Relocation Strategy Research Articles

Vessel relocation strategy for multiple steel catenary riser fatigue damage mitigation

In previous work, the vessel relocation methodology for a single steel catenary riser (SCR) was developed to extend the fatigue life of the SCR touchdown zone (TDZ). The method was developed considering symmetrical vessel relocation programs, where the vessel offsets about the nominal station in all directions is equal. However, in actual field applications, the production platform hosts multiple SCRs of different azimuth, cross-section geometry and global configurations. Also, the symmetrical relocation consideration eliminates the potentials of exploring non-symmetrical relocation patterns that may be suitable candidate solutions. These considerations add complexity to the problem since, for example, each of the SCR may have unique optimum relocation programs (ideal solutions), but one global optimum solution is required. In this work, we extend the symmetrical relocation method and apply the index matching technique to solve the multi-objective optimisation problem. The non-symmetrical relocation of this methodology for multiple SCRs is demonstrated by comparing the global optimum solutions with those of the no-relocation case. The developed approach can be applied to new and existing SCRs for life extension purposes.

War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization

This paper proposes a new metaheuristic optimization algorithm based on ancient war strategy. The proposed War Strategy Optimization (WSO) is based on the strategic movement of army troops during the war. War strategy is modeled as an optimization process wherein each soldier dynamically moves towards the optimum value. The proposed algorithm models two popular war strategies, attack and defense strategies. The positions of soldiers on the battlefield are updated in accordance with the strategy implemented. To improve the algorithm’s convergence and robustness, a novel weight updating mechanism and a weak soldier’s relocation strategy are introduced. The proposed war strategy algorithm achieves good balance of the exploration and exploitation stages. A detailed mathematical model of the algorithm is presented. The efficacy of the proposed algorithm is tested on 50 benchmark functions and four engineering problems. The performance of the algorithm is compared with ten popular metaheuristic algorithms. The experimental results for various optimization problems prove the superiority of the proposed algorithm.

Implications of the Rohingya Relocation from Cox’s Bazar to Bhasan Char, Bangladesh

This IMR Dispatch attempts to elucidate the different concerns of human right groups and international communities over the relocation of the Rohingyas, a forcefully displaced ethnic minority of Myanmar, from the Cox's Bazar refugee camps, in mainland Bangladesh, to a newly developed island, Bhasan Char, in the Bay of Bengal. Of the nearly 1 million Rohingyas currently living in Cox's Bazar camps, the Bangladesh government has started relocating 100,000 Rohingyas to Bhasan Char. International organizations have expressed three concerns over this relocation strategy: first, that the Rohingyas have been relocated to Bhasan Char forcibly, second, that since Bhasan Char is a newly built island, there are potential environmental risks for the Rohingyas, and third, that this relocation does not ensure that the Rohingyas’ human rights will be respected on the island. The Bangladesh government, however, has dismissed these allegations, arguing that the relocation of 100,000 Rohingyas is voluntary and that the island provides them an opportunity for improved living. This IMR Dispatch reflects on those allegations concerning the relocation strategy, with the goal of drawing migration scholars’ attention to these developments. Since the Rohingyas are already a forcibly displaced community, migration scholars should pay close attention to this re-migration or onward migration of large numbers of Rohingyas to a new island and its implications for host-country approaches to forced migration.

Idle vehicle relocation strategy through deep learning for shared autonomous electric vehicle system optimization

In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers’ wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services.

A Simulation-optimization framework of vehicle relocation for one-way electric carsharing systems

In one-way electric carsharing systems (ECSs), customers can pick up an electric vehicle (EV) at one location and return it to any location, causing an imbalance of EVs and revenue loss for ECSs. In order to address this problem, a simulation-optimization framework is introduced to determine optimal vehicle relocation strategies. In this framework, the discrete event simulation model considers stochasticity and EV-dependent road congestion to simulate the operation of ECSs. And the optimization module is triggered by the simulation model to provide relocation strategies to the operator based on the genetic algorithm with the optimal computing budget allocation. The results of numerical experiments demonstrate that the simulation model can reflect the operation of ECSs in the real world, and the joint relocation strategies achieve improvements in both the profit and the vehicle utilization rate and reduce the number of relocations concerning the single relocation strategies used in the ECS.

