Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems

The Three-Objective Optimization Model of Flexible Workshop Scheduling Problem for Minimizing Work Completion Time, Work Delay Time, and Energy Consumption

In recent years, the optimal design of the workshop schedule has received much attention with the increased competition in the business environment. As a strategic issue, designing a workshop schedule affects other decisions in the production chain. The purpose of this thesis is to design a three-objective mathematical model, with the objectives of minimizing work completion time, work delay time and energy consumption, considering the importance of businesses attention to reduce energy consumption in recent years. The developed model has been solved using exact solution methods of Weighted Sum (WS) and Epsilon Constraint (Ɛ) in small dimensions using GAMS software. These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems. The standard BRdata set of problems were used to investigate the algorithms performance in solving these problems so that it is possible to compare the algorithms performance of this research with the results of the algorithms used by other researchers. The obtained results show the relatively appropriate performance of these algorithms in solving these problems and also the much better and more optimal performance of the NSGA-II algorithm compared to the performance of the SFLA algorithm

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms

ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time

A Literature Review of Cuckoo Search Algorithm

Optimization techniques play key role in real world problems. In many situations where decisions are taken based on random search they are used. But choosing optimal Optimization algorithm is a major challenge to the user. This paper presents a review on Cuckoo Search Algorithm which can replace many traditionally used techniques. Cuckoo search uses Levi flight strategy based on Egg laying Radius in deriving the solution specific to problem. CS optimization algorithm increases the efficiency, accuracy, and convergence rate. Different categories of the cuckoo search and several applications of the cuckoo search are reviewed. Keywords: Cuckoo Search Optimization, Applications , Levy Flight DOI: 10.7176/JEP/11-8-01 Publication date:March 31st 202

Relevance and Applicability of Multi-objective Resource Constrained Project Scheduling Problem: Review Article

Resource-Constrained Project Scheduling Problem (RCPSP) is a Non Polynomial (NP) - Hard optimization problem that considers how to assign activities to available resources in order to meet predefined objectives. The problem is usually characterized by precedence relationship between activities with limited capacity of renewable resources. In an environment where resources are limited, projects still have to be finished on time, within the approved budget and in accordance with the preset specifications. Inherently, these tend to make RCPSP, a multi-objective problem. However, it has been treated as a single objective problem with project makespan often recognized as the most relevant objective. As a result of not understanding the multi-objective dimension of some projects, where these objectives need to be simultaneously considered, distraction and conflict of interest have ultimately lead to abandoned or totally failed projects. The aim of this article is to holistically review the relevance and applicability of multi-objective performance dimension of RCPSP in an environment where optimal use of limited resources is important

Integrated Models and Algorithms for Automotive Supply Chain Optimization

The automotive industry is one of the most important economic sectors, and the efficiency of its supply chain is crucial for ensuring its profitability. Developing and applying techniques to optimize automotive supply chains can lead to favorable economic outcomes and customer satisfaction. In this dissertation, we develop integrated models and algorithms for automotive supply chain optimization. Our objective is to explore methods that can increase the competitiveness of the automotive supply chain via maximizing efficiency and service levels. Based on interactions with an automotive industry supplier, we define an automotive supply chain planning problem at a detailed operational level while taking into account realistic assumptions such as sequence-dependent setups on parallel machines, auxiliary resource assignments, and multiple types of costs. We model the research problem of interest using mixed-integer linear programming. Given the problem’s NP-hard complexity, we develop a hybrid metaheuristic approach, including a constructive heuristic and an effective encoding-decoding strategy, to minimize the total integrated cost of production setups, inventory holding, transportation, and production outsourcing. Furthermore, since there are often conflicting objectives of interest in automotive supply chains, we investigate simultaneously optimizing total cost and customer service level via a multiobjective optimization methodology. Finally, we analyze the impact of adding an additional transportation mode, which offers a cost vs. delivery time option to the manufacturer, on total integrated cost. Our results demonstrate the promising performance of the proposed solution approaches to analyze the integrated cost minimization problem to near optimality in a timely manner, lowering the cost of the automotive supply chain. The proposed bicriteria, hybrid metaheuristic offers decision makers several options to trade-off cost with service level via identified Pareto-optimal solutions. The effect of the available additional transportation mode’s lead time is found to be bigger than its cost on the total integrated cost measure under study

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system