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In spite of all the efforts in different industries to reduce the number of undesirable accidents, a lot of events always threat industrial societies. These events often cause huge damages to the environment, facilities and even in some cases, fatalities and disabilities for people. Therefore, it is important to predict these probable accidents and plan to prevent them. Hence, this research proposed to analysis hazards in one of the most important components of natural gas distribution network which is city gate station (C.G.S). In this analysis firstly, critical hazards are identified and classified based on HSE standards. Then for decreasing risk of selected hazards, a survey is done on a specific c.G.S according to previous accidents. Finally, some suggestions are offered to improve existing preventive activities.gas

The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled "search in forest (SIF) optimizer" to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIF optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intra-group search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms.

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PurposeThe purpose of this paper is to design petroleum products’ supply chain management, which includes efficient integration of suppliers, manufacturers, storehouses and retailers.Design/methodology/approachThis paper proposes that a three-level supply chain will be turned into a bi-level supply chain of petroleum products by simultaneous integration of the middle level with the upstream and downstream levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels.FindingsThe concepts of the design, structure and outputs are led by the model's solution. The model also responds to the variations in the market via coordination in the related decisions to the distribution, production and inventory issues, and also coordinating between the demands and production.Research limitations/implicationsThis paper has limited its analysis to definite values due to the over-expansion of calculations and analysis. Future works can study other aspects of the proposed model for a multi-level petroleum product supply chain in different states of certain parameters and time zones.Practical implicationsThe designed model can directly and transparently help the oil managers and decision-makers lower the costs of manufacturing, distribution and sales with respect to the determined criteria.Originality/valueThis paper establishes that effectiveness of the dynamic petroleum materials supply chain design will increase by considering maintained and increased production costs and coordinate management flows at all levels by supply chain creation’s integration.

Contaminant materials in lubricants are signs for prediction and measurement of wear in engines. Therefore oil monitoring is considered as one of the most effective techniques for maintaining the heavy equipments. However based on oil analysis data, the identification of engine problem is very hard due to different contribution factors. Hence, this paper is purposed to analyze the wear behavior of some sets of selected heavy equipment engine based on documented oil analysis data during two years in different conditions to track engine failures using trend analysis. Furthermore the selected equipments are divided into two major groups (plantation and forestry, and general construction) based on their environment, to show the effects of conditions on the engine wear behavior.

Since late in the 20th century, various heuristic and metaheuristic optimization methods have been developed to obtain superior results and optimize models more efficiently. Some have been inspired by natural events and swarm behaviors. In this chapter, the authors illustrate empirical applications of the gravitational search algorithm (GSA) as a new optimization algorithm based on the law of gravity and mass interactions to optimize closed-loop logistics network. To achieve these aims, the need for a green supply chain will be discussed, and the related drivers and pressures motivate us to develop a mathematical model to optimize total cost in a closed-loop logistic for gathering automobile alternators at the end of their life cycle. Finally, optimizing total costs in a logistic network is solved using GSA in MATLAB software. To express GSA capabilities, a genetic algorithm (GA), as a common and standard metaheuristic algorithm, is compared. The obtained results confirm GSA’s performance and its ability to solve complicated network problems in closed-loop supply chain and logistics.