This paper considers the capacity determination in a closed-loop supply chain network when a queueing system is established in the reverse flow. Since the queueing system imposes costs on the model, the decision maker faces the challenge of determining the capacity of facilities in such a way that a compromise between the queueing costs and the fixed costs of opening new facilities could be obtained. We develop a De Novo programming approach to determine the capacity of recovery facilities in the reverse flow. To this aim, a mixed integer nonlinear programming (MINLP) model is integrated with the De Novo programming and the robust counterpart of this model is proposed to cope with the uncertainty of the parameters. To solve the model, an interactive fuzzy programming approach is combined with the hard worst case robust programming. Numerical results show the performance of the developed model in determining the capacity of facilities.

The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.

This paper focuses on the exploitation of the response surface methodology (RSM) to determine optimum cutting conditions leading to minimum surface roughness and cutting force components. The technique of RSM helps to create an efficient statistical model for studying the evolution of surface roughness and cutting forces according to cutting parameters: cutting speed, feed rate and depth of cut. For this purpose, turning tests of hardened steel alloy (AISI 4140) (56 HRC) were carried out using PVD – coated ceramic insert under different cutting conditions. The equations of surface roughness and cutting forces were achieved by using the experimental data and the technique of the analysis of variance (ANOVA). The obtained results are presented in terms of mean values and confidence levels. It is shown that feed rate and depth of cut are the most influential factors on surface roughness and cutting forces, respectively. In addition, it is underlined that the surface roughness is mainly related to the cutting speed, whereas depth of cut has the greatest effect on the evolution of cutting forces. The optimal machining parameters obtained in this study represent reductions about 6.88%, 3.65%, 19.05% in cutting force components (Fa, Fr, Ft), respectively. The latters are compared with the results of initial cutting parameters for machining AISI 4140 steel in the hard turning process.

This paper formulates robust optimization models for the problem of finding near-optimal locations for new intermodal terminals and their capacities for a railroad company, which operates an intermodal network in a competitive environment with uncertain demands. To solve the robust models, a Simulated Annealing (SA) algorithm is developed. Experimental results indicate that the SA solutions (i.e. objective function values) were comparable to those obtained using GAMS, but the SA algorithm could obtain solutions faster and could solve much larger problems. In addition, the results verify that solutions obtained from the robust models were more effective in dealing with uncertain demand scenarios.

The generalized exponential distribution could be a good option to analyse lifetime data, as an alternative for the use of standard existing lifetime distributions as exponential, Weibull or gamma distributions. Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.

Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ?-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.

In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN) trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

In today’s competition inherited business world, managing inventory of goods is a major challenge in all the sectors of economy. The demand of an item plays a significant role while managing the stock of goods, as it may depend on several factors viz., inflation, selling price, advertisement, etc. Among these, selling price of an item is a decisive factor for the organization; because in this competitive world of business one is constantly on the lookout for the ways to beat the competition. It is a well-known accepted fact that keeping a reasonable price helps in attracting more customers, which in turn increases the aggregate demand. Thus in order to improve efficiency of business performance organization needs to stock a higher inventory, which needs an additional storage space. Moreover, in today’s unstable global economy there is consequent decline in the real value of money, because the general level of prices of goods and services is rising (i.e., inflation). And since inventories represent a considerable investment for every organization, it is inevitable to consider the effects of inflation and time value of money while determining the optimal inventory policy. With this motivation, this paper is aimed at developing a two-warehouse inventory model for deteriorating items where the demand rate is a decreasing function of the selling price under inflationary conditions. In addition, shortages are allowed and partially backlogged, and the backlogging rate has been considered as an exponentially decreasing function of the waiting time. The model jointly optimizes the initial inventory and the price for the product, so as to maximize the total average profit. Finally, the model is analysed and validated with the help of numerical examples, and a comprehensive sensitivity analysis has been performed which provides some important managerial implications.