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

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Modeland Heuristics

This paper examines a ship routing problem with pickup and delivery and time windows for maritime oil transportation, motivated by the production and logistics activities of an oil company operating in the Brazilian coast. The transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, involving operations that demand agile and effective decision support systems. This paper presents an optimization approach to address this problem, based on a mixed integer programming (MIP) model and a novel and exploratory application of two tailor-made MIP heuristics, based on relax-and-fix and time decomposition procedures. The model minimizes fuel costs of a heterogeneous fleet of oil tankers and costs related to freighting contracts. The model also considers company-specific constraints for offshore oil transportation. Computational experiments based on the mathematical models and the related MIP heuristics are presented for a set of real data provided by the company, which confirm the potential of optimization-based methods to find good solutions for problems of moderate sizes

Tramp Ship Scheduling Problem with Berth Allocation Considerations and Time-dependent Constraints

This work presents a model for the Tramp Ship Scheduling problem including berth allocation considerations, motivated by a real case of a shipping company. The aim is to determine the travel schedule for each vessel considering multiple docking and multiple time windows at the berths. This work is innovative due to the consideration of both spatial and temporal attributes during the scheduling process. The resulting model is formulated as a mixed-integer linear programming problem, and a heuristic method to deal with multiple vessel schedules is also presented. Numerical experimentation is performed to highlight the benefits of the proposed approach and the applicability of the heuristic. Conclusions and recommendations for further research are provided.Comment: 16 pages, 3 figures, 5 tables, proceedings paper of Mexican International Conference on Artificial Intelligence (MICAI) 201

Optimizing multiple truck trips in a cooperative environment through MILP and Game Theory

Today, the challenge of economy regarding freight transport is to generate flows of goods extremely fast, handling information in short times, optimizing decisions, and reducing the percentage of vehicles that circulate empty over the total amount of transportation means, with benefits to roads congestion and the environment, besides economy. Logistic operators need to pose attention on suitable planning methods in order to reduce their costs, fuel consumption and emissions, as well as to gain economy of scale. To ensure the maximum efficacy, planning should be also based on cooperation between the involved subjects. Collaboration in logistics is an effective approach for business to obtain a competitive edge. In a successful collaboration, parties involved from suppliers, customers, and even competitors perform a coordinated effort to realize the potential benefit of collaboration, including reduced costs, decreased lead times, and improved asset utilization and service level. In addition to these benefit, having a broader supply chain perspective enables firms to make better-informed decisions on strategic issues. The first aim of the present Thesis is to propose a planning approach based on mathematical programming techniques to improve the efficiency of road services of a single carrier combining multiple trips in a port environment (specifically, import, export and inland trips). In this way, in the same route, more than two transportation services can be realized with the same vehicle thus significantly reducing the number of total empty movements. Time windows constraints related to companies and terminal opening hours as well as to ship departures are considered in the problem formulation. Moreover, driving hours restrictions and trips deadlines are taken into account, together with goods compatibility for matching different trips. The second goal of the Thesis is to define innovative planning methods and optimization schemes of logistic networks in which several carriers are present and the decisional actors operate in a cooperative scenario in which they share a portion of their demand. The proposed approaches are characterized by the adoption both of Game Theory methods and of new original methods of profits distribution