mathematical programming model

Today, it is essential to achieve competitive market interests and since attention to environmental issues and reduced raw resources and energy has increased, design of a suitable supply chain network can be a great help in this regard. To this end, in this study, a multi-product closed-loop green supply chain has been designed. The innovative aspect of the research is the establishment of specific multiple objective functions in the field of transportation-based energy management, which is an optimization model for the green closed loop supply chain with four optimization objectives of carbon dioxide, transportation cost, energy, and waste, and the relevant model is designed by innovative genetic algorithms. And the refrigeration simulation is solved. In order to integrate the target functions, the weighting method of the targets has been used, the weight of the targets has been chosen according to the experts' opinion. The results of the algorithms' efficiency show that when we consider the four objective functions at the same time, the genetic algorithm performs better. Also, by comparing the results of these two algorithms, the efficiency of the genetic algorithm for the two objective functions of energy and cost was proven, and the efficiency of the refrigeration simulation algorithm was determined for the two objective functions of carbon dioxide and waste. This paper helps managers to benefit from green transportation and improving environmental performance and reducing costs in the entire supply chain as a complementary strategy in order to gain a sustainable competitive advantage.

The bus rapid transit (BRT) network is an important public transportation system in some metropolises across the world. Hence, many urban managers and planners seek to develop BRT networks as they are more much flexible than railway transportation networks and can transport huge numbers of passengers. This paper designed a BRT network based on the travel demand using a binary nonlinear mathematical programming model with the aim of maximizing the covered demand and minimizing the construction cost. To calculate the covered travel demand and solve the model, a novel methodology which is exclusive to this study was proposed. To obtain the covered travel demand, the number of covered travels was calculated using the concentric circle method and the center of mass distances of the covered trips in the origins and destinations from the travel start and end stations. Based on the trip demand lines, major production and attraction land-uses, and network shape and integration, five BRT corridors were considered for the city of Karaj, Iran. The network design results suggest that the corridors would cover a total of 244,600 trips (28.7% of the total morning peak-hour trips in 2031). The corridors were estimated to have a total construction cost of 282.5 million USD.

Competition among organizations to gain more market share and higher profit encourages managers to use new and cost-effective strategies. Returned and surplus goods are always a significant part of stores and company's inventory that deciding whether or not to re-commercialize these goods can have tangible effects on their profits and losses. In this paper, a mixed integer linear programming model for maximizing profit with the cross-docking system in unsold products redistribution process is proposed. This model also takes into account the new considerations of deciding whether or not to re-commercialize products in the reverse logistics operation scheduling. Given the uncertainties in factors such as sales revenue, costs and time, the parameters of the problem are considered in terms of gray numbers and an approach to solve the gray mathematical programming model is used to deal with uncertainties. Moreover, the reverse logistics process in one of the chain stores in Tehran is considered as a case study. Implementing the proposed approach and validating the results by a sensitivity analysis on important parameters indicated that the proposed method has a high performance in decision-making process of the studied company.