A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Abstract

In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

“…However, this information can provide extra value if it is combined with other information. For example, feed manufacturers may better plan their operations if they know the inventory levels of their clients [ 36 ]. From a farmer’s perspective, the enrichment of statistical growth models with sensor data that estimate the feed intake may provide valuable estimates of the expected growth evolution of a batch of pigs.…”

A variable neighborhood search simheuristic algorithm for reliability optimization of smart grids under uncertainty

“…Simheuristics (Juan et al., 2015; Chica et al., 2020), as a simulation‐optimization approach, combine simulation with metaheuristics to solve stochastic combinatorial optimization problems. Application areas of simheuristics include transportation and logistics (Juan et al., 2014; Reyes‐Rubiano et al., 2017; Juan et al., 2018, 2019; Gruler et al., 2020; Raba et al., 2020; Mara et al., 2021; Villarinho et al., 2021), finance (Panadero et al., 2020; Saiz et al., 2021), healthcare (Fikar et al., 2016), waste collection (Gruler et al., 2017b, 2017a), and cloud computing (Mazza et al., 2018). For real‐worlds complex stochastic optimization problems, simheuristics should be considered as a “first‐resort” method (Chica et al., 2020), as it can handle reality in uncertain problems by simulation modeling, it can assess risk with ease, and a post‐run simulation output analysis can be made.…”

The Non-Smooth and Bi-Objective Team Orienteering Problem with Soft Constraints

“…Biased-randomized techniques induce a non-uniform random behavior in the heuristic by employing skewed probability distributions. These techniques have been widely used to solve different combinatorial optimization problems [34][35][36]. Thus, biased randomization allows for us to transform a deterministic heuristic into a probabilistic algorithm without losing the logic behind the original heuristic.…”