N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It is shown that the N-list-enhanced Constructive Heuristic algorithm performs significantly better than the current best-performing algorithm and three new metaheuristics in terms of both solution quality and computational time. It can, therefore, be considered a competitive benchmark for future studies on distributed production scheduling and computing

Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments

In the current work we introduce a novel estimation of distribution algorithm to tackle a hard combinatorial optimization problem, namely the single-machine scheduling problem, with uncertain delivery times. The majority of the existing research coping with optimization problems in uncertain environment aims at finding a single sufficiently robust solution so that random noise and unpredictable circumstances would have the least possible detrimental effect on the quality of the solution. The measures of robustness are usually based on various kinds of empirically designed averaging techniques. In contrast to the previous work, our algorithm aims at finding a collection of robust schedules that allow for a more informative decision making. The notion of robustness is measured quantitatively in terms of the classical mathematical notion of a norm on a vector space. We provide a theoretical insight into the relationship between the properties of the probability distribution over the uncertain delivery times and the robustness quality of the schedules produced by the algorithm after a polynomial runtime in terms of approximation ratios

Mixed integer programming and adaptive problem solver learned by landscape analysis for clinical laboratory scheduling

This paper attempts to derive a mathematical formulation for real-practice clinical laboratory scheduling, and to present an adaptive problem solver by leveraging landscape structures. After formulating scheduling of medical tests as a distributed scheduling problem in heterogeneous, flexible job shop environment, we establish a mixed integer programming model to minimize mean test turnaround time. Preliminary landscape analysis sustains that these clinics-orientated scheduling instances are difficult to solve. The search difficulty motivates the design of an adaptive problem solver to reduce repetitive algorithm-tuning work, but with a guaranteed convergence. Yet, under a search strategy, relatedness from exploitation competence to landscape topology is not transparent. Under strategies that impose different-magnitude perturbations, we investigate changes in landscape structure and find that disturbance amplitude, local-global optima connectivity, landscape's ruggedness and plateau size fairly predict strategies' efficacy. Medium-size instances of 100 tasks are easier under smaller-perturbation strategies that lead to smoother landscapes with smaller plateaus. For large-size instances of 200-500 tasks, extant strategies at hand, having either larger or smaller perturbations, face more rugged landscapes with larger plateaus that impede search. Our hypothesis that medium perturbations may generate smoother landscapes with smaller plateaus drives our design of this new strategy and its verification by experiments. Composite neighborhoods managed by meta-Lamarckian learning show beyond average performance, implying reliability when prior knowledge of landscape is unknown

Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times

The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project’s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, -indicator and empirical attaintment functions

A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Copyright @ Springer-Verlag 2010.Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

A greedy heuristic approach for the project scheduling with labour allocation problem

Responding to the growing need of generating a robust project scheduling, in this article we present a greedy algorithm to generate the project baseline schedule. The robustness achieved by integrating two dimensions of the human resources flexibilities. The first is the operators’ polyvalence, i.e. each operator has one or more secondary skill(s) beside his principal one, his mastering level being characterized by a factor we call “efficiency”. The second refers to the working time modulation, i.e. the workers have a flexible time-table that may vary on a daily or weekly basis respecting annualized working strategy. Moreover, the activity processing time is a non-increasing function of the number of workforce allocated to create it, also of their heterogynous working efficiencies. This modelling approach has led to a nonlinear optimization model with mixed variables. We present: the problem under study, the greedy algorithm used to solve it, and then results in comparison with those of the genetic algorithms

Robust multiobjective optimisation for fuzzy job shop problems

Abstract In this paper we tackle a variant of the job shop scheduling problem with uncertain task durations modelled as fuzzy numbers. Our goal is to simultaneously minimise the schedule's fuzzy makespan and maximise its robustness. To this end, we consider two measures of solution robustness: a predictive one, prior to the schedule execution, and an empirical one, measured at execution. To optimise both the expected makespan and the predictive robustness of the fuzzy schedule we propose a multiobjective evolutionary algorithm combined with a novel dominance-based tabu search method. The resulting hybrid algorithm is then evaluated on existing benchmark instances, showing its good behaviour and the synergy between its components. The experimental results also serve to analyse the goodness of the predictive robustness measure, in terms of its correlation with simulations of the empirical measure.This research has been supported by the Spanish Government under Grants FEDER TIN2013-46511-C2-2-P and MTM2014-55262-P

A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algorithm uses a fixed meme, denoting a hill climbing operator, to improve each solution in a population during the evolutionary search process. Given global parameters and multiple parametrised operators, adaptation often becomes a crucial constituent in the design of MAs. In this study, a self-adaptive self-configuring steady-state multimeme memetic algorithm (SSMMA) variant is proposed. Along with the individuals (solutions), SSMMA co-evolves memes, encoding the utility score for each algorithmic component choice and relevant parameter setting option. An individual uses tournament selection to decide which operator and parameter setting to employ at a given step. The performance of the proposed algorithm is evaluated on six combinatorial optimisation problems from a cross-domain heuristic search benchmark. The results indicate the success of SSMMA when compared to the static Mas as well as widely used self-adaptive Multimeme Memetic Algorithm from the scientific literature