Relocation Decisions Research Articles

A three-stage heuristic for optimizing container relocations in maritime container terminals

The Container Relocation Problem (CRP) is one of the most important optimization problems in maritime container terminals. The objective is to minimize the number of relocation operations for retrieving containers in a sequence. If the container to be retrieved next is not at the top of a stack, unproductive relocations have to be carried out. Due to the large number of containers handled by busy terminals, a slight reduction in relocation rates can result in significant savings in operating costs. Most of the existing heuristics make relocation decisions for the blocking containers one by one, based on simple indicators. In this article, we propose a Three-Stage Heuristic (3SH) that extends the decision horizon to multiple containers to achieve a higher-quality solution. Computational experiments are conducted on 3 sets of benchmark instances, and the results show that the proposed heuristic outperforms the state-of-the-art heuristics documented in the research literature.

‘Grand designs?’ Investigating the cultural and spatial logics of Channel 4’s media hub workspaces in Leeds, Bristol and Glasgow

This article explores the evolving spatiality of media production through an analysis of recent developments in UK broadcaster Channel 4’s move to a multi-site operating model. Historically, media buildings have served as landmarks of power and influence, embodying the aspirations and identities of media organisations and, as such, offer a fruitful lens for the specific exploration of the spaces of contemporary media production. Through a detailed analysis of Channel 4’s decision-making process, site selections and the significance of the chosen buildings within areas with an urban regeneration focus, the study investigates the complex interplay between media industry dynamics, regional development policies, and wider political and economic forces. Drawing on a range of academic literature and evidence from media, industry and policy reporting, the article highlights the strategic motivations behind Channel 4’s decentralisation efforts, positioned by the organisation as an effort to address diversity in terms of nations and regions representation but significantly shaped by political factors. However, it also raises critical questions about the long-term sustainability, economic viability and cultural impact of these relocation decisions. The article discusses how Channel 4’s regional media spaces and decentralisation reflect the impermanence and crisis facing Public Service Media, emphasising the shifting power dynamics within the media landscape and the need for adaptive strategies in an increasingly volatile industry environment. By examining the evolving investment in media buildings, this research contributes to a growing body of research seeking to understand the complex dynamics shaping contemporary media landscapes and urban environments.

Catastrophic fires, human displacement, and real estate prices in California

Millions of people are displaced by natural disasters each year, yet little is known about how evacuees affect host communities. We analyze the migratory effects of the most destructive fire in California history, the 2018 Camp Fire, which destroyed over 18,000 structures and displaced roughly 50,000 people. By merging geospatial information on the fire’s footprint with Zillow’s housing transaction data, we estimate both the spatial and temporal effects of the fire on real estate prices at a granular level. A number of important insights emerge. First, within 25 miles of the fire’s footprint, home prices increased by 13 percent in the six-week aftermath of the fire. Effects decay with distance and are statistically insignificant beyond 100 miles. Second, effects are detected within two weeks of the fire, fully materialize within four weeks, and are persistent up to ten months (which exhausts our period of consideration). Results are consistent with the relocation decisions of evacuees and are robust to a variety of specifications and modeling assumptions.

Might I have to move due to climate change? The role of exposure to risk and political partisanship in anticipation of future relocation

Recently, the body of research that assesses belief in climate change has grown, as has the number of studies appraising how experience with the impacts of climate change might affect public opinion. However, less research has addressed the question of whether/how people believe climate change will manifest itself in their own lives, or how it might affect behavioral responses more generally. Using two rounds of national survey data from 2016 and 2020, we examine the associations between climate risk, political party affiliation, and one potential planned adaptation behavior, relocation. Our results suggest that both exposure to risk and political identification help shape respondents’ beliefs. Several measures of risk are associated with respondents’ reports that weather/climate might cause them to move in the future and there is evidence to suggest that their awareness of risk may have increased between the two surveys. Regarding political affiliation, we find in one set of analyses that Republicans interviewed in 2020 were less likely than other political subgroups, including Republicans interviewed in 2016, to say weather/climate could have a potentially strong influence on future relocation decisions. We also find strong effects of age in one set of analyses, where younger respondents were much more likely than those at middle or older ages to report that weather/climate could exert a strong to moderate influence on a future move.

