A lexicographical dynamic flow model for relief operations

Emergency management is a highly relevant area of interest in operations research. Currently the area is undergoing widespread development. Furthermore, recent disasters have highlighted the importance of disaster management, in order to alleviate the suffering of vulnerable people and save lives. In this context, the problem of designing plans for the distribution of humanitarian aid according to the preferences of the decision maker is crucial. In this paper, a lexicographical dynamic flow model to solve this problem is presented, extending a previously introduced static flow model. The new model is validated in a realistic case study and a computational study is performed to compare both models, showing how they can be coordinated to improve their overall performance

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Supported Evacuation for Disaster Relief through Lexicographic Goal Programming

Disasters have been striking human-beings from the beginning of history and their management is a global concern of the international community. Minimizing the impact and consequences of these disasters, both natural and human-made, involves many decision and logistic processes that should be optimized. A crucial logistic problem is the evacuation of the affected population, and the focus of this paper is the planning of supported evacuation of vulnerable people to safe places when necessary. A lexicographic goal programming model for supported evacuation is proposed, whose main novelties are the classification of potential evacuees according to their health condition, so that they can be treated accordingly; the introduction of dynamism regarding the arrival of potential evacuees to the pickup points, according to their own susceptibility about the disaster and the joint consideration of objectives such us number of evacuated people, operation time and cost, among which no trade-off is possible. The performance of the proposed model is evaluated through a realistic case study regarding the earthquake and tsunami that hit Palu (Indonesia) in September 2018

Introducing Molly: Distributed Memory Parallelization with LLVM

Programming for distributed memory machines has always been a tedious task, but necessary because compilers have not been sufficiently able to optimize for such machines themselves. Molly is an extension to the LLVM compiler toolchain that is able to distribute and reorganize workload and data if the program is organized in statically determined loop control-flows. These are represented as polyhedral integer-point sets that allow program transformations applied on them. Memory distribution and layout can be declared by the programmer as needed and the necessary asynchronous MPI communication is generated automatically. The primary motivation is to run Lattice QCD simulations on IBM Blue Gene/Q supercomputers, but since the implementation is not yet completed, this paper shows the capabilities on Conway's Game of Life

MSIGT: Most Significant Index Generation Technique for cloud environment

Cloud Computing is a computing paradigm for delivering computational power, storage and applications as services via Internet on a pay-as-you-go basis to consumers. The data owner outsources local data to the public cloud server to reduce the cost of the data management. Critical data has to be encrypted to ensure privacy before outsourcing. The state-of-the-art SSE schemes search only over encrypted data through keywords, hence they do not provide effective data utilisation for large dataset files in cloud. We propose a Most Significant Index Generation Technique (MSIGT), that supports secure and efficient index generation time using a Most Significant Digit (MSD) radix sort. MSD radix sort is simple and faster in sorting array strings. A mathematical model is developed to encrypt the indexed keywords for secure index generation without the overhead of learning from the attacker/cloud provider. It is seen that the MSIGT scheme can reduce the cost of data on owner side to O(NT × 3) with a score calculation of O(NT). The proposed scheme is effective and efficient in comparison with the existing algorithms

Dynamic shipments of inventories in shared warehouse and transportation networks

In shared warehouse and transportation networks, dynamic shipments of inventories are carried out based on up-to-date inventory information. This paper studies the effect of network structures on optimal decision-making. We propose a discrete time modeling framework with stochastic demand, capturing a wide variety of network structures. Using Markov decision processes, we obtain optimal order and dynamic shipment decisions for small networks. We compare optimal solutions of different four-node network structures. Results indicate product characteristics significantly influence the effectiveness of network structures. Surprisingly, two-echelon networks are occasionally costlier than any other network. Moreover, dynamic shipments yield considerable gains over static shipments

