International Journal of Industrial Engineering & Production Research

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In today's competitive market, quality has an important role in manufacturing system. The manufacturers attempt to maintain their production system in a proper condition to produce high quality products. One of the key factors to achieve this goal is maintenance policy. Most studies on maintenance focused on machines and less attention has been paid to human resources as the most important factor in manufacturing system. In this paper we propose a mixed integer nonlinear model to schedule the maintenance of machines and rest of human resources based on reliability factor. This model aims to minimize the cost of machines and human resources idleness and products quality cost. The performance of proposed model was examined by two instances and the obtained results indicated this model can provide efficient and effectiveness work and rest schedule for machines and human resources in manufacturing systems.

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In this paper we develop a new approach for land leveling in order to improve the topology of a large area for irrigation or civil projects. The objective in proposed model is to minimize the total volume of cutting so that technical requirements of land leveling such as suitable slope and standard ratio of cutting to filling and maximum penstock point’s height are considered. We develop a warped surface pattern and apply a linear programming model to determine the land optimal topology. Our approach is more practical to apply, in comparison with the existing “fit to plane” methods which apply bivariate regression statistical techniques because in these methods finding optimal solution, considering technical requirements, needs trial and error. Our proposed method does not need any trial and error, furthermore its results is global optimum. Also the warped surface pattern is adoptable to plane or curved patterns, and it is applicable for any land with any magnitude.

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There are many real problems in which multiple responses should be optimized simultaneously by setting of process variables. One of the common approaches for optimization of multi-response problems is desirability function. In most real cases, there is a correlation structure between responses so ignoring the correlation may lead to mistake results. Hence, in this paper a robust approach based on desirability function is extended to optimize multiple correlated responses. Main contribution of the current study is the synthesis of ideas considering correlation structure in robust optimization through defining joint confidence interval and desirability function method. A genetic algorithm was employed to solve the introduced problem. Effectiveness of the proposed method is illustrated through some computational examples and some comparisons with previous methods were performed to show applicability of the proposed approach. Also, a sensitivity analysis was provided to show relationship of correlation and robustness in these approaches.

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This paper offers an approach that could be useful for diverse types of layout problems or even area allocation problems. By this approach there is no need to large number of discrete variables and only by few continues variables large-scale layout problems could be solved in polynomial time. This is resulted from dividing area into discrete and continuous dimensions. Also defining decision variables as starting and finishing point of departments in area makes it possible to model layout problem so. This paper also provides new technique that models basic constraints of layout problems.

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The present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. For this purpose, four objective functions have been considered simultaneously: I) maximizing the level of accordance between system design and optimum modularity design,II) maximizing the level of accessibility and the maintenance space required,III) maximizing the providing of distance requirement and IV) minimizing the layout space. The first objective function has been put forward for the first time in the present paper and in it, the optimum system modularity design was determined using the Design Structure Matrix (DSM) technique.The second objective function is combined with the concept of Level of Repair Analysis (LoRA) and developed. Simultaneous optimization of the above-mentioned objective functions has not been considered in previous studies. The multi objective problem which has been put forward was applied on a laser range finder containing 17 subsystems and the modularity and optimum layout was determined using a multi objective particle swarm optimization (MOPSO) algorithm.

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There are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. League championship algorithm is such algorithm which may be used in the mentioned problems. In the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. The proposed algorithm will be used for solving large-scale solving support vector machine model which is a quadratic optimization problem and cannot be solved in a non polynomial time using exact algorithms or optimally using traditional heuristic ones in large scale sizes. The efficiency of the new algorithm will be compared to traditional one in terms of the optimality and time measures. The superiority of the algorithm can be compared versus older version.

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This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing time of the already processed jobs on the corresponding machine, i.e., the setup time of a job is past- sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total completion time (called TC) is minimized. Since that the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on ant colony optimization algorithm (ACO) is proposed to solve the given problem. The performance of the presented model and the proposed algorithm is verified by a number of numerical experiments. The related results show that ant colony optimization algorithm is effective and viable approache to generate optimal⁄near optimal solutions within a reasonable amount of computational time.

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Manufacturing process frequently employs optimization of machining parameters in order to improve product quality as well as to enhance productivity. The material removal rate is a significant indicator of the productivity and cost efficiency of the process. Taguchi method has been implemented for assessing favorable (optimal) machining condition during the machining of nylon by considering three important cutting parameters like cutting speed, feed rate and depth of cut during machining on CNC. The objective of the paper is to find out, which process parameters having more impacts on material removal rate during turning operation on nylon using analysis of variance (ANOVA). An Orthogonal array has been constructed to find the optimal levels of the turning parameters and further signal-to-noise (S/N) ratio has been computed to construct the analysis of variance table. The results of ANOVA shown that feed rate has most significant factor on MRR compare to cutting speed and depth of cut for nylon. The confirmation experiments have conducted to validate the optimal cutting parameters and improvement of MRR from initial conditions is 555.56%.

