Comparative analysis of order batching and routing problem in the picking regarding classical HVRP (heterogeneous vehicle routing problem)

Este artículo tiene como objetivo comparar la conformación de lotes con ruteo, en la preparación de pedidos respecto al problema HVRP (Heterogeneous Vehicle Routing Problem) basándose en la utilización de una metodología de la revisión sistemática de la literatura. Del análisis comparativo se identifica la necesidad de realizar modificaciones radicales e incluir nuevos componentes al problema HVRP, para modelar la conformación de lotes con ruteo de mínimo tiempo, en la preparación de pedidos, considerando K equipos de manejo de materiales (EMM) heterogéneos, n productos, m posiciones de almacenamiento, la disponibilidad del inventario y demás restricciones asociadas a la operación.This paper aims to compare the order batching and routing problem(OBRP) regarding heterogeneous vehicle routing problem (HVRP), in order to identify whether there are any differences and similarities between these ones. The OBRP consist in generating product groups, which are collected from storage locations using material specific handling equipment. Each product group(or batch) is matched to a route, which states the sequences to pick the products in the shortest time possible. On the other hand, HVRP is a variant of the Vehicle Routing Problem(VRP), in which customers are served by a heterogeneous fleet of vehicles with various capacities, in order to delivery products in a distribution network at the lowest possible cost. Additionally, in the related literature were not identified HVRP papers that tackled order batching and routing problem (OBRP), but they were focused primarily in transportation and distribution process. Therefore, it was detected a gap in the state of the art. The comparation analysis was developed using a variation of the methodology called Systematic Literature Review (SLR) , which was based on analysis of papers. This methodology was implemented eight stages, the most important of which are as follows: i) formulating the research questions and evaluation criteria (stage 2), ii) inclusion and exclusion criteria (stage 3), iii) results of systematic review (stage 6), iv) comparative analysis between OBRP and HVRP based on set evaluation criteria (stage 7) and v) conclusions and research opportunities (stage 8). The main findings of this paper were as follow: First, order batching was not modeled in HVRP, hence relevance of this gap. Second, in order batching and routing problem is necessary to represent K heterogeneous MHE with different speed travels, load capacities and lift heights. In HVRP papers the heterogeneity is only caused by vehicles in different load capacities. Third, a constraint among n products, m storage locations and K heterogeneous EMH should be implemented to ensure the feasibility of solutions of OBRP. This constraint is raised, since any MHE are not able to pick some products from storage locations, due to theirs technical characteristics. In addition, none of HVRP papers represented this constraint. Fourth, setup time and handling time were not modeled in reviewed HVRP papers, since these times were not as significant in transportation and distribution routes. Therefore, these times should be included in HVRP to represent OBRP. Fifth, available of inventory were not considered in HVRP papers, since this condition was not important in the modeled process. It should be noted that this condition is critical in OBRP, since only can be picked products with available inventory in Distribution Centre (DC). Based on findings, it was detected a significant gap in the state of the art related to the formulation and solution a minimum time OBRP considering n products, m storage locations and K heterogeneous MHE and described constraint. Therefore, this approach, not only it will fill this gap, but also contribute to knowledge in OBRP. In addition, this paper it will be one of the first to analyze HVRP in warehouse and DC

A Mixed Integer Programming Formulation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

The heterogeneous fixed fleet open vehicle routing problem (HFFOVRP) is one of the most significant extension problems of the open vehicle routing problem (OVRP). The HFFOVRP is the problem of designing collection routes to a number of predefined nodes by a fixed fleet number of vehicles with various capacities and related costs. In this problem, the vehicle doesn’t return to the depot after serving the last customer. Because of its numerous applications in industrial and service problems, a new model of the HFFOVRP based on mixed integer programming is proposed in this paper. Furthermore, due to its NP-hard nature, an ant colony system (ACS) algorithm was proposed. Since there were no existing benchmarks, this study generated some test problems. From the comparison with the results of exact algorithm, the proposed algorithm showed that it can provide better solutions within a comparatively shorter period of time

Artificial Intelligence in Business: A Literature Review and Research Agenda

The rise of artificial intelligence (AI) technologies has created promising research opportunities for the information systems (IS) discipline. Through applying latent semantic analysis, we examine the correspondence between key themes in the academic and practitioner discourses on AI. Our findings suggest that business academic research has predominantly focused on designing and applying early AI technologies, while practitioner interest has been more diverse. We examine these differences in the socio-technical continuum context and relate existing literature on AI to core IS research areas. In doing so, we identify existing research gaps and propose future research directions for IS scholars related to AI and organizations, AI and markets, AI and groups, AI and individuals, and AI development

A Hybrid Heuristic for a Broad Class of Vehicle Routing Problems with Heterogeneous Fleet