A New Ant Colony-Based Methodology for Disaster Relief

Humanitarian logistics in response to large scale disasters entails decisions that must be taken urgently and under high uncertainty. In addition, the scarcity of available resources sometimes causes the involved organizations to suffer assaults while transporting the humanitarian aid. This paper addresses the last mile distribution problem that arises in such an insecure environment, in which vehicles are often forced to travel together forming convoys for security reasons. We develop an elaborated methodology based on Ant Colony Optimization that is applied to two case studies built from real disasters, namely the 2010 Haiti earthquake and the 2005 Niger famine. There are very few works in the literature dealing with problems in this context, and that is the research gap this paper tries to fill. Furthermore, the consideration of multiple criteria such as cost, time, equity, reliability, security or priority, is also an important contribution to the literature, in addition to the use of specialized ants and effective pheromones that are novel elements of the algorithm which could be exported to other similar problems. Computational results illustrate the efficiency of the new methodology, confirming it could be a good basis for a decision support tool for real operations

Two-stage network design in humanitarian logistics.

Natural disasters such as floods and earthquakes can cause multiple deaths, injuries, and severe damage to properties. In order to minimize the impact of such disasters, emergency response plans should be developed well in advance of such events. Moreover, because different organizations such as non-governmental organizations (NGOs), governments, and militaries are involved in emergency response, the development of a coordination scheme is necessary to efficiently organize all the activities and minimize the impact of disasters. The logistics network design component of emergency management includes determining where to store emergency relief materials, the corresponding quantities and distribution to the affected areas in a cost effective and timely manner. In a two-echelon humanitarian relief chain, relief materials are pre-positioned first in regional rescue centers (RRCs), supply sources, or they are donated to centers. These materials are then shipped to local rescue centers (LRCs) that distribute these materials locally. Finally, different relief materials will be delivered to demand points (also called affected areas or AAs). Before the occurrence of a disaster, exact data pertaining to the origin of demand, amount of demand at these points, availability of routes, availability of LRCs, percentage of usable pre-positioned material, and others are not available. Hence, in order to make a location-allocation model for pre-positioning relief material, we can estimate data based on prior events and consequently develop a stochastic model. The outputs of this model are the location and the amount of pre-positioned material at each RRC as well as the distribution of relief materials through LRCs to demand points. Once the disaster occurs, actual values of the parameters we seek (e.g., demand) will be available. Also, other supply sources such as donation centers and vendors can be taken into account. Hence, using updated data, a new location-allocation plan should be developed and used. It should be mentioned that in the aftermath of the disaster, new parameters such as reliability of routes, ransack probability of routes and priority of singular demand points will be accessible. Therefore, the related model will have multiple objectives. In this dissertation, we first develop a comprehensive pre-positioning model that minimizes the total cost while considering a time limit for deliveries. The model incorporates shortage, transportation, and holding costs. It also considers limited capacities for each RRC and LRC. Moreover, it has the availability of direct shipments (i.e., shipments can be done from RRCs directly to AAs) and also has service quality. Because this model is in the class of two-stage stochastic facility location problems, it is NP-hard and should be solved heuristically. In order to solve this model, we propose using Lagrangian Heuristic that is based on Lagrangian Relaxation. Results from the first model are amounts and locations of pre-positioned relief materials as well as their allocation plan for each possible scenario. This information is then used as a part of the input for the second model, where the facility location problem will be formulated using real data. In fact, with pre-positioned items in hand, other supplies sources can be considered as necessary. The resulting multi-objective problem is formulated based on a widely used method called lexicography goal programming. The real-time facility location model of this dissertation is multi-product. It also considers the location problem for LRCs using real-time data. Moreover, it considers the minimization of the total cost as one of the objectives in the model and it has the availability of direct shipments. This model is also NP-hard and is solved using the Lagrangian Heuristic. One of the contributions of this dissertation is the development of Lagrangian Heuristic method for solving the pre-positioning and the real- time models. Based on the results of Lagrangian Heuristic for the pre-positioning model, almost all the deviations from optimal values are below 5%, which shows that the Heuristics works acceptably for the problem. Also, the execution times are no more than 780 seconds for the largest test instances. Moreover, for the real-time model, though not directly comparable, the solutions are fairly close to optimal and the execution time for the largest test instance is below 660 seconds. Hence, the efficiency of the heuristic for real-time model is satisfactory