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In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.

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In this paper, we propose a vibration damping optimization algorithm to solve a fuzzy mathematical model for the single-item capacitated lot-sizing problem. At first, a fuzzy mathematical model for the single-item capacitated lot-sizing problem is presented. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP) which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity and NP-hardness of the problem, a vibration damping optimization (VDO) is used to solve the model for large-scale problems.  To verify the performance of the proposed algorithm, we computationally compared the results obtained by the VDO algorithm with the results of the branch-and-bound method and two other well-known meta-heuristic algorithms namely simulated annealing (SA) and genetic algorithm (GA). Additionally, Taguchi method is used to calibrate the parameters of the meta-heuristic algorithms. Computational results on a set of randomly generated instances show that the VDO algorithm compared with the other algorithms can obtain appropriate solutions.

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    Given the increasing number of documents, sites, online sources, and the users’ desire to quickly access information, automatic textual summarization has caught the attention of many researchers in this field. Researchers have presented different methods for text summarization as well as a useful summary of those texts including relevant document sentences. This study selects extractive method out of different summarizing methods (e.g. abstract method). Extractive method involves summarizing text through objective extraction of some parts of a text like word, sentence, and paragraph. A summarization issue would be unsolvable by exact methods in a reasonable time with considering documents with high amount of information (NP complete). These kinds of issues are usually solved using metaheuristic methods. A biogeography-based optimization algorithm (BBO), which is a new metaheuristic method in the domain of extractive text summarization, is used in this article. 

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Nowadays, the popularity of social networks as marketing tools has brought a deal of attention to social networks analysis (SNA). One of the well-known Problems in this field is influence maximization problems which related to flow of information within networks. Although, the problem have been considered by many researchers, the concept behind of this problem has been used less in business context. In this paper, by using a cost-benefits analysis, we propose a multi-objective optimization model which helps to identify the key nodes location, which are a symbol of potential influential customers in real social networks. The main novelty of this model is that it determines the best nodes by combining two essential and realistic elements simultaneously: diffusion speed and dispersion cost. Also, the performance of the proposed model is validated by detecting key nodes on a real social network

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A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.

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In this article, a multi objective model is presented to select and allocate the order to suppliers in uncertainty condition and in a multi source, multi customer and multiproduct case in a multi period state at two levels of supply chain. Objective functions considered in this study as the measures to evaluate suppliers are cost including purchase, transportation and ordering costs, timely delivering, shipment quality or wastages which are amongst major quality aspects, partial and general coverage of suppliers in respect of distance and finally suppliers weights making the products orders amount more realistic. The major limitations are price discount for products by suppliers which are calculated using signal function. In addition, suppliers weights in the fifth objective function is calculated using fuzzy Topsis technique. Lateness and wastes parameters in this model are considered as uncertain and random triangular fuzzy number. Finally the multi objective model is solved using two multi objective algorithms of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO) and the results are analyzed using quantitative criteria Taguchi technique was used to regulate the parameters of two algorithms. 

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In this paper a novel modelling and solving method has been developed to address the so-called resource constrained project scheduling problem (RCPSP) where project tasks have multiple modes and also the preemption of activities are allowed. To solve this NP-hard problem, a new general optimization via simulation (OvS) approach has been developed which is the main contribution of the current research. In this approach, the mathematical model of the main problem is relaxed and solved then the optimum solutions were used in the corresponding simulation model to produce several random feasible solutions for the main problem. Finally, the most promising solutions were selected as the initial population of a genetic Algorithm (GA). To test the efficiency of the problem, several test problems were solved by the proposed approach and according to the results, the proposed concept has a very good performance to solve such a complex combinatoral problem. Also, the concept could be easily applied for other similar combinatorics. 

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Considering the high costs of the implementation and maintenance of gas distribution networks in urban areas, optimal design of such networks is vital. Today, urban gas networks are implemented within a tree structure. These networks receive gas from City Gate Stations (CGS) and deliver it to the consumers. This study presents a comprehensive model based on Mixed Integer Nonlinear Programming (MINLP) for the design of urban gas networks taking into account topological limitations, gas pressure and velocity limitations and environmental limitations. An Ant Colony Optimization (ACO) algorithm is presented for solving the problem and the results obtained by an implementation of ACO algorithm are compared with the ones obtained through an iterative method to demonstrate the efficiency of ACO algorithm. A case study of a real situation (gas distribution in Kelardasht, Iran) affirms the efficacy of the proposed approach.
 

