Identification and Prioritization of Grain Discharging Operations Risks by Using ORESTE Method

Hassan Jafari

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Identification and Prioritization of Grain Discharging Operations Risks by Using ORESTE Method

Hassan Jafari

Department of Marine Transportation, Faculty of Maritime Economics and Management, Khoramshahr marine science and technology University, Khoramshahr, IRAN

Abstract

Exposing the acts of unloader, sucker and Grab in the grain terminal of Imam Khomeini Port by manipulating ORESTE and Shannon's Entropy Methods in the three phases will lead to identification and prioritization of the grain discharging processes risks from the ship. In the first phase, by the analysis of the events and occurred incidents information bank about the surveyed matters and also setting brainstorming sessions with the terminal’s experts, 22 risks were identified. In the second phase by using from the Shannon’s Entropy, the criteria (occurrence frequency, severity and detection) were weighted. Then based on the criteria of determination of causes occurrence probability (occurrence frequency), the extent of its impact on process after occurrence (severity) and probability of its identification prior to having impact on the process (detection), the identified risks were scored in form of a scale from 1 to 10. Finally according to the obtained scores of each risk, the ORSTE decision matrix was conducted and subsequently in the third phase by using this method, all of the identified risks were prioritized. Based to the achieved results, the slippery one (falling from the stairs) and the risk of the operator’s chair shaking and the risk of the grain discharging dust and vacuuming the filters were attained the top priority respectively.

Cite this article:

  • Jafari, Hassan. "Identification and Prioritization of Grain Discharging Operations Risks by Using ORESTE Method." American Journal of Public Health Research 1.8 (2013): 214-220.
  • Jafari, H. (2013). Identification and Prioritization of Grain Discharging Operations Risks by Using ORESTE Method. American Journal of Public Health Research, 1(8), 214-220.
  • Jafari, Hassan. "Identification and Prioritization of Grain Discharging Operations Risks by Using ORESTE Method." American Journal of Public Health Research 1, no. 8 (2013): 214-220.

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1. Introduction

Nowadays the security management standard and the workplace hygiene, OHSAS 18001 is considered one of the management way for promoting the security level and the workplace hygiene in a lot of organizations using the unity IMS standard frame that is in the initializing and designing mood. The first step towards the implementation work should be the operational plans and designs due to detecting the risks and evaluating their dangers and developing some processes that concern to decrease the risks. Hereby, the organizations, after this accomplishment should review the information and following to it, the personnel must be aware of the peripheral dangers that may occur alternatively [1]. According to the above mentioned matter, initializing and implementing the unity IMS management system in the dedicated grain terminal will enhance the necessity to detecting and analyzing the risks and evaluate their prioritization. This study, based to the vast activity of this terminal, exclusively probe the grain discharging processes from the ship by using unloader, sucker and Grab for detecting and evaluating the risks. In this study by using the ORESTE and Shannon’s Entropy methods the identified risks were prioritized based on their frequency of occurrence, the impact which they will trace after the occurrence (severity) and the probability of the recognizing before the incident (detection).

1.1. Risks and Risks Evaluation’s Methods

Recently we encounter a high development in different industrial fields and due to this a variety of method are going to delve into an action of the risk evaluation, where nowadays more than 70 method s are exist to evaluate the risk qualitatively and quantitatively in the world. This method s may use to reduce the risks and detect them in order to control them. Most of the existed method s is well organized for this point of the matter and they are trusted and experienced for concluding how to control and reduce these risks [4]. Although they may have their advantages and disadvantages, therefore, it is a task of the hygiene and security system in the industry to focus on one pertained method and pick it up for the implementation in the organization.

Risk: In fact risk is the potential that a chosen action or activity (including the choice of inaction) will lead to an undesirable outcome, and in the security matter defines as the consequence and probability of a hazardous event or phenomenon [5].

Risk management: Without doubt, is one of the most important matters that human beings are involved in it and is continuous especially in the complex matters [6]. So having the necessities and be a warring of these matters may help to the preciosity of the decision making and risk management is considered one of these important asset that can be a key of the solution for such matters [7]. So this will enhance particularly when a complex set of factors and consequences are responsible for external and internal vulnerabilities decision causes in the room and the demands of the customers and the nominee should be prepared according to their benefits. This matter is of high sensation.

Also a set of widespread activity is running in the room of the ports per se that these notions will implies an influence on the outcome. in the most of the times, these phenomenon are not carry a high stress but they are so numerous that cause the possibility of loss, injury, or other adverse or unwelcome circumstance. However, by cursory and slight glance on the matter of the ports security it may not understand the risk management to its profound meaning. Risk management is a dynamic system that includes a set of risk cause identification, risk values estimation, risk programming and how to reduce and control these risky actions [8, 9]. It is notable to mention here, that risk management cannot eradicate the risk totally but it is an action that reduces it to the lowest point of the possibility.