An underground pipeline relocation decision support system based on BIM and GIS integration

An underground pipeline relocation decision support system based on BIM and GIS integration

To live with floods or not: Intersectionality of drivers of urban households' adaptation and relocation intentions

AbstractThe intent of households to relocate amidst floods in Ghana's Greater Accra Metropolitan Area, using combined socio‐demographic and physical factors is analyzed within 1206 households. The National Master Sampling Frame of Ghana's Population and Housing Census is utilized for the sampling. The Probit estimation technique is employed to understand the intersectionality of social, economic, demographic, and physical considerations influencing households' decision‐making regarding relocation amidst flood risks. The findings show households' reluctance to relocate contrary to relocation considered mostly as preferred adaptation. The likelihood of relocating exhibited a non‐linear pattern, decreasing only when a population was younger until age 55 before reversing. Indigenous households preferred not to relocate. In communities where place attachment and revenue sources significantly impacted relocation decisions, households with secondary education, past flood experiences, and non‐indigenous status influenced higher perception of flood risk. Therefore, relocation as an effective global adaptation strategy to floods is not widespread. Thus, empowering households to accept a certain level of flood risk potentially avoids maladaptation and involves a combination of hard infrastructure measures and regulatory approaches in places of residence that do not compromise livelihoods. However, if relocation becomes necessary, a right‐based approach must be favored over an absolute risk‐based approach.

How social capital operated in Turkey following the earthquakes of February 6, 2023

ABSTRACT This study explores the effects of social capital on the decisions of individuals in Turkey who experienced the earthquakes of February 6, 2023, to stay in the disaster area or move to another place during the recovery period. Qualitative research was conducted using an in-depth interview technique with 70 participants. The findings show that all three types of social capital (bonding, bridging, and linking) played a significant role in the recovery process for earthquake victims, but bonding social capital was particularly important in their relocation decisions. Additionally, this study reveals that bonding social capital acted as a buffer immediately after the earthquakes and effectively solved problems in the short term, but it was replaced by bridging and linking social capital in the medium term. Finally, negative impacts of social capital are shown to have occurred not only among immigrants but also among local people.

Sydney’s residential relocation landscape: Machine learning and feature selection methods unpack the whys and whens

This study investigates household residential relocation timing, an aspect vital for transport and urban planning. Analyzing a high-dimensional dataset from 1,024 relocations in Sydney, Australia, the research contrasts ten machine learning survival techniques with three classical survival models. Results indicate that when classical models are paired with tree-based automated feature selectors, they align closely with machine learning outcomes. Notably, the GBM, XGBoost, and Random Forest models emerge as standout performers. The study provides a comprehensive comparison between automatic and manual feature selection, shedding light on variables influencing households’ duration of stay. While stacked ensemble modeling, which leverages predictions from various models, is used to enhance accuracy, the improvements are marginal, underscoring inherent modeling challenges, particularly the recurring issue of misclassifying specific pairs of households in the concordance index measure. A thorough feature analysis highlights homeownership as the foremost predictor, underscoring the importance of recent life events and accessibility features in relocation decisions. The research emphasizes the importance of considering the accessibility of both current and future homes in relocation models, with 20% feature significance in model outcomes. Building on these foundational insights, the study paves the way for a deeper understanding of individual decision-making processes in sustainable urban planning.

Robots, firm relocation, and air pollution: unveiling the unintended spatial spillover effects of emerging technology

Amidst the global upsurge in industrial robot deployment, there remains a notable gap in our understanding of their environmental impact. This paper explores how the introduction of industrial robots has changed air quality at both the local and neighborhood levels in China. Using the Spatial Durbin Model, we investigate the regional spillovers of PM 2.5 concentration and the diffusion of this innovative technology. Our findings reveal that the rise of robots significantly reduces air pollution in the local area, while exacerbating it in neighboring regions. This contrast is mainly because pollution-intensive industries are more inclined to relocate to neighboring regions than their cleaner counterparts, after the local use of robots increases. Throughout the process, internal costs rather than external costs dominate firms’ relocation decisions. This study provides novel insights into the complex environmental externalities associated with the spread of industrial robots and highlights the critical issue of growing environmental inequality in the era of emerging technologies.

Agri-food supply chain optimization through a decentralized production process in the olive oil industry

A tactical/operational model is proposed to assist decision-making in harvest planning and olive oil production planning of the different producers in the rural sector, considering allocation and transportation decisions of the kilograms of olives to the production plant with temporary relocation decisions of the mobile olive oil mills through decentralized production activities in the agri-food supply chain. A Mixed Integer Linear Programming model is presented that maximizes the profit of small farmers in the production process of single-varietal extra virgin olive oil. The decisions are based on the geographical dispersion of the different olive growers and the oil concentration of the olive varieties in a certain period, which depends on the climatic conditions of each sector where the olives are cultivated. The mathematical programming model is applied in Coquimbo, Chile, using actual data collected from previous harvest seasons. The computational results of the optimization model indicate that transportation decisions impact olive harvesting and oil production decisions. It is concluded that cooperatives of small producers in the rural sector increase overall profits and reduce transportation costs and the number of kilometers traveled by at least 41% through a decentralized production process with mobile production plants.