An Improved Energy-Efficient Hybrid Framework Eehf – Algorithm for Green Cloud Computing

This paper deals with the relocation of multi-target Virtual Machines (VMs) in a cloud server farm. The proposed VM movement technique at the cloud server farm meanders VMs from underutilized to full capacity Physical Machines (PMs) to energy-efficient Physical Machines (PMs). Furthermore, the multi-target VMs relocation technique not only reduces the forced use of PMs and switches but also confirms the essence of administration by preserving the SLA at the cloud server farm. A novel energy-efficient hybrid (EEHF) system for enhancing the proficiency of electrical energy usage in data centers is carried out and evaluated in this paper. Instead of focusing on only one approach as in previous related works, the proposed system is truly based on solicitation preparation and worker booking. Until managing the preparation, the EEH system sorts the errand clients’ requests according to their time and force requirements. It has a booking system that takes power use into account when making planning decisions. It also has a precise calculation that determines if under burdened employees should be rested or dozed in overburdened workers, virtual machines that should be floated, and workers that will receive moved virtual machines VMs. When compared to other strategies, our proposed VMs development strategy may find a great balance among three conflict goals. Furthermore, the shroud-based cloud sim test results show that our proposed multi-target VMs relocation strategy outperforms best-in-class VMs movement strategies like the Random VMs relocation system in terms of energy efficiency and SLA penetration at the cloud server farm.

Generational retreat: locally driven adaption to coastal hazard risk in two Indigenous communities in Fiji

The relocation and retreat of low-lying coastal communities are expected to increase in anticipation of, and response to, sea-level rise and subsequent coastal hazard risks. As such, there is burgeoning attention in research and policy surrounding relocation that is managed and supported through external sources. Yet there exists limited research into the ways Indigenous communities are autonomously retreating from emerging coastal hazard risk, despite long histories of mobility and inherent adaptive capacities. We contribute to this gap through qualitative data derived from two low-lying coastal iTaukei (Indigenous Fijian) communities that are exposed to coastal erosion, storm surges, and flooding events, alongside other socioeconomic pressures. We explore the approach of “generational retreat” which is emerging as a long-term relocation strategy employed by village residents. Generational retreat is a form of mobility whereby households, namely, younger generations, incrementally retreat in response to existing, emerging, and anticipated coastal changes while maintaining connections to place and village life. We show how communities are drawing on local processes and resources to direct autonomous adaptation pathways which are suited to local sociopolitical, cultural, and environmental contexts. We further explore opportunities for governments and external actors in supporting these locally conceived and driven forms of retreat as an adaptive and sustainable response to coastal change.

Understanding travel preferences for user-based relocation strategies of one-way electric car-sharing services

One-way electric car-sharing services (ECS) are considered a promising solution for sustainable passenger mobility. To ensure balanced supply–demand of shared cars in the operational areas, ECS operators tend to explore user-based relocation strategies in addition to the costly operator-based ones. Through a stated preference experiment administered in the Netherlands, we study the travel preferences for ECS and user-based relocation strategies in a comprehensive framework integrating people’s current mobility choices and contextual variables. Incentives for picking-up/dropping-off shared cars at distant alternative locations and sharing rides are embedded in two sub-experiments of varied trip distances. The results from an error component multinomial logit (EC-MNL) model show multi-faceted preferences. Particularly, we find that people interested in ECS are willing to collaborate with ECS operators. Alternative pick-up is the most favorite relocation strategy, followed by alternative drop-off and sharing rides. The willingness to accept the incentive for additional access and egress time is estimated as high as 0.33 EUR per walking minute. It is also found that respondents do not show significant differences in preferences for car sanitation by ECS operators or self-service during the times of the COVID-19 pandemic. This study provides useful implications for operators and policy-makers regarding the user characteristics and contextual settings favoring user-based relocation strategies.

Human perturbations reduce dung beetle diversity and dung removal ecosystem function

