Reinaldo J. Moraga

Meta-RaPs approach for the 0-1 multidimensional Knapsack problem

A promising solution approach called Meta-RaPS is presented for the 0-1 Multidimensional Knapsack Problem (0-1 MKP). Meta-RaPS constructs feasible solutions at each iteration through the utilization of a priority rule used in a randomized fashion. Four different greedy priority rules are implemented within Meta-RaPS and compared. These rules differ in the way the corresponding pseudo-utility ratios for ranking variables are computed. In addition, two simple local search techniques within Meta-RaPS improvement stage are implemented. The Meta-RaPS approach is tested on several established test sets, and the solution values are compared to both the optimal values and the results of other 0-1 MKP solution techniques. The Meta-RaPS approach outperforms many other solution methodologies in terms of differences from the optimal value and number of optimal solutions obtained. The advantage of the Meta-RaPS approach is that it is easy to understand and easy to implement, and it achieves good results.

Stability analysis of the supply chain by using neural networks and genetic algorithms

Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology.

A Meta-RaPS for the early/tardy single machine scheduling problem

This paper investigates a meta-heuristic solution approach to the early/tardy single machine scheduling problem with common due date and sequence-dependent setup times. The objective of this problem is to minimise the total amount of earliness and tardiness of jobs that are assigned to a single machine. The popularity of just-in-time (JIT) and lean manufacturing scheduling approaches makes the minimisation of earliness and tardiness important and relevant. In this research the early/tardy problem is solved by Meta-RaPS (meta-heuristic for randomised priority search). Meta-RaPS is an iterative meta-heuristic which is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element. In this case a greedy heuristic, the shortest adjusted processing time, is modified by Meta-RaPS and the good solutions are improved by a local search algorithm. A comparison with the existing ETP solution procedures using well-known test problems shows Meta-RaPS produces better solutions in terms of percent difference from optimal. The results provide high quality solutions in reasonable computation time, demonstrating the effectiveness of the simple and practical framework of Meta-RaPS.

Using system dynamics, neural nets, and eigenvalues to analyse supply chain behaviour. A case study

This paper presents a new methodology to predict behavioural changes in manufacturing supply chains due to endogenous and/or exogenous influences in the short and long term horizons. Additionally, the methodology permits the identification of the causes that may induce a negative behaviour when predicted. Initially, a dynamic model of the supply chain is developed using system dynamics simulation. Using this model, a neural network is trained to make online predictions of behavioural changes at a very early decision making stage so that an enterprise would have enough time to respond and counteract any unwanted situations. Eigenvalue analysis is used to investigate any undesired foreseen behaviour, and principles of stability and controllability are used to study several decision configurations that eliminate or mitigate such behaviour. A case study of an actual electronics manufacturing company demonstrates how to apply this methodology and its real benefits for enterprises.

Using neural networks to monitor supply chain behaviour

Intelligent agents are expected to play an increasingly important role in Supply Chain Management (SCM) by automating event-tracking, trend-prediction and decision-making functions. In this paper, we proposed a new trend-prediction methodology that recognises behavioural patterns and predicts future performance based on those patterns. We used fuzzy Adaptive Resonance Theory (ART) Neural Networks (NNs) to build the patterns and BackPropagation NNs (BPNNs) to make the predictions. We based this methodology on System Dynamics (SD) models, which were used to train the NNs. We believe that our approach could be incorporated easily into a number of software agents. These agents could improve dramatically the capabilities of current dashboard-monitoring systems.

Disaster and prevention management for the NASA shuttle during lift‐off

Purpose – The main objective of this paper is to introduce the development of a decision‐support environment for space range safety. Simulation modeling can provide a good environment to support disaster and prevention management.Design/methodology/approach – The paper describes the different models and the processes to find the different knowledge sources. This will help determine emergency management procedures.Findings – This case study provides guidance and an example to follow for other problems in aerospace. There are important factors to consider in order to implement risk management in NASA.Research limitations/implications – There are several limitations; first debris effects need to be added.Practical implications – First, the paper provides a guide in order to persuade managers of the utilization of decision support systems based on geographical information systems. Second, it shows that there is open source software which can be used and integrated to make a more comprehensive environment. Val...

A meta-heuristic based on simulated annealing for solving multiple-objective problems in simulation optimization

This paper presents a new meta heuristic algorithm based on the search method called simulated annealing, and its application to solving multiobjective simulation optimization problems. Since the simulated annealing search method has been extensively applied as a modern heuristic to solve single objective simulation optimization problems, a modification to this method has been developed in order to solve multiobjective problems. The efficiency of this new algorithm was tested on a real case problem modeled under discrete simulation.

Meta-RaPS approach for solving the resource allocation problem

Project scheduling is an important planning function that involves scheduling activities of a project such that the total completion time for the entire project is minimized. In performing this function, one is often faced with the problem of limited resources in addition to considering the time element and precedence constraints of the project. The task of allocation of limited resources to competing activities further complicates the project scheduling procedure. Because of the fact that the Resource Allocation Problem is a very known and well-studied combinatorial problem, a number of heuristic rules can be found in literature. In this paper, authors present a new approach, Meta-RaPS (metaheuristic for randomized priority search), to address this combinatorial problem. This article will show experimental results using the Meta-RaPS approach on several well-known resource constrained project scheduling problem test sets.

Scheduling Blocking Flow Shops Using Meta-RaPS☆

Abstract A single machine that includes loading/unloading areas for each job processed, loading and unloading could be performed while the machine is running causing minimized jobs completion time with lowest machine idle time. This design requires special kind of scheduling technique to ensure the accomplishment of those objectives if jobs’ processing, loading and unloading times are varying. The machine is modelled as a flow shop with blocking constraint. This research focuses on finding a solution to schedule this special case of flow shop of more than two machines with objective of minimizing jobs maximum completion time (makespan). The proposed solution in this research includes using a newly developed meta-heuristic known as Meta-heuristic for Randomized Priority Search (Meta-RaPS). Meta-RaPS construction phase is applied with the use of NEH flow shop scheduling algorithm and would provide very good schedules. The suggested technique is evaluated in comparison to top performing current meta-heuristics and construction heuristics on the famous benchmark flow shop data set of Taillard20. The results would suggest that applying Meta-RaPS for this flow shop problem is a great choice for constructing solutions and would provide high quality solutions with opportunity of more improvement in further research.

A modified rapid access heuristic for flowshop scheduling problem

This paper presents a modified rapid access (MRA) heuristic to solve the flowshop scheduling problems. This new heuristic follows the fundamental idea of the original rapid access approach by forming a two-machine subproblem, but the processing times are determined by using an exponential weighting modifier for the original linear weighting scheme without additional computational effort, then the subproblem is solved by using Johnsons two-machine algorithm. The performance of MRA is tested using instances from the literature and compared with the performance of the original rapid access (RA). Results show that the MRA outperforms the original RA in large size problems by using specific value of parameter alpha. Factorial experiment and response surface methodology are applied to determine the best alpha value which is the main factor that impacts the performance of MRA and produce the best performance of the MRA heuristic.