Proactive Relocation Research Articles

Proactive Relocation Decision-Making in a Multinational Manufacturing Network

Recent years have been demanding for manufacturing companies with a global footprint – the constant need to evaluate and re-evaluate manufacturing networks and sourcing due to ongoing crises e.g., coronavirus shutdowns, changing customs tariffs, economic sanctions, inflation, availability issues, and war. A decade ago, everything was more stable in corporate manufacturing networks, and capacity allocation was merely based on cost consideration, competencies, and demand of different regions. Due to the changes mentioned, developed nation companies have started reshoring manufacturing operations increasingly back to developed countries; this is mostly due to hedging uncertainty and assuring availability and deliveries. This research analyses one steel industry case study of a corporate manufacturing network and shows what kind of factors their location decisions are based on in the current environment. The case also illustrates the ongoing process of manufacturing location and relocation with make or buy consideration. The process is implemented in stages, and if triggers are reached, the assessment is completed in a more comprehensive manner. In the end, we propose Information Technology (IT) advances and solutions for this environment, where the assessment period could have better responsivity, quality, and shorter lead time. Furthermore, we propose further changes and research on what decision factors should be included in the proactive relocation decision-making process.

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this study, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers’ income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. The results suggest that our method will reduce empty mileage per trip by 54–70% under the optimal matching strategy, and a 25–32% reduction can also be achieved by following the stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment of shared automated vehicles. In this study, a large fleet of shared automated vehicles providing private rides to passengers is introduced to an agent-based simulation model based on the city of Amsterdam, the Netherlands. The fleet is dimensioned for a sufficient service efficiency during peak-hours, meaning that in off-peak hours a substantial share of vehicles is idle, requiring vehicle relocation strategies. This study assesses the performance of zonal pro-active relocation strategies for on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion and (3) balancing between demand and supply of vehicles. The strategies are analysed in regard to service efficiency (passenger waiting times, operational efficiency), service externalities (driven mileage, parking usage) and service equity (spatial distribution of externalities and service provision). All pro-active relocation strategies are outperformed by a naïve remain-at-drop off-location strategy in a scenario where curbside parking capacity is in abundance. The demand-anticipation heuristic leads to the highest average waiting times due to vehicle bunching at demand-hotspots which results in an uneven usage of parking facilities. The most favourable results in regard to service efficiency and equity are achieved with the heuristics balancing demand and supply, at the costs of higher driven mileage due to the relocation of idle vehicles. These results open up opportunities for municipalities to accompany the introduction of large fleets of shared automated vehicles with suitable curbside management strategies that mitigate undesired effects.

Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations

In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.

Ambulance Dispatch Center Pilots Proactive Relocation Policies to Enhance Effectiveness

In life-threatening emergency situations in which every second counts, the timely arrival of an ambulance can make the difference between survival and death. In practice, the response-time targets, defined as the maximum time between the moment an incoming emergency call is received the moment when onsite medical aid is provided, are often not met. A promising means to reduce late arrivals by ambulances is to proactively relocate ambulances to ensure good coverage by the available ambulances in real time. This paper evaluates two dynamic relocation policies that an ambulance service provider in the Netherlands modified for operational use and implemented in a software tool for real-time decision support. The policies were used in a pilot program within a dispatch center for 12 weeks. Based on the success of this pilot, our policies were adopted for ongoing use and permanent implementation. This paper describes the relocation methods, evaluates the pilot, provides statistics for efficiency improvements, and discusses the experiences of ambulance dispatchers and management.

The Houma Indians’ battle against the ocean

Purpose – The Houma Indians reside in the backwaters of the Mississippi Delta, a coastal area that is disappearing due to natural subsidence, sea-level rise, and mismanagements of the ecosystems. The purpose of this paper is to scrutinize the causes of this ecocide and the responses addressing such environmental challenges, including scientific strategies, political non-intervention, and the United Houma Nation’s plans for preserving a sense of place and togetherness as a prerequisite for its cultural survival. Design/methodology/approach – This ethnographic investigation relies on participant observation, and interviews with tribal leaders, fishermen, trappers, as well as scientists and local politicians. In order to grasp the emic perspective, most interviews were conducted in the Houma French dialect. Findings – The Mississippi Delta epitomizes issues that will shape tomorrow’s world, namely, the vulnerability of coastal areas and the flows of environmental refugees. As shown by this study, coastal residents do not make a passive flux of evacuees responding to state/NGO-run plans. Actually, they are chief agents who either develop resilient strategies or proactive relocation stratagems to avoid ethnocide. Their pragmatic methodologies provide valuable data for any crisis management efforts. Originality/value – This research gives a voice to the voiceless, and conveys their existential struggles from within – unlike most studies of endangered communities relying on outsiders’ viewpoints. This perspective depicts the Houmas as actors of their survival who implement diverse tribal strategies for coping with environmental change.