AbstractBiodiversity drives ecological functioning, ultimately providing ecosystem services. Ecosystem processes are favored by greater functional diversity, particularly when groups of functionally different species interact synergistically. Many of such functions are performed by insects, among which dung beetles stand out for their important role in dung decomposition. However, anthropogenic disturbances are negatively affecting their ecological dynamics and ecosystem services. We conducted a manipulative field study to evaluate the effect of human disturbance on dung beetle diversity (abundance, species richness, and functional group richness) and dung removal rates, comparing perturbed and conserved forests in three regions of Colombia (Caribbean, Andes, and Amazon). We also assess the relationship between dung beetle diversity and dung removal rates. Dung beetle diversity was assessed using pitfall traps, and specimens were measured and assigned to functional groups according to body size and dung relocation strategy. We used exclusion control units and experimental units to assess dung degradation with and without dung beetle activity and evaluate differences in removal rates between two dung removal strategies: paracoprids and telecoprids. Dung removal rates, abundance, and functional group richness were lower in perturbed forests compared to conserved forests. Dung removal increased with abundance, species richness, and functional group richness. Moreover, dung removal performed by telecoprids increased with species richness of telecoprids and paracoprids. Our results evidence a negative effect of human perturbation on dung beetle richness, abundance, and dung removal rates, and also that dung beetle diversity and functional group richness enhance dung removal rates.Abstract in Portuguese is available with online material.

Thermal-hydraulic analysis of FAIDUS design for severe accident mitigation strategy in a medium size sodium-cooled fast reactor

The impediment of core disruption is realized through the provision of diverse and redundant safety features in the design complemented by the use of inherent passive mechanisms that respond to the accident condition by ensuring convenient dispersal of molten core material from the core in the early stage of the accident. In light of this, a promising design strategy Fuel Assembly Inner DUct Structure (FAIDUS) core concept is proposed to enhance the axial discharge capability of molten fuel from core for the exclusion of core wide melt pool formation. The sequence of event during the course of relocation in the FAIDUS is analysed employing a transient integrated thermal-hydraulic model. The effectual performance of various FAIDUS design options in mitigating the accident is evaluated. Based on this, the preferred design choice conforming the laid down criteria is selected in the present study. The simulation results suggest that FAIDUS design concept delivers the best alternative way for extenuating accident progress through a comprehensive melt relocation strategy of greater reliability in an intrinsic way.

The relationship between dung beetle diversity and manure removal in forest and sheep grazed grasslands

The relationships and linkages between biodiversity and ecosystem processes are critical issues in ecology. Manure removal is an ecosystem process related to several ecological functions performed by dung beetles. However, there is little information on manure removal by beetles in temperate ecosystems of Mexico. Using a linear model based on species biomass, here we estimated the amount of manure that can be potentially removed by dung beetle species in temperate forests and grasslands with sheep herding. Then, we related the estimated amount of manure removed with attributes of dung beetle communities: species richness, diversity, evenness and functional diversity in forest and grasslands sites. We compared the amount of manure potentially removed at each habitat by all species of dung beetles and by beetle’s food relocation strategies. Also, we used Generalized Additive Models (GAMs) to test the relationships between manure removal and community attributes. Our model estimated that five species remove most of the manure, and tunneler beetles remove higher amounts of manure in grasslands than in forest sites. GAMs revealed that manure removal is related to dung beetle species richness and evenness, but not to functional diversity. Therefore, it is relevant to promote rich dung beetle communities to maintain optimal ecosystem functions, both in native forest and livestock areas.

Spatiotemporal Demand Prediction Model for E-Scooter Sharing Services with Latent Feature and Deep Learning

The electric scooter (e-scooter) sharing service has attracted significant attention because of its extensive usage and eco-friendliness. Since e-scooters are mostly accessed by foot, the presence of e-scooters within walking distance has a crucial effect on the service quality. Therefore, to maintain appropriate service quality, relocation strategies are often used to properly distribute e-scooters within service areas. There are extensive literatures on demand forecasting for an efficient relocation. However, the study of the relocation of small-scale spatial units within walking distance level is still inadequate because of the sparsity of demand data. This research aims to establish an effective methodology for predicting the demand for e-scooters in high spatial resolution. A new grid-based spatial setting was created with the usage data. The model in the methodology predicts not only the identified demand but also the unmet demand to increase practicality. A convolutional autoencoder is used to obtain the latent feature that can reduce the problem of representing sparse data. An encoder–recurrent neural network–decoder (ERD) framework with a convolutional autoencoder resulted in a huge improvement in predicting spatiotemporal events. This new ERD framework shows enhanced prediction performance, reducing the mean squared error loss to 0.00036 from 0.00679 compared with the baseline long short-term memory model. This methodological strategy has its significance in that it can solve any prediction issue with spatiotemporal data, even those with sparse data problems.

Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands

Under the dual effects of climate change and urban heat islands (UHI), non-optimum temperature-related mortality burdens are complex and uncertain, and are rarely discussed in China. In this study, by applying city-specific exposure-response functions to multiple temperature and population projections under different climate and urbanization scenarios, we comprehensively assessed the non-optimum temperature-related mortality burdens in China from 2000 to 2050. Our results showed that temperature-related deaths will decrease from 1.19 million in 2010 to 1.08–1.17 million in 2050, with the exception of the most populous scenario. Excess deaths attributable to non-optimal temperatures under representative concentration pathway 8.5 (RCP8.5) were 2.35% greater than those under RCP4.5. This indicates that the surge in heat-related deaths caused by climate change will be offset by the reduction in cold-related deaths. As the climate changes, high-risk areas will be confronted with more severe health challenges, which requires health protection resource relocation strategies. Simultaneously, the net effects of UHIs are beneficial in the historical periods, preventing 3493 (95% CI: 22–6964) deaths in 2000. But UHIs will cause an additional 6951 (95% CI: −17,637–31,539, SSP4-RCP4.5) to 17,041 (95% CI: −10,516–44,598, SSP5-RCP8.5) deaths in 2050. The heavier health burden in RCP8.5 than RCP4.5 indicates that a warmer climate aggravates the negative effects of UHIs. Considering the synergistic behavior of climate change and UHIs, UHI mitigation strategies should not be developed without considering climate change. Moreover, the mortality burden exhibited strong spatial variations, with heavy burdens concentrated in the hotspots including Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Chengdu-Chongqing City Group, Guangzhou, Wuhan, Xi'an, Shandong, and Henan. These hotspots should be priority areas for the allocation of the national medical resources to provide effective public health interventions.

Doing the right thing or doing things right: what is better for a successful manufacturing reshoring?

The article concerns the revision of earlier decisions to offshore production activities (so called “relocation of second degree”); more specifically it is focused on the “reshoring” (also referred as “relocation to the home country”, “back-reshoring” or “back-shoring”). The research aims are to investigate what types of mistakes occur along the decision-making and implementation process and how they affect the outcome, in terms of success or failure, of a relocation strategy. A multiple case study involving four companies in the fashion industry from Portugal and Italy was conducted. The cross-case analysis allowed to differentiate decision-making mistakes from implementation ones and to assess differences and similarities among the cases in terms of content of the relocation, drivers and outcomes. The research contributes to previous literature on reshoring by bringing evidence of different types of mistakes to be considered, thus requiring further conceptualization of the reshoring process. Managers and entrepreneurs should consider the importance of doing the things right also during the implementation, too often underestimated. The present article is the first one in the reshoring literature bringing evidence of cases of failure in the relocation decisions and discriminating among different kinds of mistakes.

A user-based relocation model for one-way electric carsharing system based on micro demand prediction and multi-objective optimization

As a promising sustainable mode of transportation, electric carsharing can relieve the urban traffic pressure and environmental pollution problems, which has been promoted all over the world to reduce or replace fossil-fueled private cars. However, the operation of electric carsharing is faced with difficulties in profitability and management. Vehicle relocation can alleviate the imbalance between supply and demand, and compared with operator-based relocation, user-based relocation is more sustainable and cost-efficient. In this paper, we develop a user-based relocation model with the optimization objectives of profit maximization and using failure rate minimization. Two station pricing schemes of pure preferential and preferential combined with fine are proposed as the constraints. For the input of the relocation model, the back propagation neural network of the quantity demand and the distribution fitting model of the energy demand are constructed to predict user demand. The demand prediction model and relocation model are validated through the operation data of EVCARD in Shanghai. The results demonstrate that the user-based relocation can effectively reduce the using failure rate and has the potential to increase the net profit. Compared with the situation without vehicle relocation, the user-based relocation strategy can reduce using failure rate by 13.6% and increase net profit by 76.9% in the best situation. Through sensitivity analysis of key parameters, it is found that the station pricing of pure preferential can reduce the using failure rate, but the profitability of the electric carsharing system is weakened. The introduction of fine can effectively slow down the erosion of user incentive on the system profit and increase the profit.

Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently.

