Stochastic optimization for investment in facilities in emergency prevention

A coordinated approach is developed to integrate three preventive measures (i.e. building reinforcement, reinforcement of road networks, and facility location of relief supplies), with the objectives of minimizing budgets and risk-induced penalties. The Conditional Value-at-Risk is employed as a decision-making tool to evaluate diverse decisions of prevention based on the degree of risk aversion. Based on a real-world case of an earthquake, a series of scenarios were designed, and the applicability of the proposed model was studied. The coordinated approach for investing preventive measures is cost-efficient in helping reduce the impact of disaster on society.     

A stochastic programming approach for floods emergency logistics

This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles’ availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events.     

A comprehensive evacuation planning model and genetic solution algorithm

We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.     

A three-stage stochastic facility routing model for disaster response planning

This paper presents a three-stage mixed-integer stochastic programming model for disaster response planning, considering the opening of local distribution facilities, initial allocation of supplies, and last mile distribution of aid. The vehicles available for transportation, the state of the infrastructure and the demand of the potential beneficiaries are considered as stochastic elements. Extensive computational testing performed on realistic instances shows that the solutions produced by the stochastic programming model are significantly better than those produced by a deterministic expected value approach.     

An emergency logistics distribution approach for quick response to urgent relief demand in disasters

Quick response to the urgent relief needs right after natural disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas, which remains challenging in the field of logistics and related study areas. This paper presents a hybrid fuzzy clustering-optimization approach to the operation of emergency logistics co-distribution responding to the urgent relief demands in the crucial rescue period. Based on a proposed three-layer emergency logistics co-distribution conceptual framework, the proposed methodology involves two recursive mechanisms: (1) disaster-affected area grouping, and (2) relief co-distribution. Numerical studies with a real large-scale earthquake disaster occurring in Taiwan are conducted, and the corresponding results indicate the applicability of the proposed method and its potential advantages. We hope that this study can not only make the proposed emergency logistics system available with more benefits to the development of emergency logistics systems for the urgent needs of disaster areas around the world but also stimulate more excellent researches concerning emergency logistics management.     

A location-inventory model for large three-level supply chains

We study the location-inventory problem in three-level supply networks. Our model integrates three decisions: the distribution centers location, flows allocation, and shipment sizes. We propose a nonlinear continuous formulation, including transportation, fixed, handling and holding costs, which decomposes into a closed-form equation and a linear program when the DC flows are fixed. We thus develop an iterative heuristic that estimates the DC flows a priori, solves the linear program, and then improves the DC flow estimations. Extensive numerical experiments show that the approach can design large supply networks both effectively and efficiently, and a case study is discussed.     

A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways

The hypercube is a spatially distributed queuing model based on Markovian analysis approximations, used to analyze the configuration and operation of server-to-customer emergency systems. In the present study we adapted the model to analyze emergency medical systems (EMS) on highways, which operate within particular dispatching policies. The study takes into consideration that: the emergency calls are of different types; the servers are distinct (e.g., rescue ambulances, medical vehicles); only certain servers in the system can service calls in a given region (partial backup); and, depending on the type of call, one or more identical or distinct servers are immediately dispatched to service such calls (multiple dispatch). We also consider that the arriving calls take place either along the highway or at the home location of a server – in which case the server does not need to travel to the call location. Finally, we analyzed the computational results of applying such an approach to the case study of an EMS operating on Brazilian highways.     

A reverse logistics cost minimization model for the treatment of hazardous wastes

This study presents a cost-minimization model for a multi-time-step, multi-type hazardous-waste reverse logistics system. A discrete-time linear analytical model is formulated that minimizes total reverse logistics operating costs subject to constraints that take into account such internal and external factors as business operating strategies and governmental regulations. Application cases are presented to demonstrate the feasibility of the proposed approach. By using the proposed model coupled with operational strategies, it is shown that the total reverse logistics costs for the applications cases can be reduced by more than 49%.     

The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels

This paper studies the location–allocation–configuration problem of emergency resources in a maritime emergency system and it proposes a discrete nonlinear integer-programming model, which integrates the location, allocation and the configuration problem. The model is converted into a two-stage model keeping the calculation logic. It designs a hybrid heuristic algorithm and a genetic algorithm. The test results show that the hybrid heuristic algorithm is more efficient than the genetic algorithm, the sensitivity analysis studies the influence of some parameters to the final solution and the Uncertainty–Sensitivity justification tool is used to evaluate the assumptions.     

A scenario planning approach for the flood emergency logistics preparation problem under uncertainty

This paper aims to develop a decision-making tool that can be used by government agencies in planning for flood emergency logistics. In this article, the flood emergency logistics problem with uncertainty is formulated as two stochastic programming models that allow for the determination of a rescue resource distribution system for urban flood disasters. The decision variables include the structure of rescue organizations, locations of rescue resource storehouses, allocations of rescue resources under capacity restrictions, and distributions of rescue resources. By applying the data processing and network analysis functions of the geographic information system, flooding potential maps can estimate the possible locations of rescue demand points and the required amount of rescue equipment. The proposed models are solved using a sample average approximation scheme. Finally, a real example of planning for flood emergency logistics is presented to highlight the significance of the proposed model as well as the efficacy of the proposed solution strategy.     

A bi-objective model for planning and managing rail-truck intermodal transportation of hazardous materials

We propose a bi-objective optimization framework for routing rail-truck intermodal shipments with hazardous materials, when shippers and receivers have access to alternate intermodal terminals. A tabu-search based solution methodology is developed, which together with the optimization framework is applied to realistic size problem instances to gain managerial insights. Our analysis indicates that drayage accounts for a significant portion of transport risk and that it can be reduced by scheduling direct and faster trains; and, that the mix of intermodal trains depends on the interest of the decision-makers, where the resulting traffic can facilitate planning emergency response systems.     

