Scientia Iranica
Volume:24 Issue: 5, 2017

The main contribution and novelty of this paper is proposing a more efficient mathematical model for integrated lot-sizing and scheduling in a multi-product multi-period capacitated flexible flow shop with sequence-dependent setups. A new approach for modeling the problem has been proposed and its complexity compared with former models. In comparison to the former models, because of fewer continuous and binary variables and constraints of proposed model makes it easy to be solved. Comparison between new model and former models proves the superiority of proposed model. Due to the complexity of the problem, three mixed-integer programming based heuristics all based on iterative resolutions of reduced-size MIPs and rolling horizon have been implemented to solve model. To evaluate the performance of the proposed model and solution method, problems of different scales have been studied. The used algorithms search the solution space for both lot-sizing and scheduling and find a combination of production planning and scheduling that is feasible and close to optimum. Computational results show that HA2 is superior for this problem and can find good quality solution for the problem in a reasonable computational time. Also, sensitivity analysis is used to clarify the problem and ensure suitability of the proposed model.

This paper aims to present a model for an agile supply chain network in construction enterprises with performance evaluation of suppliers and contractors. Management and selection of suppliers and contractors play an important role in the process of constructions since contractors are as corner stones of construction projects. Additionally, contractors are the main factor in converting resources to final products. Traditionally, contractor selection in construction projects is on the basis of the lowest proposed price. However, there are various qualitative and quantitative criteria with different priorities associated in this regards in order to make the best decision. In this paper, a hybrid method of DEA/AHP/FDEMATEL is used. First, important and effective evaluation criteria are selected through an FDEMATEL method. Then, the DEA/AHP method is implemented in order to evaluate and prioritize the selected indicators as well as to incorporate them in a supply chain. Furthermore, agility is involved in the considered supply chain network. Furthermore, in this paper for the first time in Iran, a supply chain model is studied and designed for civil companies.

The research of classical multidimensional knapsack problem always assumes that the weights, the values and the capacities are constant values. However, in the real-life industrial engineering applica- tions, the multidimensional knapsack problem often comes with uncertainty for lacking of the information about these parameters. This paper investigates a constrained multidimensional knapsack problem under uncertain environment, in which the relevant parameters are assumed to be uncertain variables. Within the framework of uncertainty theory, two types of uncertain programming models with discount con- straints are constructed for the problem with di erent decision criteria, i.e., the expected value criterion and the critical value criterion. Taking full advantage of the operational law for uncertain variables, the proposed models can be transformed into their corresponding deterministic models. After theoretically investigating the properties of the models, we do some numerical experiments. The numerical results illustrate that the proposed models are feasible and ecient for solving the constrained multidimensional knapsack problem with uncertain parameters.

This paper introduces a new method called multi-attribute Dijkstra that is an extension of Dijkstra to determine the shortest path between two points of a graph while arcs between points, in addition to the distance, have other attributes such as time(distance), cost, emissions, risk and etc. Technique for order preferences by similarity to ideal (TOPSIS) method is used for ranking and selection of the routes which is a method for solving multi-attribute decision making problems (MADM). In this regard, we try to choose appropriate weights for the attributes to consider the right decision to create a balance between the effective elements in route selection. In this paper, the algorithm of Dijkstra and TOPSIS will be reviewed and the proposed method obtained by the combination of these two will also be described. Finally, three examples with different conditions are presented to represent the performance of the model. Then these examples are compared with single-attribute Dijkstra to realize effectiveness of the proposed method. Obviously in solving large-scale examples the approach based on coding in appropriate software.

In this paper a dynamic cell formation problem is presented considering some new and special characteristics. The concept of machine requirement by lucky parts, the parts which are allowed to be produced in a specific period, is combined with the depreciable property of machines. Therefore, purchasing and selling of machines according to their book-value and generating income have been taken into account. This leads to a new vantage characteristic in cell formation where in each period we are only dealing with the types and number of machines required. The new mathematical model is presented and solved by exact and ant colony optimization methods for three problem sizes.

This paper considers a stochastic location-allocation problem for a capacitated bike sharing system (S-L&A-CBSS), in which a bike demand is uncertain. To tackle this uncertainty, a sample average approximation (SAA) method is used. Because this problem is an NP-hard problem, a hybrid greedy evolutionary algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO), namely greedy GA-PSO is embedded in the SAA method in order to solve the given large-sized problems. The performance of the proposed hybrid algorithm is tested by a number of numerical examples and used for empirical test based on Tehran business zone. Furthermore, the associated results show its efficiency in comparison to an exact solution method in solving small-sized problems. Finally, the conclusion is provided.

The objective of this manuscript is to present an improved aggregator operator by taking into account the effect of an unknown degree (hesitancy degree) in an interval-valued intuitionistic fuzzy sets (IVIFSs) environment. For this, firstly the shortcoming of the existing operators is addressed and then some improved operational laws on IVIFSs have been introduced. Based on these laws, an aggregation operator, namely an interval-valued intuitionistic fuzzy Hamacher interactive weighted averaging (IVIFHIWA), ordered weighted averaging (IVIFHIOWA) and hybrid weighted averaging (IVIFHIHWA), have been proposed. Various properties related to these operators are also investigated. Furthermore, based on these operators, an approach to deal with multi-criteria decision making (MCDM) problem is developed. Finally, a practical example is provided to illustrate the decision making process.

In some application, quality of product or performance of a process described by some functional relationships between some variables known as multivariate linear profile in the literature. In this paper, we propose Max-MEWMA and Max-MCUSUM control charts for simultaneous monitoring of mean vector and covariance matrix in multivariate multiple linear regression profiles in Phase II. The proposed control charts also have ability to diagnose either the location or variation of the process is responsible for out-of-control signal. The performance of the proposed control charts is compared with existing method through Monte-Carlo simulations. Finally, the applicability of the proposed control charts is illustrated using a real case of calibration application in the automotive industry.

Customer satisfaction is an important issue in competitive strategic management of companies. Supply chain logistical and cross-functional drivers have an important role to manage customer satisfaction. Customer satisfaction depends on quality, cost and delivery. In this paper a fuzzy mixed integer nonlinear programming model is proposed for a multi-item multi-period problem in multi-level supply chain. Minimizing costs, manufacturing and transportation time, transportation risks, maximizing quality by minimizing the number of defective products and maximizing customers’ service levels are considered to be objective functions of the model. Furthermore, it is assumed that the demand rates are fuzzy values. An exact -constraint approach is used to solve the problem. The problem is computationally intractable. Therefore, the Non-dominant Sorting Genetic Algorithm (NSGA-II) is developed to solve it. The Taguchi method is utilized to tune the NSGA-II parameters. Finally, some numerical examples are generated and solved to evaluate the performance of the proposed model and solving methods.

Recently, an alternative multi-delivery policy into imperfect economic production quantity (EPQ) inventory model with partial rework has been proposed, which considers the number of shipments a fixed and given value. This paper, treating the long-run average costs per unit time as a function of the replenishment lot size and the number of shipments , adopts the differential calculus approach to get the optimal solution of and jointly. In numerical examples, it is illustrated that the solution procedure is simple and accurate.