Offline-Online Approximate Dynamic Programming for Stochastic Carsharing Systems with Relocation Incentives

The development of carsharing services is expected to achieve greater resource efficiency and provide a sustainable solution for future mobility systems. However, operators inevitably face the imbalance between demand and supply in one-way carsharing systems (CSSs). Also, it is challenging for them to make quick and efficient operational decisions when both travel time and trip requests are uncertain. This study leverages historical and online data and proposes a data-driven methodology to quickly make real-time decisions for CSSs, including vehicle assignment, relocation, and user incentive decisions. Compared to the literature in approximate dynamic programming (ADP), which mostly focuses on uncertainty in trip requests, we propose an offline-online ADP approach to consider the temporal and spatial uncertainty in both trip requests and travel time. To tackle our high-dimensional problem, we integrate an online look-ahead policy into the offline value function approximation (VFA) policy to produce a computational tractable high-quality dynamic fleet management policy. Furthermore, a user-based relocation strategy is investigated to rebalance the fleet distribution to meet the demand better. Specifically, we aim to solve the optimal incentives the operator could offer to users to relocate cars, while user preferences towards relocation incentives are generally unknown in practice. We further enhance our anticipatory policy by developing an online module via Bayesian learning that calibrates the preference model on the fly using users’ revealed preference data collected online. The numerical experiments in Singapore demonstrate our offline-online ADP approach improves the solution quality and computational efficiency significantly compared to offline VFA policy. The results also confirm the importance of incorporating uncertainty in travel time. The benefits of using online data to enhance anticipatory decisions and learn unknown user preferences are also illustrated. After a few online iterations, the preference parameters converge to the true values, which further reduces the relocation cost and increases the profit.

A static relocation strategy for electric car-sharing systems in a vehicle-to-grid framework

ABSTRACT Car-Sharing Systems (CSSs) are becoming increasingly popular in urban areas replacing car ownership. The most attractive CSSs give users the opportunity to make one-way trips. This behavior creates an unbalanced status between stations. Hence, some users could leave the system because they may not find a car/parking place available near their origin/destination. In recent years, CSSs are employing Electric Vehicles (EVs). A recent technology applied to EVs, called Vehicle-to-Grid (V2G), has allowed selling energy, transferring it from vehicle batteries to an electric grid. In this paper, we adopt Electric Cars (ECs) with V2G for one-way station-based CSSs. We suggest ECs distribution among stations at the beginning of each day, simultaneously making the most of V2G technology and satisfying CSSs customers’ requests. These distributions represent the final configurations that should be obtained through overnight vehicle relocation. The proposed models have been applied to a real-sized case achieving promising results.

A polynomial-time algorithm for user-based relocation in free-floating car sharing systems

Free-floating car sharing (FFCS) systems are a promising concept to reduce the traffic volume in cities. However, spatial and temporal mismatches of supply and demand require a relocation of rental cars in order to avoid low degrees of utilization. Here, especially user-based relocation strategies seem to be promising to increase utilization in a cost-efficient manner. However, a thorough optimization-based assessment of user-based relocation strategies for FFCS systems is still missing.In this paper, we introduce an integer program that optimizes the assignment of user-based relocation strategies in FFCS fleets. We develop a graph representation that allows to reformulate the problem as a k-disjoint shortest paths problem and propose an exact algorithm to solve large-size instances. We show that this algorithm can solve real-world instances within a few milliseconds as well as instances with up to 100,000 customers and 10,000 vehicles in a few minutes.Furthermore, we present a case study based on real-world data and derive managerial insights on user-based relocation strategies. Our results reveal an upper bound on the benefit of user-based relocation strategies and demonstrate that the employment of such strategies can increase the number of fulfilled rental requests by 21%, while increasing the operator’s revenue by 10%.

Predicting shared-car use and examining nonlinear effects using gradient boosting regression trees

Flexible drop-off and pick-up (one-way) carsharing programs provide users with high levels of convenience but meanwhile incurs spatiotemporal imbalances in shared-cars distribution. Predicting shared-car use helps recognize system imbalances beforehand while identifying determinants related to shared-car use helps operators efficiently implement relocation strategies. In this study, a gradient boosting regression model (GBRT) is employed to predict shared-car use at a station level, and partial dependence plots (PDPs) are employed to examine nonlinear relationships between shared-car use and various predictors. Results show: (1) GBRTs predict shared-car use with a high level of accuracy (MSE: 1.1069–1.1648). (2) PDPs present highly consistent results with relationships derived from the traditional statistical model; (3) Time-varying variables account for 89.30%–86.84% importance in shared-cars use prediction, suggesting these variables can greatly enhance prediction accuracy; (4) Other variables like built environment, station attributes, and socioeconomic features, also account for some importance and can enhance prediction accuracy. Findings help carsharing operators accurately predict the station-level shared-car use and optimally identify the best locations for stations, and thus maintain the operational efficiency of carsharing programs.