Risk management is not superseding with the individuals’ experience but it can only help the experienced individuals to use their experience in the optimal situation. Risk management with high quality prepared information in the management delivery can help the managers to select the cost of the organization in the economic way of budget and use it on its appropriate way. Risk management can provide a far-sighted prospect for the experts to foreseen the probable risks and in order to prevent such risks; they can plan and perform in a preemptions manner [9]. In whole it is performable in the quality and quantity forms.

In fact, Qualitative Risk Management directly depends on experts experience and their own judgments during the process. However, such data and information during the process may fail to have correct and precise values and logic but they are better than nothing. Risk quality evaluation, in fact, is a degree and an outlet to the quantifying achievement. Albeit individuals’ attitudes and views for such measures and criteria are different and have their particular effect but manipulating this methods can be very fruitful and impressive. Qualitative Risk management is highly dependent on the system subject’s domain, judgments or acquired experience. Hence this method for data analysis and mathematical processes of the information refer to the very simple calculation because it is based on uncompromising mental techniques. It is worth of mention that the research carry some numerical values and data, but all of its work is founded on mental and subjective methods even for quantity risk and this may lead on the research with a little uncertainty [10].

1.2. Discharging Methods for Dry Cargo from Ship

Discharging by Grab: In this way which is still the same in the past 50 years, the bulk cargo is moved by a mobile arm attached to a grab along the jetty on a railway which is taken from the ship’s stevedore and then transferred into a hopper with a base situated on the jetty. Then, the bulk cargo is taken from under the hopper onto the conveyor belt and to the depot point or the silos. The discharging capacity of this method (by grab) is variable between 1000-500 ton per hour and subject to different elements including the average loading capacity, no. of the maneuvers per hour, the speed by which a grab is closed, movement speed of the carne carrying the grab, width, depth and the shape of the vessel’s stevedore and finally the skill of the operational personnel [10]. To increase efficiency in this method they have tried that the taken portion average weight be more in comparison to the grab. Previously, this proportion was around one but with the new wave of grabs, this amount has doubled. The dry bulk cargo that in discharging them this method is used are as Iron ore, coal, bauxite, alumina, phosphorous, other non- major bulk commodities like sugar, fertilizer, for coal industry and grain by a mobile smaller crane equipped by a grab.

Discharging by compressed air system: For different types of dry cargo that have special weight and low adhesion such as grain through compressed air system for discharging is used. This equipment functions as vacuum, suction and pressure. Vacuum method in collecting bulk cargo from several places and deliver them in one place uses vacuum and pressure methods to do so. Compression methods create dust and environmentally are drastic. Before erecting terminals, an economical and technical comparison between air compression and mechanical method should be taken. The capacity of the small mobile discharging unit on average is said to be 50 tons per hour, this is while the same amount for the different installed types on the gate cranes is 200 tons per hour. In some ports like Rotterdam of Netherlands the discharging compressed air system with the capacity of 1500-200 tons per hour is used. This system with special design for discharging ships has the capacity of between 100-150 thousand tons [10]. Other ways of discharging are available in Iran that is not of common use which is as follows:

Ÿ  Vertical conveyor belt

Ÿ  The bucket left system

Ÿ  Vessels equipped with discharging machine

2. Methodology

The present study in conducted on the descriptive nature and seek for the applied aims in which rest in the field and as the title suggest, its goal is to identify and prioritizing the following probable risks in the grain discharging processes from the ship by using ORESTE method and Shannon’s Entropy. Hence toward a successful achievability for its goal it has been accomplished in the three phases. Pursuantly In the first phase, by the analysis of the events and occurred incidents information bank about the surveyed matters and also setting brainstorming sections with the terminal’s experts, these sessions were conducted to list the probable risks that they weren’t occurred yet. In the second phase by using from the Shannon’s Entropy, the criteria (occurrence frequency, severity and detection) were weighted. Then based on the criteria of determination of causes occurrence probability (occurrence frequency), the extent of its impact on process after occurrence (severity) and probability of its identification prior to having impact on the process (detection), the identified risks were scored in form of a scale from 1 to 10. On which 1 is the least class rank and 10 is the highest class rank. Finally according to the obtained scores of each risk, the ORESTE decision matrix was conducted and subsequently in the third phase by using this method, all of the identified risks were prioritized.

2.1. Multi-Criteria Decision Making Method

If in one multi- criteria decision making case, goal, ranking option m is based on indicator K and for each indicator, a weak arrangement on the set of alternative is to be illustrated and the approximate significance (weight) of each indicator to be illustrated by another weak arrangement; the basics of each MADM methods being excel to ORESTE is to be established. This method provides a tool that is able to rank the decision making alternative completely and highlight the discrepancies eventually [11].

