Marketing Mix Optimization with Practical Constraints
- URL: http://arxiv.org/abs/2101.03663v1
- Date: Mon, 11 Jan 2021 02:10:19 GMT
- Title: Marketing Mix Optimization with Practical Constraints
- Authors: Hsin-Chan Huang and Jiefeng Xu and Alvin Lim
- Abstract summary: We address a variant of the marketing mix optimization (MMO) problem which is commonly encountered in many industries.
Given the size of a realistic problem in the industrial setting, the state-of-the-art integer programming solvers may not be able to solve the problem to optimality.
We propose a systematic reformulation to ease the computational burden.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we address a variant of the marketing mix optimization (MMO)
problem which is commonly encountered in many industries, e.g., retail and
consumer packaged goods (CPG) industries. This problem requires the spend for
each marketing activity, if adjusted, be changed by a non-negligible degree
(minimum change) and also the total number of activities with spend change be
limited (maximum number of changes). With these two additional practical
requirements, the original resource allocation problem is formulated as a mixed
integer nonlinear program (MINLP). Given the size of a realistic problem in the
industrial setting, the state-of-the-art integer programming solvers may not be
able to solve the problem to optimality in a straightforward way within a
reasonable amount of time. Hence, we propose a systematic reformulation to ease
the computational burden. Computational tests show significant improvements in
the solution process.
Related papers
- Purchase and Production Optimization in a Meat Processing Plant [0.9074663948713615]
This paper addresses an optimization problem concerning the purchase and subsequent material processing.<n>We design a simple iterative approach based on integer linear programming that allows us to solve real-life instances.<n>The results obtained using real data from the meat processing company showed that our algorithm can find the optimum solution in a few seconds.
arXiv Detail & Related papers (2025-07-14T11:05:46Z) - ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research [53.736407871322314]
We introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning.<n>Our approach emulates human cognition, implementing an end-to-end workflow that transforms requirements into mathematical models and executable code.<n>It is currently being tested internally in Lenovo's AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers.
arXiv Detail & Related papers (2025-06-02T05:11:21Z) - Learning to Optimize for Mixed-Integer Non-linear Programming [20.469394148261838]
Mixed-integer non-NLP programs (MINLPs) arise in various domains, such as energy systems and transportation, but are notoriously difficult to solve.
Recent advances in machine learning have led to remarkable successes in optimization, area broadly known as learning to optimize.
We propose two differentiable correction layers that generate integer outputs while preserving gradient.
arXiv Detail & Related papers (2024-10-14T20:14:39Z) - Sample-Efficient Multi-Agent RL: An Optimization Perspective [103.35353196535544]
We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation.
We introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs.
We show that our algorithm provides comparable sublinear regret to the existing works.
arXiv Detail & Related papers (2023-10-10T01:39:04Z) - Language Models for Business Optimisation with a Real World Case Study in Production Scheduling [3.224702011999591]
Large Language Models (LLMs) have demonstrated outstanding performance across different language-related tasks.
We present an LLM-based framework for automating problem formulation in business optimisation.
arXiv Detail & Related papers (2023-09-22T23:45:21Z) - Multiobjective variational quantum optimization for constrained
problems: an application to Cash Management [45.82374977939355]
We introduce a new method for solving optimization problems with challenging constraints using variational quantum algorithms.
We test our proposal on a real-world problem with great relevance in finance: the Cash Management problem.
Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima.
arXiv Detail & Related papers (2023-02-08T17:09:20Z) - Symmetric Tensor Networks for Generative Modeling and Constrained
Combinatorial Optimization [72.41480594026815]
Constrained optimization problems abound in industry, from portfolio optimization to logistics.
One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space.
In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
arXiv Detail & Related papers (2022-11-16T18:59:54Z) - Learning Adaptive Evolutionary Computation for Solving Multi-Objective
Optimization Problems [3.3266268089678257]
This paper proposes a framework that integrates MOEAs with adaptive parameter control using Deep Reinforcement Learning (DRL)
The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization.
We show the learned policy is transferable, i.e., the policy trained on a simple benchmark problem can be directly applied to solve the complex warehouse optimization problem.
arXiv Detail & Related papers (2022-11-01T22:08:34Z) - A Framework for Inherently Interpretable Optimization Models [0.0]
Solution of large-scale problems that seemed intractable decades ago are now a routine task.
One major barrier is that the optimization software can be perceived as a black box.
We propose an optimization framework to derive solutions that inherently come with an easily comprehensible explanatory rule.
arXiv Detail & Related papers (2022-08-26T10:32:00Z) - Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds
Global Optima [114.31577038081026]
We propose an efficient method that tackles the designer's and agents' problems simultaneously in a single loop.
Although the designer does not solve the equilibrium problem repeatedly, it can anticipate the overall influence of the incentives on the agents.
We prove that the algorithm converges to the global optima at a sublinear rate for a broad class of games.
arXiv Detail & Related papers (2021-10-04T06:53:59Z) - Knowledge engineering mixed-integer linear programming: constraint
typology [2.4002205752931625]
We investigate the constraint typology of mixed-integer linear programming MILP formulations.
MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, timetabling optimization problems.
arXiv Detail & Related papers (2021-02-20T20:07:24Z) - Offline Model-Based Optimization via Normalized Maximum Likelihood
Estimation [101.22379613810881]
We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points.
This problem setting emerges in many domains where function evaluation is a complex and expensive process.
We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
arXiv Detail & Related papers (2021-02-16T06:04:27Z) - Boosting Data Reduction for the Maximum Weight Independent Set Problem
Using Increasing Transformations [59.84561168501493]
We introduce new generalized data reduction and transformation rules for the maximum weight independent set problem.
Surprisingly, these so-called increasing transformations can simplify the problem and also open up the reduction space to yield even smaller irreducible graphs later in the algorithm.
Our algorithm computes significantly smaller irreducible graphs on all except one instance, solves more instances to optimality than previously possible, is up to two orders of magnitude faster than the best state-of-the-art solver, and finds higher-quality solutions than solvers DynWVC and HILS.
arXiv Detail & Related papers (2020-08-12T08:52:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.