A strategic planning model for the railway system accident rescue problem

The present study examines the location of emergency rescue problems for urban ambulance and railway emergency systems. The proposed model considers probabilistic rescue demand, independent busy fractions of ambulances, and the corresponding risk levels in railway segments. We formulate the proposed model using fuzzy multi-objective programming and solve it using a generic algorithm and a non-dominated sorting genetic algorithm-II. Computation results are analyzed by applying the model to a real-world Taiwan railway system. Analytical results demonstrate that a proper adjustment of the rescue resource location improves rescue effectiveness for railway rescue and urban medical service demand.     

A bilevel storage pricing model for outbound containers in a dry port system

Serving as a potential solution for seaport congestion and capacity limitation, dry port development is increasingly popular in the freight transport industry. This paper pioneers the research on dry port operations by modelling the storage pricing problem for outbound containers. The interaction between a dry port and multiple shippers is modelled as a bilevel program. The optimal properties of the proposed model under certain conditions are derived analytically, from which a closed-form solution is obtained. Contrary to intuition, the increase of container delivery frequency from shippers may lead to the reduction of dry port’s profit according to model outcomes.     

Real-time mobilization decisions for multi-priority emergency response resources and evacuation groups: Model formulation and solution

This paper presents a model formulation and solution for simultaneous mobilization destination, traffic assignment, and departure schedule for multi-priority groups (SMDTS-MPG) for real-time emergency response in no-notice disasters. The presented approach addresses the decision context in which multiple emergency responses and evacuation flow groups with different destinations and varying priorities coexist in the same traffic network, within which simultaneous mobilization strategies must consider this requirement. The proposed modeling technique and the cell transmission model (CTM)-based linear-programming model provides a mechanism to accomplish this goal in an intuitive and coherent manner. The proposed matrix formulation of the SMDTS-MPG model permits the rapid deployment of the model to large networks. Model formulation and numerical examples are presented in detail in this paper.     

A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

A Generalized Nash Equilibrium network model for post-disaster humanitarian relief

We develop a Generalized Nash Equilibrium network model for post-disaster humanitarian relief by nongovernmental organizations (NGOs). NGOs derive utility from providing relief supplies to victims of the disaster at demand points in a supply chain context while competing with each other for financial funds provided by donations. The shared constraints consist of lower and upper bounds for demand for relief items at the demand points to reduce materiel convergence or congestion. This game theory problem is reformulated as an optimization problem and numerical examples and a theoretical case study on Hurricane Katrina given.     

A MILP model for planning the trips of dynamic positioned tankers with variable travel time

The transportation of the crude oil produced in offshore oilfields to onshore terminals is performed by vessels, known as shuttle tankers. Scheduling shuttle-tanker operations entails solving complex problems to ensure a timely offloading of the platforms, taking into account several logistics and inventory constraints. This work proposes a new MILP formulation that advances previous works by considering variable travel time between platforms and terminals. The combination of the MILP formulation with an optimization solver constitutes a decision-support tool to aid engineers reach optimal decisions for a planning horizon. To handle large-scale instances, rolling-horizon and relax-and-fix strategies are proposed.     

A latent class choice based model system for railway optimal pricing and seat allocation

In this paper, discrete choice methods in the form of multinomial logit and latent class models are proposed to explain ticket purchase timing of passenger railway. The choice model and demand functions are incorporated into a revenue optimization problem which jointly considers pricing and seat allocation. The framework provides insightful policy implications in term of fare and capacity distribution derived from actual passenger behavior. It shows that accepting short-haul demand provides greater revenue than long-haul demand using the same capacity. Revenue improvement ranges from 16.24% to 24.96% in multinomial logit models and from 13.82% to 21.39% in latent class models respectively.     

A hierarchical clustering and routing procedure for large scale disaster relief logistics planning

We describe a hierarchical cluster and route procedure (HOGCR) for coordinating vehicle routing in large-scale post-disaster distribution and evacuation activities. The HOGCR is a multi-level clustering algorithm that groups demand nodes into smaller clusters at each planning level, enabling the optimal solution of cluster routing problems. The routing problems are represented as capacitated network flow models that are solved optimally and independently by CPLEX on a parallel computing platform. The HOGCR preserves the consistency among parent and child cluster solutions obtained at consecutive levels. We assess the performance of the algorithm by using large scale scenarios and find satisfactory results.     

A quantum-inspired model for human-automation trust in air traffic controllers derived from functional Magnetic Resonance Imaging and correlated with behavioural indicators

Steady growth in air traffic has resulted in a greater prevalence in automation aids as far as the field of Air Traffic Management is concerned. This has ensued in human factors, particularly trust becoming an essential point of consideration in Air Traffic Controller (ATCO)-automation teams. An undertaking to better embody trust behaviours in ATCOs was attempted by coalescing two schools of thought on trust using the principles of superposition and complementarity from quantum mechanics. This model was further refined with behavioural indicators from the experiment. Brain imaging verification of this synchronised coexistence of both philosophies was established with the use of functional Magnetic Resonance Imaging (fMRI) data, where ATCOs were given conflict detection tasks with the aid of ATS-CAP software that was able to generate credible flight plans with visible waypoints and airports. Data from self-reported questionnaires have been useful in building generalised models of trust. However, the robustness of the model that has been proposed in this paper is higher than generalised models because of the utilisation of unbiased data to represent specifically ATCO trusting behaviour under uncertainty. This is an improvement on current models that are also context-dependent and based on subjective data.