Self-adaptive global best harmony search algorithm for training neural networks

AbstractThis paper addresses the application of Self-adaptive Global Best Harmony Search (SGHS) algorithm for the supervised training of feed-forward neural networks (NNs). A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on two classification benchmarking problems. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm is compared with other harmony search algorithms and the standard back-propagation algorithm. The experiments presented that the proposed algorithm lends itself very well to training of NNs and it is also highly competitive with the compared methods

A multi-agent framework for load consolidation in logistics

Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and software engineering, are providing effective mechanisms for modelling dynamic systems generally operating under unpredictable environments and having a high degree of complex interactions. It seems that multi-agent paradigms have big potential for handling complex problems in land transportation logistics. Based on this motivation, the paper proposes a multi-agent based framework for load consolidation problems of third-party logistics companies

A PRACTICAL FUZZY DIGRAPH MODEL FOR MODELING MANUFACTURING FLEXIBILITY

There are many approaches in the literature to model and quantify manufacturing flexibility. Most of these models were developed to quantify several aspects of manufacturing flexibility like machine flexibility, routing flexibility, mix flexibility, volume flexibility, etc. This is mainly due to the fact that developing a generic model, which can be used to measure different types of flexibilities, is not straightforward. Recently, a generic flexibility measure, which is based on digraphs and permanent index, was proposed by the author. The main difficulty with that model like in all other flexibility models is the inability to collect precise data for computing the flexibility. In order to overcome this difficulty, a practical fuzzy linguistic approach is incorporated into the previous digraph model in this article. The extended fuzzy digraph model is explained in detail through an example in the present article

"GRAPH THEORY" AND "MATRIX METHOD" BASED APPROACH FOR BUSINESS PROCESS MODELING/SIMULATION SOFTWARE SELECTION

It is possible to provide practical solutions to many complex problems by making use of process modeling. For example; it is possible to predict whether an investment will be beneficial or production speed will improve or unit prices will decrease in case of adding a new machine to a process. Similarly; it is possible to investigate whether a new work practice will improve productivity and analyze its benefit level, bottlenecks in the system, cost increasing and/or non-value adding activities. Moreover, it is possible to provide effective solutions by generating alternative process chains by modeling processes. Several software developed to enable effective process modeling. In order to realize the mentioned benefits it is critical to select a suitable modeling software. In the present study a multiple criteria decision making model is proposed for selecting such software. Graph theory and matrix method is utilized for model development. This method is not well known in the literature. However it has some desirable properties like ability to model criteria interactions, hierarchy and evaluate fuzzy judgments. The current paper presents the first multiple criteria model for evaluating and selecting process modeling software. It also presents the first application of graph theory-matrix method to this problem

Gene expression programming based meta-modelling approach to production line design

Fierce competition in today's economy forces companies to fully optimize their processes in order to supply customers with high-quality products on time with lowest possible cost. Designing optimal production lines is a major step ahead in satisfying customer needs. Owing to the stochastic and highly nonlinear nature of the production lines, their optimal design is not easy and requires usage of advanced tools and techniques. In the present paper one of the new generation soft computing technique that is known as gene expression programming (GEP) is used to develop a meta-model from extensive simulation experiments for the multiple objective design of a production line. The developed meta-model is used to optimize production line design with multiple objective tabu search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to model and solve production line design problems

Goal programming using multiple objective tabu search

Goal programming (GP) is one of the most commonly used mathematical programming tools to model multiple objective optimisation (MOO) problems, There are numerous MOO problems of various complexity modelled using GP in the literature, One of the main difficulties in the GP is to solve their mathematical formulations optimally. Due to difficulties imposed by the classical solution techniques there is a trend in the literature to solve mathematical programming formulations including goal programmes, using the modern heuristics optimisation techniques, namely genetic algorithms (GA), tabu search (TS) and simulated annealing (SA). This paper uses the multiple objective tabu search (MOTS) algorithm, which was proposed previously by the author to solve Gl? models. In the proposed approach, GP models are first converted to their classical MOO equivalent by using some simple conversion procedures. Then the problem is solved using the MOTS algorithm. The results obtained from the computational experiment show that MOTS can be considered as a promising candidate tool for solving GP models

Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems

The task of balancing of assembly lines is of considerable industrial importance. It consists of assigning operations to workstations in a production line in such a way that (1) no assembly precedence constraint is violated, (2) no workstations in the line takes longer than a predefined cycle time to perform all tasks assigned to it, and (3) as few workstations as possible are needed to perform all the tasks in the set. This paper presents a new multiple objective simulated annealing (SA) algorithm for simple (line) and U type assembly line balancing problems with the aim of maximizing "smoothness index" and maximizing the "line performance" (or minimizing the number of workstations). The proposed algorithm makes use of task assignment rules in constructing feasible solutions. The proposed algorithm is tested and compared with literature test problems. The proposed algorithm found the optimal solutions for each problem in short computational times. A detailed performance analysis of the selected task assignment rules is also given in the paper

MOAPPS 1.0: aggregate production planning using the multiple-objective tabu search

In recent years, there has been a trend in the research community to solve large-scale complex planning and design problems using the modern heuristics optimization techniques (i.e. tabu search, genetic algorithms, etc.). This is mainly due to unsuitability of the classical solution techniques in many circumstances. Depending upon the assumptions made and the modelling approach used, aggregate production planning (APP) problems can be quite complex and large scale. Therefore, there is a need to investigate the suitability of modern heuristics for their solution. In this paper, the multiple-objective APP problem is formulated as a pre-emptive goal-programming model and solved by a specially developed multiple-objective tabu search algorithm. The mathematical formulation is built upon Masud and Hwang's model (original model) due to its extensibility characteristics. The present model extents their model by including subcontracting and setup decisions. The multiple-objective tabu search algorithm is applied to both the original and extended model. Results obtained from the solution of the original model are then compared. It is observed that the multiple-objective tabu search algorithm can be used as an alternative solution mechanism for solving APP problems. During this study, an object-oriented program is also developed using C++. This software is named as MOAPPS 1.0 (Multiple Objective Aggregate Production Planning Software)

Linguistic-based meta-heuristic optimization model for flexible job shop scheduling

In this paper, a linguistic based meta-heuristic modelling and solution approach for solving the Flexible Job Shop Scheduling Problem (FJSSP) is presented. FJSSP is an extension of the classical job-shop scheduling problem. The present problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that a predefined performance measure is optimized. The scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem) in the present study. Moreover, instead of using operations to represent product processing requirements and machine processing capabilities, machine independent capability units, which are known as Resource Elements (RE), are used. Representation of unique and shared capability boundaries of machine tools and part processing requirements is possible via RE. Using REs in scheduling can also reduce the problem size. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls. Using these controls and the Giffler and Thompson (1960) priority rule-based heuristic, a simulated annealing algorithm is developed to solve FJSSP. This novel approach simplifies the modelling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its solution. The results obtained from the computational study have shown that the proposed algorithm can solve this complex problem effectively within reasonable time. The results have also given some insights on the effect of the selection of dispatching rules and the flexibility level on the job shop performance. It is observed that the effect of dispatching rule selection on the job shop performance diminishes by increasing the job shop flexibility

Quantifying machine flexibility

There are several studies aiming to quantify several aspects of flexibility in manufacturing systems like routing flexibility, product mix flexibility, volume flexibility, etc. However, there is still a need to develop more generic measures that can be used to quantify flexibility of systems in order to enable decision-makers to reach better decisions in selecting between different system configurations. In this study, a new approach which is based on digraph theory and matrix algebra is proposed to quantify flexibility. Several examples are also provided to illustrate the proposed approach and its practicality and usefulness. The proposed approach is a novel one and can be used to model and quantify several types of flexibilities. In this research, the proposed modelling approach is explained through machine flexibility mainly due to the fact that most of the other flexibility types in manufacturing systems rely on this flexibility type