Solving Multiplicative Programs by Binary-encoding the Multiplication Operation

Multiplicative programs in the form of maximization and/or minimization have numerous applications in conservation planning, game theory, and multi-objective optimization settings. In practice, multiplicative programs are challenging to solve because of their multiplicative objective function (a product of continuous or integer variables). These challenges are twofold: 1. As the number of factors in the objective … Read more

The Magic of Nash Social Welfare in Optimization: Do Not Sum, Just Multiply!

In this paper, we explain some key challenges when dealing with a single/multi-objective optimization problem in practice. To overcome these challenges, we present a mathematical program that optimizes a Nash Social Welfare function. We refer to this mathematical program as the Nash Social Welfare Program (NSWP). An interesting property of the NSWP is that it … Read more

Multi-objective Optimization Based Algorithms for Solving Mixed Integer Linear Minimum Multiplicative Programs

We present two new algorithms for a class of single-objective non-linear optimization problems, the so-called Mixed Integer Linear minimum Multiplicative Programs (MIL-mMPs). This class of optimization problems has a desirable characteristic: a MIL-mMP can be viewed as a special case of the problem of optimization over the efficient set in multi-objective optimization. The proposed algorithms … Read more

Query Batching Optimization in Database Systems

Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong … Read more

A bi-level branch-and-bound algorithm for the capacitated competitive facility location problem

Competitive facility location problem is a typical facility locating optimization problem but in a competitive environment. The main characteristic of this problem is the competitive nature of the market. In essence, the problem involves two competitors, i.e., a leader and a follower, who seek to attract customers by establishing new facilities to maximize their own … Read more

Exact Solution Approaches for Integer Linear Generalized Maximum Multiplicative Programs Through the Lens of Multi-objective Optimization

We study a class of single-objective nonlinear optimization problems, the so-called Integer Linear Generalized Maximum Multiplicative Programs (IL-GMMP). This class of optimization problems has a significant number of applications in different fields of study including but not limited to game theory, systems reliability, and conservative planning. An IL-GMMP can be reformulated as a mixed integer … Read more

Learning to Project in Multi-Objective Binary Linear Programming

In this paper, we investigate the possibility of improving the performance of multi-objective optimization solution approaches using machine learning techniques. Specifically, we focus on multi-objective binary linear programs and employ one of the most effective and recently developed criterion space search algorithms, the so-called KSA, during our study. This algorithm computes all nondominated points of … Read more

Hybrid Rebalancing with Dynamic Hubbing for Free-floating Bike Sharing Using Multi-objective Simulation Optimization

For rebalancing problem of free-floating bike sharing systems, we propose dynamic hubbing (i.e. dynamically determining geofencing areas) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulation optimization approach. Given historical usage data and real-time bike GPS location information, dynamic geofenced areas (hubs) are determined to encourage users … Read more

MSEA.jl: A Multi-Stage Exact Algorithm for Bi-objective Pure Integer Linear Programming in Julia

We present a new exact method for bi-objective pure integer linear programming, the so-called Multi-Stage Exact Algorithm (MSEA). The method combines several existing exact and approximate algorithms in the literature to compute the entire nondominated frontier of any bi-objective pure integer linear program. Each algorithm available in MSEA has multiple versions in the literature. Hence, … Read more

Best subset selection of factors affecting influenza spread using bi-objective optimization

A typical approach for computing an optimal strategy for non-pharmaceutical interventions during an influenza outbreak is based on statistical ANOVA. In this study, for the first time, we propose to use bi-objective mixed integer linear programming. Our approach employs an existing agent-based simulation model and statistical design of experiments presented in Martinez and Das (2014) … Read more