Mariano Frutos

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

The Flexible Job-Shop Scheduling Problem is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying several parallel goals. We introduce a Memetic Algorithm, based on the NSGAII (Non-Dominated Sorting Genetic Algorithm II) acting on two chromosomes, to solve this problem. The algorithm adds, to the genetic stage, a local search procedure (Simulated Annealing). We have assessed its efficiency by running the algorithm on multiple objective instances of the problem. We draw statistics from those runs, which indicate that this Memetic Algorithm yields good and low-cost solutions.

Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

A Multi-objective Memetic Algorithm for the Job-Shop Scheduling Problem

Planning means, in the realm of production activities, to design, coordinate, manage and control all the operations involved in the production system. Many MOPs (multi-objective optimization problems) are generated in this framework. They require the optimization of several functions that are usually very complex, which makes the search for solutions very expensive. Multi-objective optimization seeks Pareto-optimal solutions for these problems. In this work we introduce, a Multi-objective Memetic Algorithm intended to solve a very important MOP in the field, namely, the Job-Shop Scheduling Problem. The algorithm combines a MOEA (Multi-Objective Evolutionary Algorithm) and a path-dependent search algorithm (Multi-objective Simulated Annealing), which is enacted at the genetic phase of the procedure. The joint interaction of those two components yields a very efficient procedure for solving the MOP under study. In order to select the appropriate MOEA both NSGAII and SPEAII as well as their predecessors (NSGA and SPEA) are pairwise tested on problems of low, medium and high complexity. We find that NSGAII yields a better performance, and therefore is the MOEA of choice.

Bus Network Optimization Through Time-Dependent Hybrid Algorithm

This paper focuses on a new hybrid technique that combines a genetic algorithm with simulation to solve the bus-network scheduling problem (BNSP). The BNSP has several factors that complicate both the problem formulation and the selection of efficient algorithms for its resolution. This problem is challenging because not only the BNSP is NP-complete, but also the existing methods fail to contemplate environment dependent dynamic variables. The hybrid algorithm proposed in this article comprises two stages: a modified GRASP (greedy randomized adaptive search procedures) as an initialization method, and the genetic algorithm with simulation to find the values of the environment- dependent dynamic variables. The final goal consisted in designing a meta-heuristic technique that yields an adequate scheduling to solve this general problem. The BNSP, chosen as case study, satisfies both the demand and the offer of transport. The method was applied to a solution of experimental examples with good results.

Industry 4.0: Smart Scheduling

Smart Manufacturing and Industry 4.0 production environments integrate the physical and decisional aspects of manufacturing processes into autonomous and decentralised systems. One of the main aspects in these systems is production planning, in particular scheduling operations on machines. We introduce here a new decision-making schema, Smart Scheduling, intended to yield flexible and efficient production schedules on the fly, taking advantage of the features of these new environments. The ability to face unforeseen and disruptive events is one of the main improvements in our proposed schema, which uses an efficient screening procedure (Tolerance Scheduling) to lessen the need of rescheduling in the face of those events.

The Dominance Flow Shop Scheduling Problem

Abstract We introduce a new line of analysis of Flow Shop scheduling problems, for the case of two jobs and assuming that processing times are unknown. The goal is to determine the domination relations between permutation and non-permutation schedules. We analyze the structural and dominance properties that ensue in this setting, based on the critical paths of schedules.

Visual Attractiveness in Routing Problems: a Review

Abstract Enhancing visual attractiveness in a routing plan has proven to be an effective way to facilitate practical implementation and positive collaboration among planning and operational levels in transportation. Several authors, driven by the requests of practitioners, have considered, either explicitly or implicitly, such aspect in the optimization process for different routing applications. However, due to its subjective nature, there is not a unique way of evaluating the visual attractiveness of a routing solution. The aim of this paper is to provide an overview of the literature on visual attractiveness. In particular, we analyze and experimentally compare the different metrics that were used to model the visual attractiveness of a routing plan and provide guidelines that planners and researchers can use to select the method that better suits their needs.

An Improved Hybrid Algorithm for Stochastic Bus-Network Design

The purpose of this work is to present the elastic hybrid algorithm, a method that deals in a realistic manner with the bus-network design problem. The novel technique integrates a Floyd–Warshall initialization method, a multiobjective evolutionary algorithm based on the strength Pareto evolutionary algorithm 2, and a simulation procedure. The Floyd–Warshall procedure initializes the distances and routes between each pair of bus stops. The evolutionary stage obtains several quasi-optimal bus networks, with the help of a simulation procedure that calculates the values of the environmentally dependent dynamic variables associated with the user. The method was successfully tested with a real-life case study, and its relevance was assessed after it was compared with other authors’ works. As a conclusion subsequent to several experimental stages, it can be confirmed that the elastic hybrid algorithm achieves highly competitive results compared to those from the literature, while obtaining solutions that exhibit a strong similarity to various real features of the problem under study.

Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes

To schedule production in a Job-Shop environment means to allocate adequately the available resources. It requires to rely on efficient optimization procedures. In fact, the JobShop Scheduling Problem (JSSP) is a NP-Hard problem (Ullman, 1975), so ad-hoc algorithms have to be applied to its solution (Frutos et al., 2010). This is similar to other combinatorial programming problems (Olivera et al., 2006), (Cortes et al., 2004). Most instances of the Job-Shop Scheduling Problem involve the simultaneous optimization of two usually conflicting goals. This one, like most multi-objective problems, tends to have many solutions. The Pareto frontier reached by an optimization procedure has to contain a uniformly distributed number of solutions close to the ones in the true Pareto frontier. This feature facilitates the task of the expert who interprets the solutions (Kacem et al., 2002). In this paper we present a Genetic Algorithm linked to a Simulated Annealing procedure able to schedule the production in a Job-Shop manufacturing system (Cortes et al., 2004), (Tsai & Lin, 2003), (Wu et al., 2004), (Chao-Hsien & Han-Chiang, 2009).