Petroleum Logistics Research Articles

Exact and heuristic algorithms for the fleet composition and periodic routing problem of offshore supply vessels with berth allocation decisions

This paper presents a branch-and-cut algorithm and an adaptive large neighborhood search (ALNS) heuristic for the periodic supply vessel planning problem (PSVPP) arising in the upstream offshore petroleum logistics chain. Platform supply vessels support the offshore oil and gas exploration and production activities by transporting all the necessary material and equipment back and forth between offshore units and an onshore supply base according to a delivery schedule. The PSVPP consists of solving a periodic vehicle routing problem and simultaneously determining an optimal fleet size and mix of heterogeneous offshore supply vessels, their weekly routes and schedules for servicing the offshore oil and gas installations, and the berth allocations at the supply base. The branch-and-cut algorithm considers a reduced formulation for the problem which performs much better than the complete one, and easily finds optimal solutions for the smaller and most of the clustered instances. The ALNS heuristic contains new features which include multiple starts and spaced local searches. These algorithms were tested on instances with up to 79 offshore units, providing better results than the best available.

Interdependency in coordinating networked maintenance and modification operations

PurposeThe purpose of this research is to detect, through applying a process-based view, how to manage economisation of the maintenance and modification operations in offshore petroleum logistics operations.Design/methodology/approachA single case study of engineering services, more specifically, maintenance and modification service operations, on a Norwegian Sea oil platform reveals the dynamics of building network capabilities in a consistent network structure. Two layers of coordination are studied: the engineering process and its context, represented by its network of interconnected firms. This case study empirically grounds how engineering service involves managing reciprocally interdependent exchange processes in the network structure.FindingsPooled interdependencies are vital in understanding the nature of service provision and use, and sequential interdependencies are vital in narrating the timing of processes to reveal the nature of process emergence to coordinate strings of production events. Furthermore, the network structure, when characterised by multiple interdependent projects, is also dynamic but at a slower pace.Originality/valueThrough the case study, operations management is revealed to be associated with project emergence at two levels: the core process level regarding daily continuous change, including the changing interaction of multiple different and interdependent projects, and the contextual level, where features of interdependency and integration change, affecting engineering service production. This provides guidance as to the economisation of engineering services. They change not only interactions in the flow of production but also its context.

불확실성하의 해양석유생산 최적화를 위한 추계적 모형

Offshore oil production faces several difficulties caused by oil price decline and unexpected changes in the global petroleum logistics. This paper suggests a stochastic model for optimizing the offshore oil production under uncertainty. The proposed model incorporates robust optimization and restricted recourse framework, and uses the lower partial mean as the measure of variability of the recourse profit. Some computational experiments and results based on the proposed model using scenario-based data on the crude oil price and demand under uncertainty are examined and presented. This study would be meaningful in decision-making for the offshore oil production problem considering risks under uncertainty.

Supply vessel routing and scheduling under uncertain demand

We solve a supply vessel planning problem arising in upstream offshore petroleum logistics. A fleet of supply vessels delivers all the necessary equipment and materials to a set of offshore installations from an onshore supply base, according to a delivery schedule or sailing plan. Supply vessels, being the major cost contributor, are chartered on a long-term basis. The planning of supply vessels implies resolving the trade-off between the cost of the delivery schedule and the reliability of deliveries on the scheduled voyages, i.e. the service level. The execution of a sailing plan is affected by stochastic demands at the installations since a high demand fluctuation quite often leads to insufficient vessel capacity to perform a voyage according to the sailing plan. In addition, the average demand level at the installations may change over time, while the number of vessels in the sailing plan remains the same. Maintaining a reliable flow of supplies under stochastic demand therefore leads to additional costs and reduced service level. We present a novel methodology for reliable supply vessel planning and scheduling, enabling planners to construct delivery schedules having a low expected total cost. The methodology involves the construction of delivery schedules with different reliability levels using an adaptive large neighborhood search metaheuristic algorithm combined with a discrete event simulation procedure for the computation of the expected solution cost.

Robust supply vessel routing and scheduling

We solve the problem of tactical supply vessel planning arising in the upstream offshore petroleum logistics. Supply vessels deliver all the necessary materials and equipment to offshore installations from an onshore supply base according to a delivery schedule. The planning of supply vessels should be done so that their number is minimized and at the same time provide a reliable flow of supplies from the base. The execution of a weekly sailing plan is affected by weather conditions, especially in winter time. Harsh weather conditions increase the number of vessels required to perform the operations as well as the service times at the installations, and thus disrupt the schedule, leading to additional costs and reduced service level. We present a methodology for robust supply vessel planning enabling a trade-off analysis to be made between the schedules’ service level and vessels’ cost. The methodology involves the generation of multiple vessel schedules with different level of robustness using an adaptive large neighbourhood search metaheuristic and a subsequent discrete event simulation procedure for the assessment of the service level. To control the level of robustness we developed a concept of slacks and incorporated it into the metaheuristic algorithm.

