Magnus Stålhane

A construction and improvement heuristic for a liquefied natural gas inventory routing problem

We present a large scale ship routing and inventory management problem for a producer and distributor of liquefied natural gas (LNG). The problem contains multiple products, inventory and berth capacity at the loading port and a heterogeneous fleet of ships. The goal is to create an annual delivery program to fulfill the producers long-term contracts at minimum cost, while maximizing the revenue from selling LNG in the spot market. To solve this problem we have developed a construction and improvement heuristic (CIH). The CIH is a multi-start local search heuristic that constructs a set of solutions using a greedy insertion procedure. The solutions are then improved using either a first-descent neighborhood search, branch-and-bound on a mathematical formulation, or both. Tests on real-life instances show that the CIH provides good solutions in a short amount of time.

Optimisation of maintenance routing and scheduling for offshore wind farms

An optimisation model and a solution method for maintenance routing and scheduling at offshore wind farms are proposed. The model finds the optimal schedule for maintaining the turbines and the optimal routes for the crew transfer vessels to service the turbines along with the number of technicians required for each vessel. The model takes into account multiple vessels, multiple periods (days), multiple Operation & Maintenance (O&M) bases, and multiple wind farms. We develop an algorithm based on the Dantzig–Wolfe decomposition method, where a mixed integer linear program is solved for each subset of turbines to generate all feasible routes and maintenance schedules for the vessels for each period. The routes have to consider several constraints such as weather conditions, the availability of vessels, and the number of technicians available at the O&M base. An integer linear program model is then proposed to find the optimal route configuration along with the maintenance schedules that minimise maintenance costs, including travel, technician and penalty costs. The computational experiments show that the proposed optimisation model and solution method find optimal solutions to the problem in reasonable computing times.

A branch-price-and-cut method for a ship routing and scheduling problem with split loads

We present a branch-price-and-cut method to solve a maritime pickup and delivery problem with time windows and split loads. The fleet of ships is heterogeneous and fixed for the planning horizon, and no common depot exists. The cargoes picked up and delivered consist of both mandatory and optional cargoes, and each cargo may be split among several ships. The objective is to design a route for each ship that will maximize the total profit from transporting all the mandatory and a subset of the optional cargoes. To solve this problem we introduce a new path-flow formulation, which we solve by branch-price-and-cut. The subproblem is a new variant of the elementary shortest path problem with resource constraints, where a multi-dimensional knapsack problem is solved to compute optimal cargo quantities. Further, we present new valid inequalities for this problem, and adaptations of existing inequalities successfully used to solve related problems in the literature. Finally, the computational results show that for certain types of instances, our solution method outperforms existing methods proposed in the literature.

Routing and Scheduling of Maintenance Fleet for Offshore Wind Farms

Reducing the operation and maintenance (O&M) cost is a necessity in current offshore wind farms so that the produced power can achieve a competitive price in the market. An offshore wind farm normally comprises a large number of turbines which demand frequent maintenance visits. In addition to making maintenance plans that avoid downtime and production losses, it is important to utilize the expensive resources, such as service vessels, in an efficient way. This article introduces the routing and scheduling problem of a maintenance fleet for offshore wind farms (RSPMFOWF), which is to determine the optimal assignments of turbines and routes to the vessels in terms of cost. Simultaneously considering the characteristics and limitations in this problem, we present the mathematical formulations for the RSPMFOWF. A computational case study is also carried out. The results provide both the optimized cost and detailed arrangements, which can be directly used in maintenance planning.

An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping

In this paper a vendor managed inventory (VMI) service in tramp shipping is considered. VMI takes advantage of introducing flexibility in delivery time and cargo quantities by transferring inventory management and ordering responsibilities to the vendor which in this case is a shipping company. A two-phase heuristic is proposed to determine routes and schedules for the shipping company. The heuristic first converts inventories into cargoes, thus turning the problem into a classic ship routing and scheduling problem. It then uses adaptive large neighborhood search to solve the resulting cargo routing and scheduling problem. The heuristic iteratively changes the cargoes generated to handle the customers inventories, based on the information obtained from an initial solution. Computational results are presented, discussed and compared with exact solutions on large realistic instances. The results reveal the potential savings from converting traditional contracts of affreightment to an integrated VMI service. The factors that influence the benefits obtainable through VMI are also analyzed. We study a problem that combines tramp shipping with a VMI service.We use a novel method to convert inventories into cargoes with time windows.An iterative heuristic combining cargo generation and an ALNS heuristic is presented to solve the problem.The factors that influence the benefits obtainable through VMI are analyzed.

