This paper optimizes joint mixed bundling, assortment planning and pricing problems for complementary retail categories that comprise substitutable items. These categories are divided into one primary category and multiple secondary categories and based on cross-selling effect, customers of primary category can opt to buy from other categories. It is assumed that the bundle can comprise one product of each offered complementary categories. A mixed-integer nonlinear programming is proposed that maximizes total profit by optimizing strategy of bundling, assortment selection and prices of single products and the bundle under a maximum-surplus customer choice model. Then because of computational considerations, this model is linearized and converted to a mixed-integer linear programming; whereby exact solution for even large-scale problems can be obtained. The results show that if cross-selling effect between product categories is overlooked or bundling of products is not allowed, then significant profit losses, will be resulted. To the best of our knowledge, this is the first study on bundling strategies in assortment selection decisions.

This paper is an attempt to examine the implementation of Lean Manufacturing system in the Indian industries. Predominant elements of Lean Manufacturing, benefits gained after its implementation and obstacles observed by Indian Industry have been recognized. The results of this survey support the opinion that Lean Manufacturing had potential to improve the organizational performance of Indian industries. Nevertheless, Indian industries are required to be passionate to transform their manufacturing by adopting Lean manufacturing to gain the full benefits. A large numbers of literature papers are available on the better side of Lean manufacturing approach and its benefits gained by manufacturing organizations after implementation. But the adverse impacts of Lean manufacturing are not discussed to a great extent. Some drawbacks of Lean manufacturing are also highlighted in this paper.

Deterioration of items is a phenomenon which cannot be neglected as it may provide absurd result. In high-tech business market, deterioration is not always constant but it is time dependent. This paper presents inventory model for time dependent deterioration and time dependent demand under shortages. The mathematical model is provided to optimize the cycle time by minimizing the total cost. Numerical results are discussed to validate the proposed model. The variation of the optimal solution for different parameters is discussed for some instances.

During the past few years, there have been tremendous efforts on development of Iranian banking industry in an attempt to fight against monopoly of banking system. Bank Pasargad is one of the biggest Iranian banks established for offering retail, commercial as well as investment banking services. The firm was established in 2005 as part of the government & apos; s privatization of the banking system. This paper presents an empirical investigation to determine the most efficient locations for development of this bank. The study uses three main criteria, customer services (C1), Equipment (C2) and Profitability (C3) to compare different alternatives for establishing new branches in city of Tehran, Iran. Using fuzzy data envelopment analysis the study has determined that center of the city could receive the most important attraction followed by north region, west region as well as south west.

Locating, routing and inventory control in production and distribution centers are the most important decisions in supply chain management. Because of the dependence of these decisions to each other, considering these three aspects simultaneously can have a huge impact on cost reduction. In this study, first, a location-inventory model is developed by utilizing METRIC approach and then, METRIC approach is applied to the location-inventory-routing model. The intended supply chain includes a supplier, distributors and retailers, and the inventory control policy is implemented for both the distributors and retailers. Retailers' demand follows Poisson distribution and the lead-time is also considered probabilistic and is affected by the shortage in distribution centers. Given that the presented model belongs to the class of NP-hard problems, a hybrid metaheuristic solution method is also presented to solve the resulted problem. The proposed hybrid metaheuristic algorithm contains a Simulated Annealing algorithm, to optimize the location-routing problem, and a Genetic Algorithm, to optimize the inventory problem. Also, to evaluate the performance of hybrid algorithm, a comparison between the results of the proposed hybrid algorithm and the exact solutions obtained from Lingo software is provided and, finally, the results are analyzed.

Due to more than three million people in Germany with a Turkish migration background country-of-origin (COO)-sensitive, designs of offers directed at this customer segment have been implemented by various corporations and discussed in the management literature for quite a while. Unfortunately, to date most publications have a weak empirical foundation and refrain from simultaneously investigating preference effects of several country-of-origin-sensitive and -neutral offer characteristics among Turkish migrants living in Germany. Therefore, the present paper explores the relative impacts of three COO-sensitive offer characteristics and one COO-neutral attribute of bundled mobile communication offers on preference statements derived from a conjoint-analysis of questionnaire responses of 249 consumers in Germany with Turkish roots. The results suggest that for the offering category in question a COO-neutral feature (cell phone type/brand) shapes the preferences of Turkish migrants almost to the same extent as the three remaining price- and communication-related characteristics investigated. Furthermore, we found that Turkish consumers in Germany encompass four subsegments with distinct preferences with respect to the design of mobile communication offerings. The members of these subsegments in turn differ primarily in terms of their age and gender structures as well as their level of accommodation to the German culture.

Surface roughness and cutting forces are considered as important factors to determine machinability rate and the quality of product. A number of factors like cutting speed, feed rate, depth of cutting and tool noise radius influence the surface roughness and cutting forces in turning process. In this paper, an Artificial Neural Network (ANN) model was used to forecast surface roughness and cutting forces with related inputs, including cutting speed, feed rate, depth of cut and tool noise radius. The machined surface roughness and cutting force parameters related to input parameters are the outputs of the ANN model. In this work, 24 samples of experimental data were used to train the network. Moreover, eight other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation.

Stochastic scheduling addresses several forms of uncertainty to represent better production environments in the real world. Stochastic scheduling has applications on several areas such as logistics, transportation, production, and healthcare, among others. This paper aims to evaluate the performance of various greedy functions for a GRASP-based approach, under stochastic processing times. Since simulation is used for estimating the objective function, two simulation techniques, Monte Carlo simulation and Common Random Numbers (CRN), are used to compare the performance of different greedy (utility) functions within the GRASP. In order to validate the proposed methodology, the expected total weighted tardiness minimization for a single machine problem was taken as case study. Results showed that both, CRN and Monte Carlo, are not statistically different regarding the expected weighted tardiness results. However, CRN showed a better performance in terms of simulation replications and the confidence interval size for the difference between means. Furthermore, the statistical analysis confirmed that there is a significant difference between greedy functions.

Supplier evaluation and selection play a vital role in establishing an effective supply chain for any firm. The supplier evaluation not only enables the firm to know the performance of suppliers, but also gives an indication of the ability of the firm itself to gain competitive advantage. The appropriate evaluation is the basis for the subsequent selection of the best supplier. This paper presents a hybrid methodology using Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA) to evaluate suppliers and to identify the best supplier for a firm. In this work, super efficiency and cross efficiency methods of DEA are employed to determine the efficiencies and the performance scores of the suppliers respectively. On the basis of super efficiency and cross efficiency scores, the list of efficient suppliers with high performance is identified and GRA methodology is adopted to identify the best supplier. A case study is presented to demonstrate the proposed hybrid methodology.