Perturbation of Residential Preferences by COVID-19 Shocks in the Greater Toronto Area

This research investigates the immediate effects of the COVID-19 pandemic on residential preferences in the Greater Toronto Area (GTA), Canada, using a stated preference (SP) survey dataset. The study examines changes in relocation preferences and trends in the GTA after the Ontario government lifted the initial lockdown. The obtained choice data is then modeled using a mixed cross-nested logit model to find substitution patterns across regions and dwelling types, as well as explore residents’ preferences for different dwelling characteristics and the accessibility of their residence, including factors such as telecommuting options. The results reveal that the pandemic caused short-term residential dissonance, with residents tending to want to move to lower-density areas to relocate to their preferred dwelling type, emphasizing telecommuting as a key factor influencing residential relocation preferences. Housing qualities were prioritized over accessibility. The study also found heterogeneous behavior among GTA residents with regard to telecommuting as a factor in residential relocation. The study’s findings are relevant for planners and policymakers in anticipating the potential long-term pandemic-induced home relocation decisions and their impact on future household travel behavior, particularly with regard to telecommuting and accessibility.

RESISTENSI MASYARAKAT PESISIR PANTAI MERPATI ATAS KEBIJAKAN PEMBANGUNAN WATERFRONT CITY BULUKUMBA

This study aims to determine the pattern and stage of resistance of the Merpati Bulukumba coastal community after the enactment of Regional Regulation No. 21 of 2012 concerning the 2012-2032 Bulukumba Regency Spatial Planning regarding the development of coastal areas through the Waterfront City grand design. This study used a descriptive qualitative method in the form of written narratives from the research subjects and observed behavior. In this study, the researchers determined the research location to be the coastal area of Merpati Beach in the Terang-Terang sub-district, Bulukumba district, South Sulawesi. The results showed that first, there was resistance in the coastal communities of Merpati Beach, with open patterns and strategies of resistance through rejection, demonstrations, and campaigns. Second, the process of resistance from the Pesisir Pantai Merpati community went through three stages so that they were able to influence local government policies through relocation decisions in the form of making shelter for the eviction victims.

Floating Ports as Support for Port Relocation Measures on Sea Level Rise

Ports are one of the structures where the effects of global warming are most severe and intense in atmospheric, oceanic, and geographical terms. According to the Intergovernmental Panel on Climate Change (IPCC)'s assessment reports, although it is possible to slow down global warming by reducing greenhouse gas (GHG) emissions, it is not foreseen to stop global warming and sea level rise (SLR) in any scenario. The rising sea level, an inevitable consequence of global warming, is a clear threat to conventional port facilities. In summary, SLR triggered by climate change, which is today's hot topic, may cause conventional port infrastructures to be flooded and lose their functionality. To cope with this threat, port facility planning, and design stages must be carried out by referring to the updated threshold values in Shared Socioeconomic Pathway (SSP) scenarios defined by the Working Groups of the IPCC. However, the uncertainty about the scale, timing, and location of SLR makes definitive solution-oriented approaches more prominent. One of these approaches is floating port structures. This study aims to reveal the role of floating port structures in the implementation of the relocation measure emphasized in the IPCC Sixth Assessment Report (AR6) for conventional ports under the threat of SLR. Initially, in this study, regions with higher SLR risk were identified by considering SSP scenarios contributed by Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) data. Afterwards, the dynamic downscaling model was used to determine the regions with higher regional sea level rise (RSLR) risk and the Marine Traffic database was used to determine the ports in these regions. Thus, it is evaluated whether floating ports can be a suitable alternative in the relocation decision of ports under SLR risk. It is expected that maritime transport will be maintained at adequate security and operational levels by revealing the pros and cons of floating ports.

Evaluating relocation behavior of establishments: Evidence for the short-term effects of COVID-19

Due to the significant effects of establishments’ relocations on travel patterns and land-use conditions, investigating establishments’ relocation behavior is an important issue. In recent years, many establishments closed or relocated because of a downturn economy, health concerns, interrupted supply chains, and work-from-home caused by the pandemic. Hence, this study aims to propose a modeling approach to assess and compare the relocation behavior of establishments before, during, and after the pandemic. Establishments’ relocation behavior is modeled in two steps: relocation decision and relocation action. The former provides insights into behavioral factors associated with establishment relocation and the latter models likelihood of spatial relocation choice. Using the data collected from the state of Tennessee, USA, the Random Forest classification approach is incorporated to model both steps, where the model validation results showed the promising accuracy of this modeling approach. Moreover, statistical analyses are applied to evaluate the differences between the spatial relocation choices throughout the time. Results showed that in post-Covid conditions, the importance of establishment characteristics on relocation decisions was reduced by half and relocations occurred more due to office profile and accessibility. Results of modeling relocation action indicated the high importance of accessibility, even though the attractiveness of accessibility was reduced by 20.9% in post-Covid analysis. The findings of this study enrich the knowledge on establishment relocation behavior and provide valuable information regarding the effect of the pandemic, which can be used in policy development and travel behavior modeling by urban and transportation planners.

