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.
The implementation of confidential contracts between a container liner carrier and its customers, because of the Ocean Shipping Reform Act (OSRA) 1998, demands a revision in the methodology applied in the carrier's planning of marketing and sales. The marketing and sales planning process should be more scientific and with a better use of operational research tools considering the selection of the customers under contracts, the duration of the contracts, the freight, and the container imbalances of these contracts are basic factors for the carrier's yield. This work aims to develop a decision support system based on a linear programming model to generate the business plan for a container liner carrier, maximizing the contribution margin of its freight.
ResumoNa presente pesquisa se propõe um modelo matemático de programação linear com variáveis binárias 0/1, para projetar a configuração de uma rede de distribuição de encomendas expressas, visando minimizar os custos e garantindo um bom nível de serviço. O modelo, que é uma modificação da formulação proposta por O'Kelly, define as posições dos hubs, a alocação deles às demais instalações físicas e a construção de roteiros com apenas uma parada intermediária, o que confere mais agilidade ao atendimento da demanda de transporte de carga. Considera-se a instalação de um único hub maior (Hub Principal), que serve como ponto de transbordo para os fluxos de carga entre as distintas regiões de um território, e um conjunto de terminais regionais (mini-hubs), servindo como ponto de conexão, unicamente, para os fluxos de carga existentes em um determinado raio de cobertura. Foram propostas extensões que incluem uma restrição da capacidade operacional do Hub Principal, para evitar seu congestionamento, e duas diferentes estratégias de induzir o aumento de fluxo de carga manipulado pelos mini-hubs. O modelo proposto e suas extensões foram aplicados ao estudo de um caso real, obtendo-se resultados consistentes e uma redução significativa no custo total da rede de distribuição da empresa analisada. Palavras-chaveLocalização industrial, pesquisa operacional, terminais de carga, transporte de carga. Abstract This study proposes a mathematical model of linear programming with binary variables 0/1 to project the configuration of a distribution network for express cargo which aims to minimize costs and guarantee a high level of service. The model, which is a modification of the formulation proposed by O'Kelly, defines the position of the hubs, their allocation regarding the other physical installations, and the building of itineraries with
A decision support system (DSS) is being proposed to support cargo planners in how to allocate general cargo transportation orders into platform supply vessels (PSVs). Besides mandatory cargoes which must be embarked, pending transportation orders should be prioritized and many operational constraints must be observed. The available tools found in commercial enterprise resource planning (ERP) allow for creating and editing cargo manifests without any support mechanism. The decision process it is totally dependent upon the planner, who is responsible for cargo selection, for checking consistencies and for solving conflicts without knowing, in advance, the position of each cargo in the deck. The proposed DSS interacts with commercial ERPs and provides an environment for manual cargo allocation through a drag-and-drop mechanism, and a heuristic allocation procedure focused in maximizing deck occupation respecting a broad range of operational constraints. 1. Introduction An effective response to the demand of drilling rigs and production platforms for supply items is a key issue in offshore logistics. Besides being a strategic segment, the assets present in the exploratory and production phases have extremely high capital and operational costs, reaching as high as $ 650,000 /day (Energy Global, 2013; Offshore Magazine, 2013), and cannot have its operations jeopardized due to supply shortages. While urban distribution systems often operate under traffic congestion, waterborne distribution operations may be adversely affected by environmental conditions hindering vessels to execute timely schedules. It is not uncommon reports of vessels that were kept from delivering supplies due to critical weather conditions in Campos Basin, Brazil. Irregular demand patterns may cause the distribution problem to become even more complex inasmuch as incoming transportation orders, that should be promptly managed, compete with backlogged ones. Considering that orders must attend a time window frame, late orders may gain an emergency status as their due dates are approached. Given that the ports serving the offshore system considered in our study are working under high occupation rates and that late deliveries from suppliers often occur, the distribution system demands continuous monitoring and effective support tools in order to get the most out of limited resources. Differently from the container market where general cargo are unitized into a few types of standardized ISO containers and conveyed in full cellular containerships, in the offshore segment no such standardization exists. Consider, for instance, the following companies that were found by browsing the internet in search of " offshore container": Acorn (2013); BSL (2013); Ferguson (2013). One will realize that each company provides more than a dozen types of containers and baskets for transporting goods. Rather than criticize or defend the use of a restricted set of standardized container types in offshore operations, our intention is to stress that this aspect poses an additional difficulty when planning cargo allocation.
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