The Relationship between the Construction and Solution of the MILP Models and Applications

The automatic search method based on Mix-integer Linear Programming (MILP) is one of the most common tools to search the distinguishers of block ciphers. For differential analysis, the byte-oriented MILP model is usually used to count the number of differential active s-boxes and the bit-oriented MILP model is used to search the optimal differential characteristic. In this paper, we present the influences between the construction and solution of MILP models solved by Gurobi : 1). the number of variables; 2). the number of constraints; 3). the order of the constraints; 4). the order of variables in constraints. We carefully construct the MILP models according to these influences in order to find the desired results in a reasonable time. As applications, we search the differential characteristic of PRESENT,GIFT-64 and GIFT-128 in the single-key setting. We do a dual processing for the constraints of the s-box. It only takes 298 seconds to finish the search of the 8-round optimal differential characteristic based on the new MILP model. We also obtain the optimal differential characteristic of the 9/10/11-round PRESENT. With a special initial constraint, it only takes 4 seconds to obtain a 9-round differential characteristic with probability $2^{-42}$. We also get a 12/13-round differential characteristic with probability $2^{-58}/2^{-62}$. For GIFT-128, we improve the probability of differential characteristic of $9 \sim 21$ rounds and give the first attack on 26-round GIFT-128 based on a 20-round differential characteristic with probability $2^{-121.415}$

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)

Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of preventing and reducing substance abuse. While effective, unfortunately, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. This paper investigates the problem of optimizing the social influence related to the deviant behavior via careful construction of the intervention groups. We propose a Mixed Integer Optimization formulation that decides on the intervention groups, captures the impact of the groups on the structure of the social network, and models the impact of these changes on behavior propagation. In addition, we propose a scalable hybrid meta-heuristic algorithm that combines Mixed Integer Programming and Large Neighborhood Search to find near-optimal network partitions. Our algorithm is packaged in the form of GUIDE, an AI-based decision aid that recommends intervention groups. Being the first quantitative decision aid of this kind, GUIDE is able to assist practitioners, in particular social workers, in three key areas: (a) GUIDE proposes near-optimal solutions that are shown, via extensive simulations, to significantly improve over the traditional qualitative practices for forming intervention groups; (b) GUIDE is able to identify circumstances when an intervention will lead to deviancy training, thus saving time, money, and effort; (c) GUIDE can evaluate current strategies of group formation and discard strategies that will lead to deviancy training. In developing GUIDE, we are primarily interested in substance use interventions among homeless youth as a high risk and vulnerable population. GUIDE is developed in collaboration with Urban Peak, a homeless-youth serving organization in Denver, CO, and is under preparation for deployment