Combining hybrid genetic search with ruin-and-recreate for solving the capacitated vehicle routing problem

The Capacitated Vehicle Routing Problem (CVRP) has been subject to intense research efforts for more than sixty years. Yet, significant algorithmic improvements are still being made. The most competitive heuristic solution algorithms of today utilize, and often combine, strategies and elements from evolutionary algorithms, local search, and ruin-and-recreate based large neighborhood search. In this paper we propose a new hybrid metaheuristic for the CVRP, where the education phase of the hybrid genetic search (HGS) algorithm proposed by (Vidal Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood 2020) is extended by applying large neighborhood search (LNS). By performing a series of computational experiments, we attempt to answer the following research questions: 1) Is it possible to gain performance by adding LNS as a component in the education phase of HGS? 2) How does the addition of LNS change the relative importance of the local search neighborhoods of HGS? 3) What is the effect of devoting computational efforts to the creation of an elite solution in the initial population of HGS? Through a set of computational experiments we answer these research questions, while at the same time obtaining a good configuration of global parameter settings for the proposed heuristic. Testing the heuristic on benchmark instances from the literature with limited computing time, it outperforms existing algorithms, both in terms of the final gap and the primal integral.publishedVersio

Analyzing passing networks in association football based on the difficulty, risk, and potential of passes

This paper investigates the use of network analysis to identify key players on teams, and patterns of passing within teams, in association football. Networks are constructed based on passes made between players, and several centrality measures are investigated in combination with three different methods for evaluating individual passes. Four seasons of data from the Norwegian top division are used to identify key players and analyze matches from a selected team. The networks examined in this work have weights based on three different aspects of the passes made: their probability of being completed, the probability that the team keeps possession after the completed pass, and the probability of the pass being part of a sequence leading to a shot. The results show that using different metrics and network weights leads to the identification of key passers in different phases of play and in different positions on the pitch. Keywords: network analysis, pagerank, centrality, sportpublishedVersio

Vessel Fleet Optimization for Maintenance Operations at Offshore Wind Farms under Uncertainty

In this paper we consider the problem of determining the optimal fleet size and mix of vessels to support maintenance activities at offshore wind farms. A two-stage stochastic programming model is proposed where uncertainty in demand and weather conditions are taken into account. The model aims to consider the whole life span of an offshore wind farm, and should at the same time remain solvable for realistically sized problem instances. The results from a computational study based on realistic data is provided.publishedVersio

Optimizing Jack-up vessel strategies for maintaining offshore wind farms

In this paper we present a new two-stage stochastic mathematical programming model that determines the optimal jack-up vessel strategy for an offshore wind farm. Given an offshore wind farm site, and distance to shore the model decides when, and for how long, a jack-up vessel should be chartered in order to minimize the total expected cost. The model considers both chartering and operational costs of the jack-up vessels, and the downtime costs of the wind farm which occurs when the wind turbines are not producing electricity. The model considers uncertainty both in the weather conditions and in when and how many components fail each year at the wind farm.publishedVersio

Optimization of routing and scheduling of vessels to perform maintenance at offshore wind farms

This paper studies the problem of finding the optimal routes and schedules for a fleet of vessels that are to perform maintenance tasks at an offshore wind farm. To solve the problem two alternative models are presented: an arc-flow and a path-flow formulation. Both models are tested on instances of varying numbers of vessels and maintenance tasks. The arc-flow model is solved with commercial software using branch-and-bound. The path-flow model is solved heuristically by generating a subset of the possible routes and schedules, but produces close to optimal solutions using a lot less computing time than the exact arc-flow model

A metaheuristic solution method for optimizing vessel fleet size and mix for maintenance operations at offshore wind farms under uncertainty

Maintenance operations at offshore wind farms are challenging due to the offshore element; maintenance technicians and spare parts need to be transported from an onshore port or offshore station to the individual wind farm components in need of maintenance. The vessel resources needed to support these maintenance tasks constitute a major part of the total maintenance costs, and hence up-keeping an optimal vessel fleet and corresponding deployment is essential to reduce cost-of-energy. This paper introduces a metaheuristic solution method to determine cost-efficient vessel fleets to support maintenance tasks at offshore wind farms under uncertainty. It considers weather conditions and failures leading to corrective maintenance tasks as stochastic parameters, and evaluates candidate solutions by a simulation program. The solution method has been incorporated in a decision support tool. Computational experiments, including comparison of results with an exact solution method, illustrate that the decision support tool can be used to provide near-optimal solutions within acceptable computational time.publishedVersio

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

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

A Branch-and-Price Algorithm for the Liner Shipping Network Design Problem

Maritime transportation is the backbone of the global economy and one of its most important segments is liner shipping. To design a liner shipping network is notoriously difficult but also very important since an efficient network can be the difference between prosperity and bankruptcy. In this paper, we propose a branch-and-price algorithm for the liner shipping network design problem, which is the problem of designing a set of cyclic services and to deploy a specific class of vessels to each service so that all demand can flow through the network at minimal cost. The proposed model can create services with a complex structure and correctly calculate the transshipment cost. The formulation of the master problem strengthens a known formulation with valid inequalities. Because of multiple dependencies between ports that are not necessarily adjacent and no defining state at any of the ports, the subproblem is formulated and solved as a mixed integer linear program. Strategies to improve the solution time of the subproblem are proposed. The computational study shows that the algorithm provides significantly tighter lower bounds in the root node than existing methods on a set of small instances

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 customer’s 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