JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH IN PRODUCTION SYSTEMS (IERPS)
Year:2019 | Volume:4 | Issue:14 |Papers Count:13

In this paper, a new model for energy planning problem is proposed based on environmental and social aspects of sustainability. Power plants capacity expansion is modeled in a way that the water consumption cost is minimized and the greenhouse gases emission rate is controlled. Moreover, social acceptance for the capacity expansion plan must ensure the government’ s social acceptance level, in the planning periods. Different scenarios generated to cover the variety of uncertainties, based on stochastic P-robust approach. In this approach, the expected cost is minimized and the regret value for each scenario would not be more than P, so the solution is P-robust. The proposed model is applied for a case study from Iran energy sector. The results indicate that a slight increase in expected cost value leads to considerable regret reduction.

Decision-making based on inaccurate information or in the absence of information can result in irreparable damages. Hence, this paper examines the impact of the revenue sharing contract on information sharing and information leakage in a supply chain with one manufacturer and two competing retailers. One of the retailers has more detailed information about the forecast of uncertain demand and can share it with the manufacturer. The manufacturer may also share the information send by the retailer having more accurate information to the other one in order to gain higher profits (information leakage). Therefore, the informed retailer may share his private information with the manufacturer inaccurately. As such, the manufacturer uses a revenue sharing contract to encourage the more informed retailer to release his private information truthfully to the manufacturer and the other retailer. The results show that although the information leakage by the manufacturer increases his profit, the informed retailer encourages to share incorrect information which is harmful to the manufacturer. Besides, the sensitive analysis reveals that under revenue sharing contract, the retailer with more accurate information about the uncertain demand will share its information truthfully to the manufacturer and other retailer, which makes coordination among the member of the supply chain which increases the profit of the whole supply chain.

Dependence of a product order cost to the order quantity is one of the practical and less surveyed assumptions of the literature of economic order quantity model. This assumptions will cause the goal function to be nonconvex and increases the complexity of the problem model. Furthermore, inventory management of the products which are likely to be out of fashion or perished over time, has a great importance. In this regard, simulation of the inventory control model of perishable products has been considered in the present study via the stochastic demand of the product which depends on the time and price of the product. In addition, the dependence of the order cost to the order quantity and dependence of holding cost to the inventory level which are among the practical assumptions of the business world have been considered. These factors cause the usual mathematical solutions not to be able to solve the problem. Therefore, system dynamics have been used as a powerful, flexible, and practical solution to model and solve the problem. A numerical example is also presented to provide a better understanding of the simulation operation; and with the assistance of the optimization of the input variables (ReOrder Point, Order Quantity) the optimal amount of the objective function (Gross Cost Average) is reached. The results showed that the proposed replenishment policy can benefit the necessary decisions regarding inventory management and control of the perishable products.

Joint optimal inventory control and preventive maintenance are interested in many research and has a potential impact on the performance of manufacturing systems. In addition, due to the uncertainty in demand, maintenance and inventory shortages are almost inevitable. Therefore, determining the optimal amount of buffer level, the time required to create additional storage space to address the loss, and the maintenance time is a concern for many manufacturers. Paper studied a single-machine production unit with incremental failure rates. The system begins with a h-sized inventory stored in period A and stops as soon as it reaches period m, whichever occurs earlier, and is subject to maintenance. The buffer inventory during this period. A mathematical model and a numerical approach are used to obtain optimal values of variables simultaneously to minimize the average total cost and satisfy the access constraint. The results show that, in the presented model, the total cost and decision variables are highly sensitive to the inventory holding cost but not also for the occurred scenario.

Reducing fuel consumption leads to reduced greenhouse gas emissions and customer service costs, resulting in customer satisfaction and reduced environmental degradation. . For this purpose, this paper focuses on the optimization and planning of the movement of inbound and outbound trucks and the green supply chain, with multi cross-docking and two different types of objective functions of minimizing the sequence of truck transportation and carbon dioxide emissions into the supply chain. Since the paper model is a linear programming integer of zero and since these models belong to the NPhard class, their solving time severely increases with increasing the problem dimensions. In this paper, to solve the model meta-heuristic algorithms have been used. The algorithms used in solving thae model are Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Ant Colony (MOACO) Algorithm. Finally, the model has been solved using two algorithms and computational experiments reported carefully to illustrate and compare designing and computational.

