Joaquín Bautista

Ant algorithms for a time and space constrained assembly line balancing problem

The present article focuses on the application of a procedure based on ant colonies to solve an assembly line balancing problem. After an introduction to assembly line problems, the problem under study is presented: the Time and Space constrained Assembly Line Balancing Problem (TSALBP); and a basic model of one of its variants is put forward for study. Subsequently, an ant algorithm is presented that incorporates some ideas that have offered good results with simple balancing problems. Finally, the validity of the proposed algorithms is tested by means of a computational experience with reference instances, and the conclusions of the study are presented.

HEURISTICS AND EXACT ALGORITHMS FOR SOLVING THE MONDEN PROBLEM

Abstract Sequencing units on an assembly line in order to obtain a regular requirement of resources is a problem that can be modelled in a variety of ways. One of the most popular is known as the Monden problem, and the heuristic proposed to obtain a ‘satisfactory’ solution is called ‘goal-chasing’ ethod. In the paper the myopic behaviour of this heuristic is shown, and some improvements are proposed. An exact procedure, based on BDP, is also proposed. By relaxing the assumptions, the BDP procedure becomes a new, powerful heuristic. A sample of computational results is included.

A dynamic programming based heuristic for the assembly line balancing problem

The simple assembly line balancing problem is the simplification of a real problem associated to the assignment of the elementary tasks required for assembly of a product in an assembly line. This problem has been extensively studied in the literature for more than half a century. The present work proposes a new procedure to solve the problem we call Bounded Dynamic Programming. This use of the term Bounded is associated not only with the use of bounds to reduce the state space but also to the reduction of such space based on heuristics. This procedure is capable of obtaining an optimal solution rate of 267 out of 269 instances, which have been used in previous works, thus obtaining the best-known performance for the problem. These results are an improvement from any previous procedure found in the literature even when using smaller computing times.

Solving an urban waste collection problem using ants heuristics

This paper describes the methodology that we have applied for the solution of an urban waste collection problem in the municipality of Sant Boi de Llobregat, within the metropolitan area of Barcelona (Spain). The basic nature of the considered problem is that of a capacitated arc routing problem, although it has several specific characteristics, mainly derived from traffic regulations. We present the model that we have built for the problem, which results after an appropriate transformation of the problem into a node routing one. We also present the ant colonies heuristics that we have used to obtain the solutions to the problem. These combine constructive methods, based on nearest neighbor and on nearest insertion, with a local search that explores various neighborhoods. The application of the proposed methods gives results that improve considerably the ones that were previously used in the municipality.

Ant Algorithms for Assembly Line Balancing

The present work is focused on the assembly line balancing design problems whose objective is to minimize the number of stations needed to manufacture a product in a line given a fixed cycle time, equivalent to a fixed production rate. The problem is solved using an ACO metaheuristic implementation with different features, obtaining good results. Afterwards, an adaptation of the previous implementation is used to solve a real case problem found in a bike assembly line with a hierarchical multi-objective function and additional constraints between tasks.

Multiobjective constructive heuristics for the 1/3 variant of the time and space assembly line balancing problem: ACO and random greedy search

In this work we present two new multiobjective proposals based on ant colony optimisation and random greedy search algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. Some variants of these algorithms have been compared in order to find out the impact of different design configurations and the use of heuristic information. Good performance is shown after applying every algorithm to 10 well-known problem instances in comparison to NSGA-II. In addition, those algorithms which have provided the best results have been employed to tackle a real-world problem at the Nissan plant, located in Spain.

Solving mixed model sequencing problem in assembly lines with serial workstations with work overload minimisation and interruption rules

In this manuscript, we present a formulation for the MMSP-W (Mixed model sequencing problem with workload minimisation) for production lines with serial workstations. We demonstrate the validity of the basic models in the presence of a control system on the production line that allows the stopping of operations with no restrictions. We propose an extension of the basic models that allows conditioned interruption of operations to facilitate line management. We then propose a procedure to solve the proposed problem through BDP (Bounded Dynamic Programming), and demonstrate its validity through a computational experiment with reference instances and a case study linked to the Nissan powertrain plant in Barcelona.

Including different kinds of preferences in a multi-objective ant algorithm for time and space assembly line balancing on different Nissan scenarios

Most of the decision support systems for balancing industrial assembly lines are designed to report a huge number of possible line configurations, according to several criteria. In this contribution, we tackle a more realistic variant of the classical assembly line problem formulation, time and space assembly line balancing. Our goal is to study the influence of incorporating user preferences based on Nissan automotive domain knowledge to guide the multi-objective search process with two different aims. First, to reduce the number of equally preferred assembly line configurations (i.e., solutions in the decision space) according to Nissan plants requirements. Second, to only provide the plant managers with configurations of their contextual interest in the objective space (i.e., solutions within their preferred Pareto front region) based on real-world economical variables. We face the said problem with a multi-objective ant colony optimisation algorithm. Using the real data of the Nissan Pathfinder engine, a solid empirical study is carried out to obtain the most useful solutions for the decision makers in six different Nissan scenarios around the world.

A GRASP algorithm to solve the unicost set covering problem

The set covering problem (SCP) is a well-known combinatorial optimization problem. This paper presents a GRASP algorithm to solve a special SCP case known in the literature as the unicost set covering problem. The algorithm incorporates a local improvement procedure based on the heuristics to solve binary constraint satisfiability problems (SAT). The quality of the proposed algorithm is tested on a set of reference instances, comparing the obtained results with those found in the literature. Our algorithm improves the best-known solutions for many of these instances.

Local search heuristics for the assembly line balancing problem with incompatibilities between tasks

This paper deals with the assembly line balancing problem considering incompatibilities between the tasks with the aim of: first minimizing the number of workstations, and then minimizing the cycle time for the minimum number of workstations. In order to solve the problem we propose the use of a greedy randomized adaptive search procedure obtained from the application of some classic heuristics based on priority rules, and a genetic algorithm that searches for the solution in the heuristic space. A computational experience is included to illustrate the performance of the proposed approach.