A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem

Abstract

In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research.

Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues

“…Most of the articles mentioned above employ simulation to varying extents to validate algorithms developed for their AGV-related problems. Simulation facilitates a better understanding of reality's complexity, reducing the necessity for the oversimplifications and unrealistic assumptions sometimes required in analytical models [35]. Discrete-event simulation, in particular, has proven to be a valuable tool for modeling AGV-based transport systems, such as AGV traffic management policies, or, as in the current study, AGV task dispatching/sequencing rules [36].…”

“…Leveraging such a simulator enables the realistic simulation of dynamic queues resulting from stochastic processing times. Furthermore, enhancing the interaction between the algorithmic component and simulation could allow for more integrated simulation-optimization frameworks, often referred to as simheuristics [35,47]. This integration holds promise for efficiently deriving high-quality and robust solutions that consider the stochastic nature inherent in warehouse and production processes.…”

Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance

“…The rapid development of electronic commerce in recent years has posed substantial implications and challenges for warehouse operation management. Given the demanding timeliness and large-scale, diverse, small-batch nature of customer orders, the urgent resolution of the problem of implementing precise and efficient order picking operations is a critical concern for every warehouse enterprise [1,2]. The MRPS, operating as a semiautomated picking system, provides a fresh way to address this problem [3].…”

“…(1) A novel idea of "customer orders → racks usage frequency → racks location optimization" is proposed to effectively model and solve the RLOP. (2) The MABBD algorithm is proposed to solve the integer programming model, and two lower-bound generation methods are designed based on the characteristics of RLOP, which enriches the theoretical research in the field of warehousing optimization. (3) A Memetic algorithm is specifically developed to address large-scale instances.…”

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

“…The potential of these simulators can be greatly amplified by integrating them with advanced optimization or machine learning tools. Connecting DES platforms with external programming languages such as Python or R can enhance simulation modeling by leveraging additional mathematical and algorithmic capabilities, thereby enabling more sophisticated analyses and insights [2].…”