maedeh mosayeb motlagh; Parham Azimi; maghsoud Amiri
Abstract
This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate ...
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This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered.
SEYYED ALI Mirnezhad; Parham Azimi; Ahmad Yousefi Hanoomarvar
Abstract
In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have ...
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In the present study, the redundancy allocation problem (RAP) of series-parallel system has been investigated to maximize the system's availability. To achieve the research objective, budget, weight and volume constraints, and the maximum and minimum number of elements assigned to each subsystem have been considered. The main innovation of this research is to consider the failure and repair rates of components with non-exponential distribution function in the process of optimization. Taking into account failure and repair rate via non-exponential distribution function makes it impossible to calculate accessibility using mathematical relations. Therefore, the present study has used simulation method to calculate system availability. Since the simulation has no optimization capability On the other hand, in the redundancy allocation problem, it is necessary to evaluate the system availability recurrently in order to find the optimal solution. Further, due to the high degree of difficulty of developed mathematical function, the genetic metaheuristic algorithm was used to solve it. Finally, the efficiency of the genetic algorithm was measured against particle swarm algorithm and simulated annealing algorithm. To compare fairly, the parameters affecting the algorithms are adjusted using the Taguchi method and the algorithms are in their best practice. The computational results prove the high ability of the genetic algorithm in optimizing the concerned problem.
Shamsoldin Hosseini; Parham Azimi; Mani Sharifi; Mostafa Zandieh
Abstract
The aim of dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. This paper developed a bi-objective mathematical model which is able to simultaneously minimize ...
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The aim of dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. This paper developed a bi-objective mathematical model which is able to simultaneously minimize the material handling costs between facilities and the cost of rearrangement facilities and material handling time. Due to probabilistic characteristic of the transporters, such as the time of handling operation and existence of the failure, calculating the time required to carry the material using analytical relationships is impossible.Therefore, this paper uses the simulation approach and artificial neural networks. In this approach, a lot of scenarios are generated by combining of various levels of variables. Each scenario shows the location of the facilities and how transportation operations in each period is performed. Then each of these scenarios is implemented through computer simulation and simulation results are considered as the response variable. Finally, using input and response variable, an artificial neural network is trained to accurately estimate the time of carrying out the transportation operations. Given that the above problem is a NP-hard; this paper proposes a new meta-heuristic algorithm to optimize the problem and compares the performance of the proposed algorithm with existing algorithms in literature.
Nastaran Bakhshizadeh; Parham Azimi
Abstract
In nowadays market, the increased level of competitiveness and uneven fall of the product/service demands are pushing enterprises to make key efforts for optimization of their process management. It involves collaboration in multiple dimensions including information sharing, capacity planning, and reliability ...
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In nowadays market, the increased level of competitiveness and uneven fall of the product/service demands are pushing enterprises to make key efforts for optimization of their process management. It involves collaboration in multiple dimensions including information sharing, capacity planning, and reliability among players. One of the most important dimensions of the supply chain network is to determine its optimal operating conditions incurring minimum total costs. However, this is even a tough job due to the complexities inherit the dynamic interaction among multiple facilities and locations. In order to resolve these complexities and to identify the optimal operating conditions, we have proposed a hybrid approach via integrating the simulation technique, Taguchi method, robust multiple non-linear regression analysis and the Harmony Search algorithm, which is the main contribution of the research. In the first experiment, design concepts are used to define a number of scenarios for the supply chain. Then each of these scenarios is implemented in a simulated environment. The results of the simulation used to estimate the relationship between the chain and chain cost factors. This relationship can be used to optimize the supply chain which minimizes the system costs. This research provides a framework to understand the intricacies of the dynamics and interdependency among the various factors involved in the supply chain. It provides guidelines to the manufacturers for the selection of appropriate plant capacity and proposes a justified strategy for delayed differentiation.
Samira Parsaiyan; Maghsoud Amiri; Parham Azimi; Mohammad Taghi Taghavifard
Abstract
The increasing concern about the deteriorating effects of supply chains related activities on the environment has led to the growing attention to develop green closed-loop supply chains in order to minimize greenhouse gases emission. This paper presents a green closed-loop supply chain model developed ...
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The increasing concern about the deteriorating effects of supply chains related activities on the environment has led to the growing attention to develop green closed-loop supply chains in order to minimize greenhouse gases emission. This paper presents a green closed-loop supply chain model developed under the demand uncertainty aiming at minimizing total cost and total CO2 emission across the supply chain, and maximizing the product’s market share in the presence of a competitor. In this regards, an agent-based market model is developed to estimate the demand’s parameter function then a hybrid simulation model which integrates agent-based and discrete event simulation modelling approaches is designed to simulate the closed-loop supply chain which is the novelty of this paper. Then, scenarios are created using Taguchi design of experiments (DOE) method, and are executed with the market model and the supply chain model to capture total cost, total CO2 and market share. A decision matrix is configured using scenarios and recorded results for three mentioned criteria and ELECTRE and SAW methods are used to rank scenarios and select the best one. The other contribution of this research is its comprehensiveness in considering variables related to three categories of inventory replenishment policy, marketing mix (price and advertisement) and transportation. An automotive industry case is provided to demonstrate the capabilities of the model and its applicability and effectiveness in resolving real-world problems.