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This research focuses a fleet scheduling problem with new operational constraints: each task requiring multiple types of vehicles simultaneously. This kind of operation occurs in offshore exploitation and production sites, when more than one vessel is needed to accomplish the tugging and mooring of oil platforms. Other constraints are maintained such as time windows, precedence between tasks, route duration and the demand attendance. The solution schedules are cost oriented, which encompasses the routing variable costs and the customer service costs. This is a variation of the classical fleet routing and scheduling, which is an NP-Hard problem. This research aims to solve the real scale problem through a combined use of branch and cut strategy with local search algorithms such as local branching and variable neighborhood search. An efficient heuristic rule will be used in order to generate initial solutions using the feasibility pump method.

This study addresses a vehicle routing problem with time windows, accessibility restrictions on customers, and a fleet that is heterogeneous with regard to capacity and average speed. A vehicle can perform multiple routes per day, all starting and ending at a single depot, and it is assigned to a single driver whose total work hours are limited. A column generation algorithm is proposed. The column generation pricing subproblem requires a specific elementary shortest path problem with resource constraints algorithm to address the possibility for each vehicle performing multiple routes per day and to address the need to set the workday’s start time within the planning horizon. A constructive heuristic and a metaheuristic based on tabu search are also developed to find good solutions.

This paper addresses a practical problem encountered in the oil industry, related to the supplying of general cargo to offshore rigs and production units. For a given route assigned to a supply vessel we seek to determine the optimal two-dimensional positioning of deck cargoes such that the overall profit is maximized, while ensuring that several safety and operational constraints are respected. In terms of mathematical modelling, the resulting problem can be seen as a rich variation of the two-dimensional knapsack problem, since some cargoes may wait for a later trip. Furthermore, given that the trip may serve many offshore units and that a substantial number of items must also return from these units, the problem becomes even more complex and can be viewed as a pickup and delivery allocation problem. We propose a probabilistic constructive procedure combined with a local search heuristic to solve this problem. We also report the results of computational experiments with randomly generated instances. These results evidence that our proposed heuristic can effectively help ship planners when dealing with such largescale allocation problems, with many operational constraints.

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