Improving participation in voluntary activities in Iran is important because increasing productivity among young people, given the country's young population, contributes to community sustainability. Therefore, a better understanding of volunteer motivation in Iranian youth is needed. The Volunteer Functions Inventory (VFI) for assessing volunteer motivations has good psychometric properties and is adapted to several languages, but no validated Iranian translation yet exists. The purpose of this study was to investigate the psychometric Characteristics of voluntary functions inventory in members of Iranian Red Crescent Society. Sample size was 595 members of Youth Organization of Iranian Red Crescent Society from 31 provinces and 175 cities of Iran that were selected by multi-stage cluster sampling method and responded to voluntary functions inventory. Data were collected using a demographic sample and voluntary functions inventory (VFI). Confirmatory factor analysis using principal components method was used for data analysis. The results of the present study showed that the voluntary functions inventory had validity and reliability. Also, the factor structure showed that 29 items and 6 factors well assess people's attitudes to volunteering, and the structure of this inventory was well-fitted and confirmed all goodness of fit models. The present study provides the use of the Iranian translation of the Voluntary Functions Inventory (6 scales and 29 items) to assess volunteer motivation among young Iranian volunteers.

Production planning is one of the most important challenges in steel industry. Order planning is one major part of this aspect according to the fact that production is usually planned based on customer orders in this industry. Inventory planning is also vital for maximizing capacity utilization. The proposed model in this paper is capable of selecting order, assigning finish time of each order, and determining open orders in each process by considering order planning and inventory planning simultaneously. The model could have a key role in improving production planning of steel companies by on time delivering of the orders, reducing work-in-process, and balancing utility of capacities. Finally, a heuristic algorithm based on genetic algorithm is proposed and its efficiency evaluated in an empirical case study of Iran alloy steel enterprise.

When it comes to providing aid to the victims of natural and unnatural disasters, the main goal of a relief chain is to provide the items needed by the victims such as water and food, medicine, shelter and other necessities to reduce the number of deaths caused by reduce the occurrence of disasters as much as possible; therefore, designing, developing and implementing a relief chain can play an important role in finding a suitable answer. The most obvious differences in dealing with the relief supply chain are the unpredictability of demand in terms of time, place, type, scale and volume. Other reasons such chains are the sudden occurrence of a large amount of demand and a very short opportunity to provide a large amount of goods, lack of resources including goods, relief forces, appropriate technology, transportation capacity, the need to provide timely and sufficient supplies after the accident, and the risks in the relief environment. In the present research, a mathematical model for the location-inventory problem for planning response to casualties is presented; also, due to the NP-hard nature of the problem considered, meta-heuristic algorithms were used to solve it.

The production-inventory models are traditionally adopted for manufacturing systems. A relatively new area of production-inventory planning is related to livestock growing process. This research aims to propose a class of production-inventory model for new items titled growing items. In such a case, a rancher orders a quantity of newborn livestock like chicks or lambs, raises them to an appropriate weight during the growing period, slaughters them and then sells them to the meat market. The goal is to calculate the economic order quantity of the growing items at the start of a growing cycle, the optimal length of the growing cycle, and the optimal total profit. We, in this research, extend the previous work to the case of joint growing and deteriorating items, where livestock grow at growing period, and, additionally, the slaughtered livestock may be deteriorated during the sales period. Moreover, since some amount of slaughtered livestock is waste and should be disposed, a weight reduction factor is considered when livestock are slaughtered. The inventory models are constructed for this case, a heuristic solution algorithm is presented, a numerical example is discussed, and finally, sensitivity analysis is carried out to investigate the applicability of the problem.

On time supply of materials in construction projects is very important. Therefore, coordinating a project with key suppliers, while ensuring the timely supply of materials, make the minimum costs of project. One way to create this coordination is integrated inventory planning which has been accomplished in this study. It is necessary to mention that such a model is considered NP_hard in terms of complexity class, because the model is compound of the inventory planning and planning with limited resources, that in literature, the class of programs with limited resources is considered NP_hard. Therefore, in this paper while introducing the inventory planning model in project supply chain, a model based on genetic algorithm is proposed to determine the solution. The proposed algorithm has been run for the suitable size problem and the results show that the introduced algorithm, in actual sizes, within a reasonable time, provides an appropriate response.

In this paper, a production inventory model proposed for deteriorating items which deteriorating rate is assumed to follow weibull distribution with known parameter. The purpose of this paper is to find the economic selling price for producer, optimal production time and the number of delivery Periods simultaneously that maximizes profits for firm. A solution procedure is found to determine the optimal decisions and a numerical example is solved to endorse the solution procedure. Finally, sensitivity analysis of the optimal solution is investigated.

Generally, the inventory routing problem occurs in a supply chain where customers consider the supplier responsible for inventory replenishment. In this situation, the supplier finds the answer to questions regarding the time and quantity of delivery to the customer as well as the sequence of customers in the routes. Considering the effect of production decisions on answering these questions, the present paper examines the integrated decision making on production, routing and inventory in a two-echelon supply chain composed of a manufacturer and multiple retailers. Transshipment, as a policy in supply chain logistic which increase integration and decrease inventory cost, is also allowed between retailers. The mathematical formulation for the problem is developed and an adaptive large neighborhood search heuristic is proposed to solve this complicated problem. The results of numerical experiments showed that the solutions yielded by the heuristic method have high efficiency.

This paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. This problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life time into a lot sizing to be shipped with multi-vehicle to multi-distribution-center to minimize multi-objective such as setup costs between products, holding costs, shortage costs, spoilage costs, transportation costs and production costs. There are many investigations on production/distribution planning area with different assumptions. However, this research aims to extend this planning by integrating an inventory system in which for each distribution center, net inventory, shortage, FIFO system and spoilage of items are calculated. A mixed integer non-linear programming model (MINLP) is developed for the considered problem. Furthermore, a genetic algorithm (GA) and a simulated annealing (SA) algorithm are proposed to solve the model for real size applications. Also, Taguchi method is applied to optimize parameters of the algorithms. Computational characteristics of the proposed algorithms are examined and tested using t-tests at the 95% confidence level to identify the most effective meta-heuristic algorithm in terms of relative percentage deviation (RPD). Finally, Computational results show that the GA outperforms SA although the computation time of SA is smaller than the GA.