A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system

Abstract

Goal: This study developed a structured decision model capable of solving the storage location assignment problem (SLAP) in a picker-to-parts system, using multiples key performance indicators (KPIs). Design / Methodology / Approach: A hybrid approach was developed. For that, a Multi-Objective Genetic Algorithm (MOGA) was used considering three fitness functions, but more functions may be considered. Through MOGA it was possible to verify a high number of solutions and reduce it into a Pareto frontier. After that, a Multiple-Criteria Decision-Making (MCDM) approach was used to choose the best solution. Results: This model was able to find viable solutions considering multiples objectives, warehouse restrictions and decision makers' preferences, and the required processing time for the simulated cases was insignificant. Limitations of the investigation: One limitation of this work was the consideration of known and predictable data. Practical implications: The proposed model was developed with the purpose of assisting companies that face this type of problem, providing a solution for SLAP requiring the minimum information and operational actions. Originality / Value: SLAP is a NP (Non-Deterministic Polynomial time) complex problem and, after the MOGA, the number of solution can be still high for the final decision making by the engineering manager (decision maker-DM). Thus, the MOGA-MCDM hybrid approach developed was able incorporate the DM' preferences into a compensatory view, vetoing alternatives that were worse in any of the KPIs, to recommend a final solution.

Hybrid Genetic Algorithms for Order Assignment and Batching in Picking System: A Systematic Literature Review

“…Fontana et al [14] proposed an innovative hybrid decision model designed to address the Storage Location Assignment Problem (SLAP), amalgamating Multiple Objective Genetic Algorithms (MOGA) with Multi-Criteria Decision Making (MCDM). Results from simulations indicated that the additive-veto model assists decision makers in scrutinizing the Pareto front and incorporating qualitative criteria.…”

Planning and picking in small warehouses under industry-relevant constraints

“…For businesses, large or small, some conditions affect performance, such as: limits on design because of available space; existence of middle aisles [ 19 ] and wide aisles [ 20 ]; product deterioration while in storage [ 21 ]; accounting for product returns [ 22 ]; safety constraints, such as a minimum distance between the storage locations of two items; convenience constraints, as when two products should be within a certain distance because it is likely that they are ordered together; precedence constraints [ 3 , 23 , 24 ], which impose that certain products should be among the first to be picked, for being heavy, and that others should be among the last to be picked, for being light or fragile; traffic constraints, with one-way aisles in parts of the warehouse; order due time constraints [ 25 , 26 ]; ergonomic considerations [ 27 ] and the risk of infections [ 28 ]. Rigorous, optimal solutions are generally beyond the capabilities of current computers and many publications use heuristic approaches (e.g., [ 29 ]) and/or empirical optimization methods inspired by physical analogies, such as simulated annealing [ 30 ], or biological analogies, such as the genetic algorithm [ 31 – 33 ], ant colony optimization [ 34 – 36 ], and particle swarm optimization [ 11 , 37 ], including the development of hybrid methods [ 38 , 39 ] that use artificial neural networks [ 40 , 41 ] to provide quick answers. There is no guarantee that such algorithms will locate the global minimum of the objective function but computer implementations of several of them are freely available or may be readily coded.…”