In 1979 and in a conference which was held on multi-criteria decision making issues, polytechnic university professor-Marc Roubens - in Belgium, presented his idea of one new multi- criteria decision making method called ORESTE or a collective ranking method compare the sequential evaluation alternative according to the presented indicators and made effort that with the help of ORESTE, to avoid the practical requirement in ELECTRE method in specifying indicators’ weight. After his 1st presentation in the conference, Professor Robins, described ORESTE in 2 articles. In 1980 in the 1st article which was limitedly published; in a nutshell, introduced the algorithm of this method and then in 1982 in another article which was published in the authorized European research magazine, described in details ORESTE and in a case study, he solved a real problem pertaining to choosing computer system. In this article, Professor Robins, in introducing his creative method states that: A is a set of the limited possible alternative which should be evaluated by some special indicators. This superior way with regard to the determiner’s priority on A by every indicator, a weak level may appear and also among the indicators creates a half-sequential Equation. Albeit, there proposed many different methods to create the superiority relationships, the most prominent one was presented by B Roy as ELECTRE; this is while, in our method, the information pertaining to weight is replaced by the half-sequential Equations [12].

If we consider A as a limited m set, these alternative shall be analyzed by the set C including k. in this method, the relative importance of each index is not specified by their weight, but it is stated by a superiority structure on the index C, which is described under a weak level. The so called weak level is stated in a full and transition Equation of S, which is consisted of P and I Equations. P or superiority show discrepancy and I shows incuriosity, which the representative of superiority coordination among the criteria. Also for each of the criteria of j = 1,…, k, a superiority structure in the set A is described, which is similar to C criteria of the superiority structure is transitional and consisting of a set of P and I relationships [13]. Thus, the 1st superiority structure is established based on criteria’ relative importance to each other and the 2nd superiority structure also created on the optional set and according to each one of them individually. After formation of the abovementioned 2 superiority structures, we should pay attention to the preliminary ranking of these structures. To do so, we may use Besson average ranking method. In such a way to refer to the superiority structure 1st and according to its rank in comparison to all other criteria, dedicate numbers 1-K (k index) and for all alternative numbers 1-m (m indicator). Then we obtain the mean from the maximum or the minimum dedicated number which is constructed based on the superiority structure enjoys similar superiority or I (Equation1). In other words, instead of dedicating grades 1and 2 to the so called two criteria (alternative), we shall grant it to both ranks (1/5); therefore, with Besson average ranking, the priorities shall turn to ranks. The obtained rank for criteria shall be represented by rk and the gained rank for each option in each index shall be represented by rk(m) [11].

(1)

X1 is the maximum amount while X2 is the minimum amount and is regarded the average distance.

ORESTE Method to perform the ranking process has 3 phases as the following:

Projection of alternative intervals d(o,mk) : Estimating in ORESTE method is based on using the hypothetical matrix called position- matrix that in all its columns the decision alternative are organized from the best to the worst and accordingly the columns are arranged based on the criteria ranks. By scanning matrix’s members eventuating from the main diameter, the best situation are listed on the left side of the diameter and the worst are at the right side. Then a zero offset is located at the very end of the left side of the diameter and all the formed pictures are considered and their intervals are determined from the zero offset which is shown by d (o,mk) as it is shown below [10]:

(2)
(3)

The interval estimation d (o,mk), which was explained above is executed for different modes including:

Direct linear estimation: In this mode to perform the interval estimation d (o,mk) from rk andfor option m in k index we shall comply to Equation (4).

(4)

Indirect linear estimation: In this mode, pictures’ intervals from the offset point are computed as the following using Equation (5):

(5)

Non-linear estimation: In non- linear scanning mode to determine the pictures distances from the desired origin we use Equation (6).

(6)

To achieve more general conditions, Equation (6) shall change as follows:

(7)

And finally if we add the normal weights of Equation (8) shall be gained:

(8)

In this regard, with respect to some amounts, the R distance of d shall be illustrated.

Global ranking of the alternative interval R(mk): By determining the interval of the scans pertaining to matrixes’ members, the sources’ position and the global ranking shall be implemented by one of the abovementioned styles. Generally speaking, selecting every mode or different R amounts for scanning and determining intervals d(o,mk) with the solemn intention of creating an impact on their position in comparison to each other which in progress, the intervals with the assistance of Besson average ranking method and consequently the issue shall revert to its original sequential essence. The result of this ranking equals to the obtained rank by Besson method and the intervals of is in a way that we shall have the following e.g [12].

