Municipal solid waste management is one of the main challenges in mega cities. Consequences of landfilling and importance of using alternative energies contribute to the popularity of incineration facilities for city authorities. This article tries to determine the location and allocation decisions on landfills and incineration facilities regarding social and environmental effects. The proposed model minimizes revenue of selling recovered energy and minimizes visual pollution impacts. In addition, the environmental impacts of incineration and landfilling operations are considered as part of constraints. Using the data of Tehran's waste management organization, numerical results show the number of active transfer stations and traffic reduce leading to an increase on waste collection network's efficiency. The results also show a decrease on the landfilling and the amount of hazardous methane emissions as well as an improvement on power and heat energy recovery.

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.

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.

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.

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.