We consider a family of Rich Vehicle Routing Problems (RVRP) which have the particularity to combine a heterogeneous fleet with other attributes, such as backhauls, multiple depots, split deliveries, site dependency, open routes, duration limits, and time windows. To efficiently solve these problems, we propose a hybrid metaheuristic which combines an iterated local search with variable neighborhood descent, for solution improvement, and a set partitioning formulation, to exploit the memory of the past search. Moreover, we investigate a class of combined neighborhoods which jointly modify the sequences of visits and perform either heuristic or optimal reassignments of vehicles to routes. To the best of our knowledge, this is the first unified approach for a large class of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants. The efficiency of the algorithm is evaluated on 643 well-known benchmark instances, and 71.70\% of the best known solutions are either retrieved or improved. Moreover, the proposed metaheuristic, which can be considered as a matheuristic, produces high quality solutions with low standard deviation in comparison with previous methods. Finally, we observe that the use of combined neighborhoods does not lead to significant quality gains. Contrary to intuition, the computational effort seems better spent on more intensive route optimization rather than on more intelligent and frequent fleet re-assignments

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

Problemas de conformación de lotes con ruteo en el acomodo y la preparación de pedidos considerando K equipos heterogéneos

Esta tesis doctoral tiene como objetivo modelar problemas de conformación de lotes con ruteo en el acomodo y la preparación de pedidos. En la preparación de pedidos, se modeló un problema que considera K equipos de materiales (EMMateriales) heterogéneos, n productos, m posiciones de almacenamiento y la condición de disponibilidad del inventario, lo cual permite superar las limitaciones detectadas en la literatura científica. De otra parte, al revisar el estado del arte del acomodo no se identificaron problemas que abordan la formación de lotes con ruteo, sino que únicamente solucionan el problema de asignación de posiciones de almacenamiento (Slotting). Para solucionar la conformación de lotes en cada uno de los problemas modelados, se desarrollaron reglas de prioridad y metaheurísticos basados en búsqueda local inteligente. En tanto, para resolver el ruteo se modelaron metaheurísticos de búsqueda tabú híbridos con estrategias de intensificación y diversificación. Las reglas y metaheurísticos para cada problema, se integraron a un WMS (Warehouse Management System) y fueron modelados considerando las características y restricciones particulares de estas operaciones del Centro de Distribución (CEDI). Diseños de experimentos (Designs of Experiment_DoE) de parcelas divididas no sólo fueron implementados para validar los problemas modelados sino también para realizar un análisis numérico exhaustivo que permitió identificar las combinaciones de niveles de los factores significativos que generan los menores tiempos promedios totales y la mayor eficiencia en el acomodo y la preparación de pedidos. El uso de diseños de parcelas divididas puede considerarse un aporte metodológico al estado del arte, ya que este enfoque no fue detectado en la literatura para resolver este tipo de problemas. A partir del análisis experimental se identificó que los metaheurísticos para la formación de lotes, INS (Intelligent Neighboorhood Search) y PNS (Picking Neighboorhood Search), generaron las mejores soluciones para el acomodo y la preparación de pedidos. De otra parte, los metaheurísticos híbridos RABTIN (Ruteo acomodo búsqueda tabú intensificación) y RUPBTDI (Ruteo preparación de pedidos búsqueda tabú diversificación) 3-Opt Inserción produjeron las mejores soluciones para el ruteo de estas operaciones.Abstract: The aim of doctoral thesis is to model and valid the order batching and routing problem in the putaway and picking operation. In the picking operation was modelled a problem, took in account K Material Handling Equipment, n products, m storage locations and a condition of available inventory. This approach contributed to overcome the gaps detected in the scientific literature. On the other hand, a significant contribution to the knowledge was achieved in the putaway operation since the batching and routing problems had not been detected in the systematic literature review (SLR) results. This is raised because the slotting problem was only approach identified for the putaway operation in the scientific literature. Priority rules and metaheuristics based on intelligent local search were developed to solve the order batching component in each modelled problem. Meanwhile, hybrid tabu search metaheuristics with intensification and diversification strategies were implemented to sort out routing component in the putaway and picking problem. In addition, the priority rules and metaheuristics developed not only were integrated a WMS (Warehouse Management System) but these were modelled considering the specific characteristics and constrains of the putaway and picking operation. A DOE (Design of Experiment) named Split Plot not only was implemented to valid the modelled problems but also performed an exhaustive numeric analysis, enabled to identify the combination of significant factors levels that generated the shortest average times and the higher efficiency in the putaway and picking operation. Split plot design could be considered a methodological contribution to the state of the art since this approach was not identified in the scientific literature for solving is kind of problems. Based on results of experiments it was detected that INS (Intelligent Neighboorhood Search) and PNS (Picking Neighboorhood Search) metaheuristics generated the best solutions for solving the order batching component in the putaway and picking operation. Meanwhile, the hybrid metaheuristics called RABTIN (Putaway routing using intensification strategy with tabu serach) and RUPBTDI (Picking routing using diversification strategy with tabu search) 3-Opt produced the best solution for sorting out the routing component of these operations.Doctorad