This paper examined a warranty model where after the end of the twophase warranty period, an extra warranty period is provided to the customer. The two-phase warranty period is split into two sections; the first period is free replacement / repair warranty (FRRW) period and the other is period of Pro-Rata Warranty (PRW). If the product failure occurs during the FRRW period, the damaged product is repaired by the manufacturer free or replaced by a new product. If this failure occurs in PRW period, the manufacturer and the customer are both responsible for paying for the repair costs; and if the failure happens during the extended warranty period, only minimal repairs is made. As customer consumption rate differs in a given period, it is advisable to classify customers based on their rate of consumption before determining the type of warranty policy, which will reduce the cost of warranties. Then, for each type of customer, a maintenance policy are supplied. In addition, the percentage of low consuming customer (high-consumption) will be effective in the overall costs.

It is usual for a production environment to encounter uncertainty and variable data that causes generating random parameters. Failure to pay attention to these changes will make the scheduling not adequately match the reality and cause many losses in production environments. Considering the importance of the issue, in this article we use the Robust Optimization Approach to deal with uncertainty in the aggregate production planning parameters. In this paper, in a robust model, it is assumed that the uncertainty of non-deterministic parameters is continuous and a completely new and innovative approach is proposed for Robust Optimization for risk-averse managers and then, an optimization strategy is used to examine the uncertainty. In order to investigate the model results, examples have been made in small and large sizes and the problem has been solved and analyzed using the GAMS software and Lagrange relaxation method. The results of the implementation of the proposed robust models in this paper, compared to the basic model, show that the results have more stability against uncertainty and this causes a significant reduction in risk.

Risk management is a significant issue in supply chain management. Improving the ability to control and manage the risk, enables the companies to be more successful in competing with other companies and decrease the expected long-term loss. In this manuscript, a mixed integer linear programming model for designing the green supply chain is presented. This model aims to minimize the cost, greenhouse gas emissions, and risk. Risk of supplying the raw materials and transportation in all levels of supply chain are under uncertainty. Furthermore, cost of raw materials is suggested by suppliers to producers with an incremental discount. The initial modelling is turned into a deterministic one using Bertsimas and Sim budget of uncertainty approach and consequently solved by GAMS software to manage risk. Furthermore, the uncertain parameter is analyzed and using various amounts the obtained result has been assessed and evaluated. The results show that the risk function is the most important factor in objective function, because parameters of risk function are subject to uncertainty.

New technologies require new approaches to create valuable opportunities in the supply chain to integrate not only the physical progress of goods and services but also massive information and financial data. Using the technology of the day and analyzing the existing data and presenting the reports to the managers of the organization at the right time enable them to make suitable and intelligently decisions due to market fluctuations. It causes to move their effective steps toward the organizations strategic goals. Nowadays, the flexibility of the organizations is very important due to the changing customer’ s needs. On the other hand, the suitable time and amount of ordering has a significant impact to reduce the costs and to increase the organization's agility. Cloud technology, as a key feature in today's world, can contribute on data transfer in various performance models of the supply chain and also in analyzing various business parts. For this purpose, this research fallows to explore the use of cloud technology to value the supply chain processes and presents the problem as a mathematical model. The mathematical model has been thoroughly solved and analyzed in the large dimensions of the problem using the best-developed optimization algorithm. The results showed that with the implementation of the proposed supply chain network, transportation and costs reduce significantly and increase company revenue, which lead the companies to the green supply chain. Moreover, by removing intermediaries, the goods are delivered to the final customer with a better price and quality, which result in more customer satisfactions.

Processes of production and supply of products have been changed in pattern in condition of intensifying the competitive atmosphere and are studied in the form of a supply chain network. In the meantime, the importance of the flow of materials in the supply network, and the distribution of products in the distribution network is more important among the three streams of finance, information, and materials. More attention to customer has leaded production process to build to order (BTO). Distribution chain as a part of supply chain is studied in this paper and a new mathematical model is produced for build to order supply chain (BTOSC). Providing constrains are considered in order to close condition to real world. There is kinds of production units, distribution centers, and retailers. After deterministic an order, it will be sent from distribution centers to a retailer or from a production unit to retailers directly. The objective function is to maximize total profit. First, a new mixed integer linear programing model is developed for the considered problem. Due to the complexity of mathematical model, a new algorithm is introduced to solve it based on Lagrangian Relaxation (LR). Finally, the efficiency of the proposed algorithm is evaluated by solving a numerical example.

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 recommercialize 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 recommercialize 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.

displacement between levels, and maximize the reliability of delivery for suppliers. This network is including supplier centers, production/resuscitation centers, distribution/collection centers, customer centers, and disposal centers. A new Linear Integer programming model is formulated. Besides, fuzzy parameters and multi-objective function are used to approach the real world. In this regard, a deterministic two-step approach is used to consider the uncertainty in the proposed model. Finally, the performance and efficiency of the proposed model and solution methods are simulated in the numerical example and examined and suggestions are presented for using this model in the real world.