Scheduling the truckload operations in automatic warehouses

doi:10.1016/j.ejor.2005.03.066

European Journal of Operational Research

Volume 179, Issue 3, 16 June 2007, Pages 723-735

https://doi.org/10.1016/j.ejor.2005.03.066 Get rights and content

Abstract

This work presents a scheduling problem that arises in an automatic storage/retrieval warehouse system AS/RS involving the scheduling of the truck load operations. The truck loading operations are modelled as job shop problem with recirculation. The loads are considered as jobs, the pallets of a load are seen as the job’s operations. The forklifts are the machines. The minimization of the makespan allows minimizing the idle time of the warehouse’s equipments.

A procedure based on genetic algorithms is presented to sequence the pallets of a set of loads that are prepared simultaneously. The genetic algorithm includes specific knowledge of the problem to improve its efficiency. This work presents interesting computational results for the minimization of the makespan.

Introduction

Automatic storage equipments must be efficient in order to justify the investment they imply and also to provide an alternative to conventional storage systems. The efficiency of an automatic storage system depends, among other factors, on the planning of the load operations of the trucks. In the AS/RS (Automated Storage and Retrieval System) warehouses, which perform on a daily basis a large number of truckloads, it is necessary to plan and execute accurately the loading procedures in order to fulfill the delivery deadlines.

This article describes the application of a metaheuristic to a real problem that arises within the domain of loads’ dispatch inside an automatic warehouse. An effective and efficient genetic algorithm is presented to sequence the pallets’ retrieval aiming to maximize the warehouse throughput and fulfill the delivery deadlines. The article is organized in the following way: in the next section, is described the functioning of the automatic warehouse and the planning problem is identified; in the third section, is presented the adopted model, some remarks are made about its application and some extensions to the model are presented; the forth section is dedicated to the characterization of the adopted solution’s methodology; the fifth section presents computational results of the developed algorithm; and in the end, the conclusions about the work are discussed.

Section snippets

Description of the storage system

The warehouse works as distribution center. It stores the products of the factory and sends the customers’ orders by truck. The route of a truck is established previously in agreement with the customers’ orders that are being transported. This information is known in advance. Eleven aisles of pallets racks compose the main body of the warehouse, with capacity for 40,000 pallets. There is an automatic stacker crane (also S/R machine, operating in dual command mode) in each aisle to move the

Modelling as a job shop problem

The problem described in the sections above can be modelled as a JSP. The loads represent the jobs that have to be processed. Each transportation for each of the pallets represents a job operation that is carried out by one of the forklifts truck that represents the machines.

Essentially, in the JSP, each job has a technological constraint that determines a specific order to process the job’s operations, and it is also necessary to guarantee that there is no overlap, in time, of the processing

Proposed methodology

In this work we adopted method based on genetic algorithms. This technique’s simplicity and easiness to model more complex problems and its easy integration with other optimization methods were factors that were considered for its choice. The algorithm proposed was conceived to solve the JSP, with or without recirculation.

Computational experiments

Computational experiments were carried out with representative instances of the real problem and with test instances known for the job shop problem. The representative instances of the real problem were generated randomly and they concern a job-shop problem with recirculation. The dimension of these instances corresponds to the defined maximum dimension of the real problem. Instances Jr11, Jr12, Jr13, Jr14 and Jr15 are constituted by 13 jobs (docking bay number) and 35 operations per jobs (one

Conclusions

This paper presents a model for scheduling load operations in an automatic warehouse and a genetic algorithm to solve either the JSP with recirculation or the classical JSP. The schedules are built using information given by the genetic algorithm to order the operations.

The algorithm incorporates a local search procedure with the shape of a genetic operator. This operator allows to include specific knowledge of the problem and makes the algorithm very efficient. This algorithm allows, with

Acknowledgements

This text was written and edited in English with the help of Carina Barbosa and Pedro Ribeiro, within the graduate degree of Applied Foreign Languages of the University of Minho, Portugal.

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Scheduling the truckload operations in automatic warehouses

Abstract

Introduction

Section snippets

Description of the storage system

Modelling as a job shop problem

Proposed methodology

Computational experiments

Conclusions

Acknowledgements

Parallel Computing

European Journal of Operational Research

European Journal of Operations Research

European Journal of Operational Research

Annals of Discrete Mathematics

OR-Library: Distributing test problems by electronic mail

Journal of the Operational Research Society

A GRASP for job shop scheduling

The job shop scheduling problem: Conventional and new solution techniques

European Journal of Operational Research

Travel-time models for automated storage/retrieval systems

IIE Transactions

Programação de operações em sistemas com tempo de processamento variável

Pesquisa Operacional

Operations sequencing in automated warehousing systems

International Journal of Production Research

Sequencing and Scheduling—An Introduction to the Mathematics of the Job-Shop