Navigating the Relocation Trend: The Rising Shift of Multinational Corporations from China to Other Countries

This study explores the rising trend of multinational corporations relocating their operations from China to other countries, examining the drivers behind this migration and its potential implications for businesses and the Chinese economy. Based on institutional theory and the resource-based view, the study identifies factors influencing MNC relocation decisions, such as rising labor costs, trade tensions, market dynamics, and regulatory changes. Through an analysis of potential benefits and drawbacks, as well as opportunities and challenges for the Chinese economy, the study contributes to the existing literature by shedding light on the nuanced motivations driving MNC relocations and their impact on global business strategies and economic development. This research provides valuable insights for policymakers, business leaders, and academic researchers seeking to navigate the complexities of the worldwide economy. It underscores the need for further empirical research to explore the long-term implications of MNC migration trends.

Competing for manufacturing value added: How strong is competitive cost pressure on sectoral level?

Unit labour cost plays a pivotal role in shaping the regional distribution of manufacturing value added by influencing both export dynamics and business relocation decisions. Nonetheless, cost competitiveness is often regarded as playing a minor role in strategies promoting industrial upgrading. This perception stems from the belief that high-tech manufacturing sectors compete on the basis of quality rather than cost. This paper challenges this assumption and explores whether industries characterized by high levels of innovation and technological sophistication are indeed less reliant on competitive cost structures. In order to attain accurate estimations of sectoral value added elasticity in relation to cost, unit labour cost is conceptualised as a vertically integrated metric, accounting for the cost situation along the value chain. The analysis is centered on 18 manufacturing sectors across 28 European countries and covers the period from 1995 to 2018. The discrepancies in estimated cost elasticity across sectors appear to align more closely with their exposure to international markets and not with their degree of technological advancement. According to this sectoral-level evidence, cost competitiveness emerges as an essential prerequisite for industrial success.

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

Introducing climate-related counterurbanisation: Individual adaptation or societal maladaptation?

Climate disruption today and anticipated future climate breakdown are reshaping demographic and spatial processes, with profound consequences for societies across the globe. Specifically, migration can become a key strategy to attempt to respond to and cope with environmental change. This paper seeks to make sense of one type of migration, counterurbanisation, in this climate breakdown era. It provides conceptual clarity to what is termed ‘climate-related counterurbanisation’ vis-à-vis wider climate-induced migration and positions climate disruption within the counterurbanisation literature. Climate-related counterurbanisation is presented as a largely voluntary movement down the settlement hierarchy as a direct or indirect response to climate change, with positive representations of ‘rurality’ central to the relocation decision: individual adaptation. However, it is mediated by numerous geographically variegated and specific environmental, cultural, social and economic factors. Indeed, it may ultimately come to be seen more as maladaptation than adaptation. While moving from urban to rural may make sense at individual household level, such relocations can overall have much more negative impacts on host rural communities or the urban people left behind.

A failure or success?: A qualitative analysis of post-disaster relocation among survivors of 2010 floods in Pakistan

In Pakistan, the enormous 2010 floods inflicted unprecedented destruction to 0.5 million houses in South Punjab. This study was conducted in Muzaffargarh, a district of the Punjab province of Pakistan, one of the worst affected areas during the 2010 floods. As an adaptation and disaster risk reduction measure, an International NGO in collaboration with the Government of the Punjab province constructed 1274 houses for flood-affected communities. The houses were allocated through a lottery method. Initially, the allottees moved to the new settlements for the sake of possession. However, over time, it was observed that many flood-affected individuals sold out or rented their houses and returned to their villages/settlements. This qualitative study was conducted to identify the factors affecting the flood survivors’ decision to return to their original villages/settlements after a certain time. The specific objective of the study was to identify the factors that pushed and pulled the flood survivors into their original settlements. The current study interviewed 20 respondents through the snowball sampling technique by using an interview guide. The findings highlighted the loss of social capital, place attachment, unavailability of grazing places, non-transfer of house, and increase in the cost of living were the major factors that pushed the flood survivors to return to their native villages. The current study highlighted social and psychological costs (such as nostalgia, homesickness, loss of social networks, etc.) for the relocation of disaster survivors. This study contains important policy implications regarding relocation decisions taken by the government and policymakers. Findings indicate relocation decisions as the failure to address the local needs, priorities, and contexts.

Factors influencing employee relocation decisions: a study of chemical enterprises near the Three Gorges Dam Yangtze River China

Factors influencing employee relocation decisions: a study of chemical enterprises near the Three Gorges Dam Yangtze River China