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In this article, we propose a special case of two-echelon location-routing problem (2E-LRP) in cash-in-transit (CIT) sector. To tackle this realistic problem and to make the model applicable, a rich LRP considering several existing real-life variants and characteristics named BO-2E-PCLRPSD-TW including different objective functions, multiple echelons, multiple periods, capacitated vehicles, distribution centers and automated teller machines (ATMs), different type of vehicles in each echelon, single-depot with different time windows is presented. Since, routing plans in the CIT sector ought to be safe and efficient, we consider the minimization of total transportation risk and cost simultaneously as objective functions. Then, we formulate such complex problem in mathematical mixed integer linear programming (MMILP). To validate the presented model and the formulation and to solve the problem, the latest version of ε-constraint method namely AUGMECON2 is applied. This method is especially efficient for solving multi objective integer programing (MOIP) problems and provides the exact Pareto fronts. Results substantiate the suitability of the model and the formulation.
 

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Natural disasters and crisis are inevitable and each year impose destructive effects on human as injuries and damage to property. In natural  disasters and after the outbreak of the crisis, demand for logistical goods and services increase. Effective distribution of emergency aid could have a significant role in minimizing the damage and fatal accident. In this study, a three-level relief chain including a number of suppliers in fixed locations, candidate distribution centers and affected areas at certain points are considered. For this purpose a mixed integer nonlinear programming model is proposed for open transportation location routing problem by considering split delivery of demand. In order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed mode. The objectives of the proposed model include total cost minimization, minimization of the maximum travel time of
vehicles and minimization of unmet demands. In order to solve the problem of the proposed model, fuzzy multi-objective planning is used. For efficiency and effectiveness of the proposed model and solution approach, several numerical examples are studied. Computational results show the effectiveness and efficiency of the model and the proposed approach.

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In today's world, natural disasters such as earthquakes, floods, crises such as terrorist attacks and protests threaten the lives of many people. Hence, in this research we present a mathematical modeling that provide efficient and effective model to locate temporary depot, equitable distribution of resources and movement of injured people to health centers, with the aim of developing the multi-objective model and considering multiple central depot, multiple temporary depot and several type of relief items in the model . This paper is considered certainty state and uncertainty of influencing parameters of the models in robust optimization for three different levels uncertainty and in different size with consideration of traditional goals function and humanitarian purposes functions simultaneously. The model has been solved with multi-objective Particle Swarm optimization algorithm (MOPSO) and GAMS software to validate the model. Some numerical examples are presented. In Addition, we present sensitivity analyzes of model and study the relationship of the number of temporary depot location and the number of injured people to move to health centers and the number of uncovered damaged points.

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In the Wire EDM of oil hardening die steel materials is a complicated machining process. Hence to find out the best set of process parameters is an important step in wire EDM process. Multi-response optimization of machining parameters was done by using analysis called desirability function analysis coupled with the dimensional analysis approach. In the present work, based on Taguchi’s L₂₇ orthogonal array, number experiments were conducted for OHNS material. The WEDM process parameters such as, pulse on time , pulse off time, input current, wire feed rate and the servo voltage are optimized by multi-response considerations such as material removal rate and surface roughness. Based on desirability analysis, the most favorable levels of parameters have been known. The significant contribution of parameters is determined by dimensional analysis. The experimental results show that the results obtain by using DA approach has a good agreement with the measured responses. The correlation up to  99% has been achieved between the developed model and the measured responses by using dimensional analysis approach. 
In the Wire EDM of oil hardening die steel materials is a complicated machining process. Hence to find out the best set of process parameters is an important step in the wire EDM process. Multi-response optimization of machining parameters was done by using analysis called desirability function analysis coupled with the dimensional analysis approach. In the present work, based on Taguchi’s L₂₇ orthogonal array, number experiments were conducted for OHNS material. The WEDM process parameters such as pulse on time, pulse off time, input current, wire feed rate, and the servo voltage are optimized by multi-response considerations such as material removal rate and surface roughness. Based on desirability analysis, the most favorable levels of parameters have been known. The significant contribution of parameters is determined by dimensional analysis. The experimental results show that the results obtain by using DA approach has a good agreement with the measured responses. The correlation up to  99% has been achieved between the developed model and the measured responses by using dimensional analysis approach.