The carrier collaboration problem for logistic optimization is a fundamental part of the sustainable supply chain, and has increasingly been of concern in recent years. This study proposes a novel nonlinear mathematical model to address the carrier collaboration problem. It involves a set of heterogenous vehicles with specific capacities for serving Perishable Products to several pickup and Delivery nodes. Products are corrupted at a constant rate. The purpose is simultaneously finding routes to maximize profits in order to enhance customer satisfaction which is dependent on the freshness of delivered Products. There are various applications of the proposed model in the environment which can help reducing transportation emissions, eliminating empty back hauls. Further, the proposed model uses a Hybrid Genetic Algorithm (HGA) is proposed to solve this problem due to improve the quality of initial solutions Variable Neighborhood Search (VNS) method is developed. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time.

In recent years, much more attentions have been focused on siRNA-based gene therapy. This approach is based on PTGS (Post-transcriptional gene silencing). Due to some defects in viral vectors, non-viral vectors have been used to deliver nucleic acids into target cells. Although, transfection efficiency in non-viral vectors is less than viral ones, but safety of non-viral vectors is much more. Characteristic features of siRNA, such as high compatibility, application in low doses and its versatility make it suitable in the gene therapy field. However, some challenges, such as stimulating immune system and Off-target silencing will be remain. In this review article, we express bottlenecks existing in siRNA Delivery into target cells. According to the information, with further development of siRNA Delivery in the future, it could be a promising approach in treatment for a variety of genetic diseases.

Determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research. Perishable Products have a short life and due to their deterioration over time, they cause great damage if not managed. Many industries, retailers, and service providers have the opportunity to increase their revenue through optimal pricing of Perishable Products that must be sold within a certain period. In the pricing issue, a seller must determine the price of several units of a Perishable or seasonal Product to be sold for a limited time. This article examines pricing policies that increase revenue for the sale of a given inventory with an expiration date. Booster learning algorithms are used to analyze how companies can simultaneously learn and optimize pricing strategy in response to buyers. It is also shown that using reinforcement learning we can model a demand-dependent problem. This paper presents an optimization method in a model-independent environment in which demand is learned and pricing decisions are updated at the moment. We compare the performance of learning algorithms using Monte Carlo simulations.

In this paper, a three-echelon supply chain, consisting of suppliers, a number of distribution centers (DCs), and a number of retailers (customers) is modeled in form of the integrated bi-objective inventory-location – routing problem (ILRP), in a way that Perishable Products are delivered to the customers in a limited time horizon, consisting of several time periods. The retailers’ demand is stochastic and is applied on the model by the concept of scenario. The transportation fleet is heterogeneous, and distribution centers use a timetable, which will prevent interference of the vehicles operation and allocation of a vehicle to more than one distribution center in each time period. Three methods of calculating the distance to the ideal point and in order to solving the model, have been investigated and analyzed and have brought some examples for expressing proposed methods. At the end, besides concluding the discussion, recommendations are made for future studies.

One of the most challenging tasks in the Perishable Products industry is to control the inventory of Products and find the optimum location to store them. This paper tries to respond to these challenges in a way that is close to reality. In this paper, a location-inventory model has been studied for a supply chain with Perishable Products, considering the dependence of demand to price. The investigated multi-Product and multi-period supply chain includes manufacturers, distributors, retailers and customers. Products and centers of this chain are equipped with Radio Frequency Identification (RFID) technology. The location-inventory model, by simultaneously capturing strategic, tactical and operational decisions, can be very effective in increasing the supply chain efficiency. For this purpose, a mixed integer nonlinear programming model is presented. The purpose of the model is to maximize the profitability of the supply chain, which is achieved by the simultaneous decision-making of location, inventory, pricing and demand. Finally, in order to show the efficiency of the proposed model, numerous examples with various parameters are solved and sensitivity analysis is done. The results show that as the dependence of customer demands on price increases, it is inevitable that the supply chain sells the Product to customers at a lower price and lower profit, and this causes an extreme decrease in supply chain profit. The lower dependence on the supply chain results in higher profit.

In this study, a two echelons supply chain system in which a supplier is producing Perishable Product and distribute it to multiple customers is considered. By allowing lateral transshipment mechanism, it is also possible to deliver Products to some customers in some periods in bulk, then customers using their own vehicle to transship goods between each other seeking further reduction in the overall cost. The aim here is minimizing the Production, inventory carrying cost, and distribution as the first objective, and transshipment cost as the second objective, which is contrary objectives, without facing any shortage anywhere in the chain during the planning horizon. This problem is formulated as a bi-objectives mixed integer programming (BOMIP), and then a proper Pareto front as a set of multiple decision alternatives is provided using NSGAII and NRGA approach. Novelty of this research is providing a bi-objectives mathematical modeling of Perishable Product inventory routing with Production and transshipment (BO-P-PIRPT) that help the decision maker to choose the best mixture of routing and transshipment.

In the competitive market of Perishable cargoes, determining the price of the Product and making opportunities for customers to accelerate the sale of goods through discounts is crucial. Over the life of Perishable goods, generally its value reduced to the customer, in this situation, to encourage the purchase, policies such as a discount or reduced price sales policies can be effective. Literature has not provided a model for determining the optimal time to announce a price reduction. While early or late prices announcement could reduce profit, the aim of this paper is to analyze such a model at the level of an enterprise. In the modeling, we assumed that by announcing price discount, tangible changes occur in demand, and demand is a function of price and time. The demand rate in the discount time is decreasing in the beginning of the time and then declining over time. The purpose of the model is determining the optimal price, discount time and order size to maximize the total profit in a single period. After modeling, concavity of the profit function is considered and optimal pricing and discounts are exclusive. Then, a heuristic algorithm derived from the literature was used in order to determine the optimal price, the optimal discount time and the optimal order quantity.

Background and Aim: What constitutes adequate dialysis has been debated in the nephrology literature over the past years. We believed that short and infrequent dialysis session contributed to poor outcome. This result provides additional evidence that (KT/V) in a flawed concept upon which to based the dose dialysis in general. Base on published evidence from many source we proposed a new index of adequacy of hemodialysis to be called hemodialysis Product (HDP).Materials and Methods: The prospective study was performed on 100 dialysis patient in the Imam hospital dialysis ward in Tehran during 2002. delivered (KT/V) and (HDP) achieved for any patient separated.Results: 100 patients including 44 female and 56 male were studied. Result has shown (KT/V) d≥l in 43% and (KT/V) d<1 in 57%. HDP in patient with (KT/V) d> 1 were ≥36. HDP in patient with (KT/V) d < 1 were < 36. Correlation between (KT/V) d and albumin was significant (p< 0.03, Albumin ≥4mg/dl). Correlation between (KT/V) d and cholesterol, hemoglobin, creatinine were not significant. Correlation between (KT/V) d and HDP was significant (p< 0.001).Conclusion: Finding above has shown that higher weekly delivered (KTN) correlate with dialysis result increase hemodialysis Product (HDP). More frequent and session dialysis results increase in HDP, provide better weekly (KT/V) d with normal albumin (show good caloric and protein intake).