Increasing attention to environmental problems and social responsibility lead to appear reverse logistic (RL) issues in designing supply chain which, in most recently, has received considerable attention from both academicians and practitioners. In this paper, a multi-product reverse logistic network design model is developed; then a hybrid method including Chance-constrained programming, Genetic algorithm and Monte Carlo simulation, are proposed to solve the developed model. The proposed model is solved for risk-averse and risk-seeking decision makers by conditional value at risk, sum of the excepted value and standard deviation, respectively. Comparisons of the results show that minimizing the costs had no direct relation with the kind of decision makers; however, in the most cases, risk-seeking decision maker gained more return products than risk-averse ones. It is clear that by increasing returned products to the chain, production costs of new products and material will be reduced and also by this act, environmental benefits will be created.

This paper presents a model to solve the multi-objective location-routing problem with capacitated vehicles. The main purposes of the model are to find the optimal number and location of depots, the optimal number of vehicles, and the best allocation of customers to distribution centers and to the vehicles. In addition, the model seeks to optimize vehicle routes and sequence to serve the customers. The proposed model considers vehicles’ traveled distances, service time and waiting time while guaranteeing that the sum of these parameters is lower than a predetermined value. Two objective functions are investigated. First objective function minimizes the total cost of the system and the second one minimizes the gap between the vehicles’ traveled distances. To solve the problem, a Multi-Objective Imperialist Competitive Algorithm (MOICA) is developed. The efficiency of the MOICA is demonstrated via comparing with a famous meta-heuristics, named Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Based on response surface methodology, for each algorithm, several crossover and mutation strategies are adjusted. The results, in terms of two well-known comparison metrics, indicate that the proposed MOICA outperforms NSGA-II especially in large sized problems.

Engaging customer is the burning issue for companies especially the service sector, either online or offline. Minimizing the customer disengagement is the same like reducing dissatisfaction or churn. Customer disengagement may be caused by many factors, ad skepticism is one of them; ad skepticism has two main antecedents personality variable and consumption/influencing varia-bles. This research explores the relationship of ad skepticism with customer disengagement through personality variables which are cynicism, reactance and self-esteem. The unit of analysis is the telecom and banking industry of Pakistan which is foreseeing an era of virtual currency and both are customer oriented industries. Only offline disengagement is researched and data is collected from the Business centers of telecom and banking branches dealing with virtual curren-cy in Pakistan. Hypothetical model is given after digging the relevant literature; model is tested through confirmatory factor analysis and structural equation modeling. Eight hypotheses were purposed from the connections of model, all hypotheses are accepted except the cynicism posi-tive effect on social ad skepticism. This can be due to commonality of social and charity in Paki-stani society, Muslims consider charity as a pious act and they do not think for cynic behavior in charity or social related works. The results manifest that customers in telecom industry are hav-ing ad skepticism and that is becoming the cause of their disengagement. Further, social ad skep-ticism has more impact on the customer disengagement than the general ad skepticism. While the reactance has more effect on general ad skepticism than other antecedents and cynicism has the lowest impact on social ad skepticism than other antecedents.

Inventory control of deteriorating items constitutes a large part of the world’s economy and covers various goods including any commodity, which loses its worth over time because of deterioration and/or obsolescence. Vendor managed inventory (VMI), which is a win-win strategy for both suppliers and buyers gains better results than traditional supply chain. In this research, we study an economic order quantity (EOQ) with shortage in form of partial backorder under VMI policy. The model is concerned with multi-item subject to multi-constraint including storage space, time period and budget constraints. Two metaheuristic algorithms, namely Simulated Annealing and Tabu Search, are used to find a near optimal solution for the proposed fuzzy nonlinear integer-programming problem with the objective of minimizing the total cost of the supply chain. Furthermore, the sensitivity analysis of the metaheuristic parameters is performed and five numerical examples containing different numbers of items are conducted in order to evaluate the performance of the algorithms.

In this study, we incorporate trade credit policy into a joint marketing and pricing problem in which demand rate depends on the length of the credit period provided by the retailer for her customers, marketing expenditure, and selling price. The trade credit policy adopted here is a delayed payment policy in partial form in which the customers must pay a percent of the total purchasing cost at the time of placing an order and can pay the remaining amount later. Shortages are allowed and partially backordered. The main objective of this study is to determine the optimal credit period, marketing expenditure, selling price, and variables of inventory control simultaneously in order to maximize retailer’s total profit. For solving the proposed problem, first an approximation method is applied to simplify the profit function and transform the problem into a constrained Signomial Geometric Programming (SGP) problem, then a global optimization approach is used for solving the model. Finally, a numerical example and sensitivity analysis of the important parameters are conducted to show the effectiveness of proposed approach.