A branch-and-price method for a ship routing and scheduling problem with cargo coupling and synchronization constraints

The purpose of this paper is to present a branch-and-price method to solve a maritime pickup and delivery problem with time windows, cargo coupling, and synchronized deliveries. This problem originates from a segment of shipping called project shipping that subsists on transporting unique and specialized cargoes. The fleet of ships is heterogeneous and considered fixed. Some sets of cargoes are coupled, meaning that either all or none of them are transported. Further, some of these coupled cargoes require synchronized deliveries, restricting the time elapsed between the first and last delivery is made. The objective is to design a route and schedule for each ship that maximizes the total profit from transporting a subset of the cargoes available. A new mathematical formulation is presented and solved using branch-and-price. The subproblem is a new variant of the elementary shortest path problem with resource constraints, and is solved by dynamic programming. Finally, the computational results show that the approach presented in this paper is significantly better than existing methods for solving this problem.

2D-Packing with an Application to Stowage in Roll-On Roll-Off Liner Shipping

Roll-on/Roll-off (RoRo) ships represent the primary source for transporting vehicles and other types of rolling material over long distances. In this paper we focus on operational decisions related to stowage of cargoes for a RoRo ship voyage visiting a given set of loading and unloading ports. By focusing on stowage on one deck on board the ship, this can be viewed as a special version of a 2-dimensional packing problem with a number of additional considerations, such as one wants to place vehicles that belong to the same shipment close to each other to ease the loading and unloading. Another important aspect of this problem is shifting, which means temporarily moving some vehicles to make an entry/exit route for the vehicles that are to be loaded/unloaded at the given port. We present several versions of a new mixed integer programming (MIP) formulation for the problem. Computational results show that the model provides good solutions on small sized problem instances.

A variable neighbourhood search heuristic for disruption management in offshore oil and gas logistics

Abstract This paper studies operational planning and disruption management in offshore oil and gas logistics. A significant amount of time is currently spent on operational planning, and major costs are caused by disruptions to the planned routes and schedules for the vessels supplying the offshore installations. The disruptions are mainly due to uncertain and harsh weather conditions. To be able to solve realistic instances of the planning problem, a variable neighbourhood search heuristic is proposed, and tested on instances based on data provided by the case company. The computational results show that the heuristic finds optimal solutions for all the problem instances where the optimal solution is known, and finds high-quality solution for larger instances.

Global Optimisation of Multi-Plant Manganese Alloy Production

Abstract This paper studies the problem of multi-plant manganese alloy production. The problem consists of finding the optimal furnace feed of ores, fluxes, coke, and slag that yields output products which meet customer specifications, and to optimally decide the volume, composition, and allocation of the slag. To solve the problem, a nonlinear pooling problem formulation is presented upon which the bilinear terms are reformulated using the Multiparametric Disaggregation Technique (MDT). This enables global optimisation by means of commercial software for mixed integer linear programs. We demonstrate the model and solution approach through case studies from a Norwegian manganese alloy producer. The computational study shows that the model and proposed optimisation approach can solve problem sizes of up to ten furnaces to a small optimality gap, that global optimization approach with MDT scales well with larger, real problem instances, and that the model outperforms the current operational practice.

A Shortest Path Heuristic for Evaluating the Quality of Stowage Plans in Roll-On Roll-Off Liner Shipping

Roll-on Roll-off shipping companies transport rolling cargo, such as cars, trucks and large construction machines. When sailing, this type of cargo must be attached to the deck using chains, to prevent damaging the cargo. For each voyage including multiple port calls where the cargo is loaded/unloaded, an important decision is to decide where to place each vehicle (or unit), such that the time used on shifting is minimized. Shifting means temporarily moving some vehicles to make an entry/exit route for the vehicles that are to be loaded/unloaded at a given port. As the vehicles are securely fastened to the deck, shifting is a time-consuming procedure. We present the stowage plan evaluation problem which is to determine the optimal vehicles to shift at each port call, such that the time spent on shifting is minimized. Given a set of alternative stowage plans for a voyage, the results from the stowage plan evaluation problems are used to determine the best among these stowage plans. We present a shortest path based heuristic for solving the problem. Computational results show that the solution method is a powerful tool for comparing stowage plans, due to its fast computing times and high success rate, i.e. its ability to determine the better of two stowage plans.