The periodic supply vessel planning problem with flexible departure times and coupled vessels

In upstream offshore petroleum logistics, periodic supply vessel planning plays an important role since it ensures the replenishment of offshore installations on a regular basis with all the necessary equipment and materials from an onshore base. The problem involves the determination of the fleet composition and of the vessel schedules over a given time horizon. We present an extended version of this problem involving flexible departures from the base and the possibility of coupling vessels by swapping their schedules. We propose a voyage-based model that can be solved exactly for small- and medium-size instances. For the solution of larger instances, we have developed an adaptive large neighborhood heuristic, which yields optimal or near-optimal solutions relatively fast on small- and medium- size instances. Its performance on larger instances is significantly better than that of alternative algorithms previously developed for the same problem.

Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base

Abstract The future of logistics shipping bases will be to seek efficient flows of materials to meet the needs of business partners. Supply chain and operations managers of supply bases will need to integrate technologies that allow for greater automation, digitalization, flexibility and improved communications among stakeholders. The technologies that are likely to boost integration will consist of a plethora of Industrial Internet-of-Things (IIoT) technologies that may include Wireless Sensor Network (WSN) technologies and could be applied for improved monitoring of healthy and safe industrial workplaces for workers. However, little is known regarding how WSN technologies can be implemented on a larger scale and its implications when integrated on standard logistics and operations of industrial workplaces such as a shipping base. The WSN sensor units represent an integrating resource that are capable of monitoring air temperature, humidity and levels of carbon dioxide (CO2) and other gasses and of disseminating this information to different actors in the production system. Air quality factors play a critical role in the perceived levels of workers' comfort and in reported medical health. The low cost of wireless sensor network (WSN) technologies offer potential for continuous, autonomous and importantly networked assessment of industrial workplace air quality that may have implications for operations management and quality of production. This paper initially presents a case study that monitors air quality that is collected with WSN technologies from two workshops carried out by a large on-shore logistics base that supports offshore petroleum logistics. The case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. However, with the advancement in IIoT technologies and the emergence of smart sensing and actuating devices, it is possible to form a digital closed-loop system that we argue is essential for managers to link together information about air quality with supply chain and operations management decisions. We propose that central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. We discuss how WSN technologies can be integrated into the logistics management and operations of the shipping base. Through an analytical discussion of BD we explore how to extend the potential application of IIoT and Visual Analytics to facilitate a smart workplace for the Industry 4.0 era.

English

English

Agent-based modelling of offshore upstream petroleum logistics

Faced with dwindling oil prices, the petroleum industry now needs to economize its operations. A case study of upstream petroleum logistics, the management of supplies to and from petroleum platforms and rigs, is used to describe current logistics and supply chain management (SCM) challenges facing these operations in Norway. The provided case narrative reveals how an integrated planning system used to co-ordinate operations is proven relatively difficult to implement and use. As alternative research approach agent-based modelling (ABM) is applied and discussed in association with actor network theory in a SCM business-functional setting to theoretically ground use of ABM as methodology. An empirically-grounded conceptual model, the first stage of ABM methodology, is created for petroleum logistics. Findings from this first stage of inquiry also suggest how and why ABM is applicable in petroleum logistics and SCM.

Location Model and Optimization of Seaborne Petroleum Logistics Distribution Center Based on Genetic Algorithm

The network of Chinese Waterborne Petroleum Logistics (CWPL) is so complex that reasonably disposing and choosing Chinese Waterborne Petroleum Logistics Distribution Center (CWPLDC) take on the important theory value and the practical significance. In the study, the network construct of CWPL distribution is provided and the corresponding mathematical model for locating CWPLDC is established, which is a nonlinear mixed interger model. In view of the nonlinerar programming characteristic of model, the genetic algorithm as the solution strategy is put forward here, the strategies of hybrid coding, constraint elimination , fitness function and genetic operator are given followed the algorithm. The result indicates that this model is effective and reliable. This method could also be applicable for other types of large-scale logistics distribution center optimization.

石油物流・精製に関する世界モデルの構築

石油物流・精製に関する世界モデルの構築

Tankers and the U. S. Energy Environmental Analysis

The United States is facing an energy problem. The range of solutions varies from "the advancement of technology at any cost" to "a reversion to a bucolic society." Many of these solutions, while solving the energy problem, create an associated ecological problem. This paper illustrates a method by which additional oil can be safely imported to the U. S. by a marine transportation system. This marine system actually reduces the transportation cost of providing this energy and, more importantly, reduces the ecological damage to the oceans and beaches when compared with existing systems. The prospect of increasing oil imports for the United States at six or seven times the rate experienced in the past decade adds a new dimension to U. S. external petroleum logistics, particularly with respect to tankers and port facilities to accommodate them. The paper is divided into four major areas:The U. S. energy situation and projected oil requirements;economics of tankers to import that oil to the U. S.;the environmental impact of tanker transportation systems upon the U. S.; andthe cost and effectiveness of those systems.