by shipping companies

МОДЕЛИРОВАНИЕ ЭФФЕКТИВНОСТИ ИСПОЛЬЗОВАНИЯ ГРУЗОВОГО АВТОМОБИЛЬНОГО ТРАНСПОРТА В ЗАВИСИМОСТИ ОТ СРОКА ЕГО ЭКСПЛУАТАЦИИ

Существенная доля грузового транспорта приобретается производственными и торговымикомпаниями для выполнения перевозок своих грузов. Ограниченный объем собственного грузопо-тока приводит к недоиспользованию провозной возможности грузового транспорта, что ведет кросту транспортных издержек. Учитывая этот фактор, а также высокую стоимость нового транс-порта, предприятия приобретают подвижной состав, бывший в эксплуатации.Целью работы является оценка эффективности эксплуатации подержанных тягачей на основетехнико-экономического моделирования. При моделировании транспортного процесса за базовыепоказатели принимаются грузооборот предприятия и протяженность маршрутов.Анализ производительности транспортных средств от срока службы позволил получить зави-симость изменения стоимости среднегодового пробега тягача от срока эксплуатации. На основа-нии анализа рынка по продаже грузового автотранспорта предложено ввести коэффициент, учи-тывающий снижение производительности тягачей от срока их эксплуатации. По статистическимданным была аппроксимирована функция, отражающая динамику изменения введенного коэффи-циента. С помощью данной функции построена математическая модель для технико-экономической оценки использования подвижного состава по показателям производительности.Моделирование перевозочного процесса позволило определить рациональный срок эксплуатацииподвижного состава, позволяющего выполнить весь объем работ с минимальной стоимостью

Tutorial: Modern Branch-and-Cut-and-Price for Vehicle Routing Problems Plan of the talk

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