(9)

The obtained ranks are called the total ranks and all exist in the following scope:

(10)

Thus an incremental sequential structure is modified based on and with regard to the following Equations:

(11)
(12)

An option that the relative is smaller is more appropriate and a better rank shall be awarded to it; in other words, it is the top option in which the total sum of all its criteria is less than the others.

2.2. Shannon Entropy and Objective Weights

Shannon and Weaver proposed the entropy concept, which is a measure of uncertainty in information formulated in terms of probability theory. Since the entropy concept is well suited for measuring the relative contrast intensities of criteria to represent the average intrinsic information transmitted to the decision maker, conveniently it would be a proper option for our purpose. Shannon developed measure H that satisfied the following properties for all pi within the estimated joint probability distribution P:

It is proved that the only function that satisfied these properties is:

(13)

Shannon’s concept is capable of being deployed as a weighting calculation method, through the following steps:

Step 1: Normalize the evaluation index as:

(14)

Step 2: Calculate entropy measure of every index using the following equation:

(15)

Where

(16)

Step 3: Define the divergence through:

(17)

The more the is the more important the criterion th

Step 4: Obtain the normalized weights of criteria as:

(18)
2.3. Results

In the first phase, by the analysis of the events and occurred incidents information bank about the surveyed matters and also setting brainstorming sessions with the terminal’s experts, 22 risks were identified. These risks are represented in Table 1.

In the second phase by using from the Shannon’s Entropy, the criteria (occurrence frequency, severity and detection) were weighted. The result presented in the Table 2.

Then based on the criteria of determination of causes occurrence probability (occurrence frequency), the extent of its impact on process after occurrence (severity) and probability of its identification prior to having impact on the process (detection), the identified risks were scored in form of a scale from 1 to 10. on which 1 is the least class rank and 10 is the highest class rank. Finally according to the obtained scores of each risk, the ORESTE decision matrix was conducted and subsequently in the third phase by using this method, all of the identified risks were prioritized in the following steps.

2.4. Forming a Superiority Structure on Alternative & Criteria’ Set

For ranting purposes using this method, 1st of all there should be 2 superiority structures for the set of alternative & criteria. To establish the superiority structure for criteria out of the obtained weights we have used Shannon entropy method. Similarly, for the set of alternative & based on the criteria individually & by using the decision- making matrix’s data, the superiority structure as it is illustrated in Table 3, is formed.

Table 3. Superiority structure of alternative & criteria’ set

2.5. Specifying the Primary Rating on The Alternative- Criteria Set

By having the abovementioned relations & structures & using Besson average rating, the primary rating of the alternative & criteria is computed. Accordingly, no. 1-15 was given to index & the rk is formed. The mentioned processes are applicable for alternative, too. Table 4 presents the primary indexes & options.

Table 4. The primary rating on the alternative- criteria set

2.6. Projection of Alternative’ Intervals

By obtaining the primary levels for the set of criteria & alternative based on each index, we have used direct linear evaluation method for gaining the intervals. The evaluated intervals for all alternative & based on the criteria are presented in Table 5.

Table 5. Evaluated intervals for all alternative

2.7. Aggregation Phase

By obtaining R(mk) for all the alternative of the criteria, the aggregating step should be taken; in other words, to be computed for all alternative that its amount equals the general sum of the computed R(mk) for each option regarding each index. Thus, R(mk)is shown for all alternatives in Table 6.

2.8. Comparing the Results & Specifying the Top Choice in ORESTE Method

Finally to determine the top choice, we compare the aggregation results from the decision- making phase. In this section the less the total sum, the higher the rank will be.

3. Conclusion

Exposing the acts of unloader, sucker and Grab in the grain terminal of Persian Gulf by manipulating ORESTE method and Shannon's Entropy in the three phases will lead to identification and prioritization of the grain discharging processes risks from the ship. In the first phase, by the analysis of the events and occurred incidents information bank about the surveyed matters and also setting brainstorming sessions with the terminal’s experts, 22 risks were identified. In the second phase by using from the Shannon’s Entropy, the criteria (occurrence frequency, severity and detection) were weighted. Then based on the criteria of determination of causes occurrence probability (occurrence frequency), the extent of its impact on process after occurrence (severity) and probability of its identification prior to having impact on the process (detection), the identified risks were scored in form of a scale from 1 to 10. Finally according to the obtained scores of each risk, the ORSTE decision matrix was conducted and subsequently in the third phase by using this method, all of the identified risks were prioritized. According to the final result, the risk of being slippery (falling) from the stairs, operator chair shaking and the risk of the dust of the discharging grain and vacuuming the filters were obtained the highest priority respectively and the risk of the sunshine’s reflection toward the operator cabin and the risk of the manifold pressure toward the muscles and the risk of the falling into a sea were obtained the least priority respectively.

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