Location Allocation is one of the most important decision making problems, which attracted many operational researchers during recent decades and many solution procedures are developed so far to cope with this problem. This paper proposes a new graph theory based method to cope with small size capacitated location allocation problems. Additionally, a genetic algorithm is utilized to solve medium and large scale problems. Finally, through some computational experiments, the quality and capability of these algorithms are shown.

In this paper, a new robust approach is presented to handle demand uncertainty in cell formation and layout design process. Unlike the scenario based approaches, which use predefined scenarios to represent data uncertainty, in this paper, an interval approach is implemented to address data uncertainty for the part demands, which is more realistic and practical. The objective is to minimize the total inter- and intra-cell material handling cost. The proposed model gives machine cells and determines inter-and intra-cell layouts in such a way that the decision maker can control the robustness of the layout against the level of conservatism. An illustrative example is solved by CPLEX 10 to demonstrate the performance of the proposed method. The results reveal that when the level of conservatism is changed the optimal layout can vary, significantly.

In this paper, we develop a novel framework for defining radial measures of centrality in complex networks. This framework is based on the combination of two approaches: social network analysis and traditional social science approach by considering both structure of relations and individual characteristics. It is always an important issue to detect communities in complex networks as efficiently as possible to understand both the structure and function of the networks and to interpret radial centrality measures. Therefore, we propose spectral clustering by determining the best number of communities as a prerequisite stage before finding radial measures. Based on the proposed framework, an algorithm to compute the closeness centrality in complex networks is developed. We test the proposed algorithm on Zachary’s karate club network, which is considerably used as a benchmark for community detection in a network. The preliminary results indicate that the new method is efficient at detecting both good inter-cluster closeness centrality and the appropriate number of clusters.

This paper deals with a deterministic inventory model developed for deteriorating items having two separate storage facilities (owned and rented warehouses) due to limited capacity of the existing storage (owned warehouse) with linear time dependent demand (increasing) over a fixed finite time horizon. The model is formulated with infinite replenishment and the successive replenishment cycle lengths are in arithmetic progression. Partially backlogged shortages are allowed. The stocks of rented warehouse (RW) are transported to the owned warehouse (OW) in continuous release pattern. For this purpose, the model is formulated as a constrained non-linear mixed integer programming problem. For solving the problem, an advanced genetic algorithm (GA) has been developed. This advanced GA is based on ranking selection, elitism, whole arithmetic crossover and non-uniform mutation dependent on the age of the population. Our objective is to determine the optimal replenishment number, lot-size of two-warehouses (OW and RW) by maximizing the profit function. The model is illustrated with four numerical examples and sensitivity analyses of the optimal solution are performed with respect to different parameters.

Supplier selection is always found to be a complex decision-making problem in manufacturing environment. The presence of several independent and conflicting evaluation criteria, either qualitative or quantitative, makes the supplier selection problem a candidate to be solved by multi-criteria decision-making (MCDM) methods. Even several MCDM methods have already been proposed for solving the supplier selection problems, the need for an efficient method that can deal with qualitative judgments related to supplier selection still persists. In this paper, the applicability and usefulness of measuring attractiveness by a categorical-based evaluation technique (MACBETH) is demonstrated to act as a decision support tool while solving two real time supplier selection problems having qualitative performance measures. The ability of MACBETH method to quantify the qualitative performance measures helps to provide a numerical judgment scale for ranking the alternative suppliers and selecting the best one. The results obtained from MACBETH method exactly corroborate with those derived by the past researchers employing different mathematical approaches.

For many years, supplier selection as an important multi-criteria decision has attracted both the researchers and practitioners. Recently, high incidences of natural disasters, terrorism attacks, labor strikes, and other kinds of risks, also known as disruptions, indicate the vulnerability of procurement process to these unpredicted events. In this study, a new framework is introduced to select suppliers while considering the supply risks. In the proposed framework, an expert is asked to determine the reliability of each procurement element (i.e., production, transportation, and communication) based on some proposed risk factors. Then, a distinct Multi-Layer Perceptron (MLP) network is trained to play the role of the expert opinion for estimating the reliability scores of each procurement. In addition to reliabilities, the Data Envelopment Analysis (DEA) is used to take into account the conventional selection criteria: price, delivery, quality, and capacity. A set of Pareto-optimal suppliers is obtained from the combination of efficiencies and reliability scores. Finally, the decision maker is recommended to choose between the non-dominated suppliers. Obtained experiment results indicate the effectiveness of the proposed framework.

Ultrasonic machining (USM) process has multiple performance measures, e.g. material removal rate (MRR), tool wear rate (TWR), surface roughness (SR) etc., which are affected by several process parameters. The researchers commonly attempted to optimize USM process with respect to individual responses, separately. In the recent past, several systematic procedures for dealing with the multi-response optimization problems have been proposed in the literature. Although most of these methods use complex mathematics or statistics, there are some simple methods, which can be comprehended and implemented by the engineers to optimize the multiple responses of USM processes. However, the relative optimization performance of these approaches is unknown because the effectiveness of different methods has been demonstrated using different sets of process data. In this paper, the computational requirements for four simple methods are presented, and two sets of past experimental data on USM processes are analysed using these methods. The relative performances of these methods are then compared. The results show that weighted signal-to-noise (WSN) ratio method and utility theory (UT) method usually give better overall optimisation performance for the USM process than the other approaches.

This paper describes computer aided finite element analysis of parabolic leaf spring. The present work is an improvement in design of EN45A parabolic leaf spring used by a light commercial automotive vehicle. Development of a leaf spring is a long process which requires lots of test to validate the design and manufacturing variables. A three-layer parabolic leaf spring of EN45A has been taken for this work. The thickness of leaves varies from center to the outer side following a parabolic pattern. These leaf springs are designed to become lighter, but also provide a much improved ride to the vehicle through a reduction on interleaf friction. The CAD modeling of parabolic leaf spring has been done in CATIA V5 and for analysis the model is imported in ANSYS-11 workbench. The finite element analysis (FEA) of the leaf spring has been carried out by initially discretizing the model into finite number of elements and nodes and then applying the necessary boundary conditions. Maximum displacement, directional displacement, equivalent stress and weight of the assembly are the output targets of this analysis for comparison & validation of the work.

In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.

Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM), a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performance were analyzed for CGSCM performance. Herein, the proper return, subsidy and control strategy could optimize construction green supply chain.