Detecting how a cutout affects the critical buckling load in circular cylindrical shell is a serious issue for the design of the shells used in marine structures, aerospace and automobile applications. In this paper, buckling behavior of aluminum cylindrical shell with rectangular cutout, subjected to axial pressure was studied by means of finite element simulations. The effects of geometric parameters (R, t, a, b and L) on the first buckling mode capacity of the shell were studied. The effects of these factors and their interaction effects were investigated by combined numerical and statistical analysis. The results show that R/b, t and Rt/b were the main effective factors of critical buckling load. Based on the statistical analysis, a model for prediction of the critical buckling load was obtained with an accuracy equal to 95% (R2, R2 (pred) and R2 (adj)). This equation could be used to predict the critical buckling load of an isotropic circular shell with a rectangular cutout.

Nowadays, companies offer product bundles with special discounts in order to sell more products. However, it is important to note that customers show different levels of loyalties to companies, and each segment of the market has unique features, which influences the customers’ buying patterns. The primary purpose of this paper is to propose a novel model for product bundling in e-commerce websites by using market segmentation variables and customer loyalty analysis. RFM model is employed to calculate customer loyalty. Subsequently, the customers are grouped based on their loyalty levels. Each group is then divided into different segments based on market segmentation variables. The product bundles are determined for each market segment via clustering algorithms. Apriori algorithm is also used to determine the association rules for each product bundle. Classification models are applied in order to determine which product bundle should be recommended to each customer. The results demonstrate that the silhouette coefficient, support, confidence, and accuracy values are higher when both customer loyalty level and market segmentation variables are used in product bundling. Accordingly, the proposed model increases the chance of success in direct marketing and recommending product bundles to customers.

Mokran coasts have good potentials for development and investment, in terms of access to the high seas, geopolitical situation, especially, benefit from certain natural circumstances and history, but for various reasons, this area has no growth, commensurate with the potential. This study is conducted to evaluate investment opportunities in the region, to identify the necessary infrastructures and the promotion of entrepreneurial activities. The method identifies 22 investment opportunities through a comparative study and using best-worst multi-criteria decision method (BWM), and experts’ opinion, the possibility of implementing them are ranked in Mokran. Then, the necessary public infrastructure requirements are ranked in eleven groups with TOPSIS. The results show that, private port sites, loading, warehouses, commercial centers and special areas of fisheries, provide good opportunities for investors, and infrastructure, security, transport, sustainable energy, and the single window government services are the priorities for planning in the public sector.

Inspired by the industries such as food and beverage, metal and steel, as well as petroleum and petrochemical ones, the current study addresses a joint order acceptance and scheduling, lot streaming in a flexible flow shop and batch delivery problem. For maximizing a profit objective function with trading off between the revenue of the accepted orders and the costs incurred, a novel mixed integer linear programming is proposed. This paper develops a hybrid metaheuristic algorithm based on the Genetic Algorithm. In the developed algorithm, (1) a heuristic, (2) a local search, and (3) a restart phase is proposed. To set the appropriate parameters of the algorithms, Taguchi experimental design was applied. The obtained results reveal the appropriate performance of the hybrid genetic algorithm.

Medical tourism industry is an international phenomenon, which most of medical tourists for some reasons such as high costs of treatment, long waiting queues, lack of insurance and lack of access to health care in the origin country, travel long distances to benefit from health care services of destination country. Given the competitive nature of this industry, most countries are designing practical and legal services and planning for their development. For this purpose, this study has been conducted to develop a strategic planning framework for development of medical tourism industry in Yazd province of Iran; because in recent years Yazd has recognized as the health pole by patients in developing countries. In sum, emphasizing on servicing, enhancing and developing specialized treatment centers, has attracted patients from center, south and east of the country as well as Middle East and Central Asia countries. The dominant approach in this study is developmental – practical and also the research method is descriptive, analytical and survey. In order to analyzing the data, the SWOT model and best-worst techniques have been used. In the following, after identifying strategic position of Yazd province in terms of medical tourism industry, the related strategies were formulated and practical results were presented.