A Queuing Model to Reduce Energy Consumption and Pollutants Production through Transportation Vehicles in Green Supply Chain Management

Transcription

1 A Queuing Model to Reduce Energy Consumption and Pollutants Production through Transportation Vehicles in Green Supply Chain Management Amir Azizi 1 and Yones Yarmohammadi 2 Faculty of Manufacturing Engineering 1, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia 1, Allame tabatabai university, Industrial Engineering Department, Tehran, Iran 2 s: amirazizi@ump.edu.my 1,Yarmohamadi_65@yahoo.com 2 Abstract: - Environmental pollution and shortage of nonrenewable energy resources have increased governments and people's concerns in consuming green products. Green products are the result of green supply chain management in organizations' performing strategies in order to reduce waste and environmental pollutants and take a step towards saving energy resources. Queuing and transportation theories have been used in order to reduce energy consumption in a green supply chain. The main goal of this model is a minimizing function with coefficient c which minimizes vehicles' transportation time and waiting time in supply chain centers. In order to validate the model, transportation data of an Iranian dairy products company was used and modeling was performed using LINDO Software. The results showed that proper allocation of the present vehicles with certain capacities and employment of queuing theory in the green supply chain caused the reduction of energy consumption through optimizing transportation and waiting times in the green supply chain. Key-words:- Green Supply Chain Management, Renewable, Energy Consumption, Queuing Theory, Production, Transportation. 1 Introduction Green supply chain management is a product of traditional supply chain (Fortes, 2009)[5]. Quality revolution in the late 1980s and supply chain revolution in the late 1990s directed business towards being sensitive to the environment (Srivastava, 2007)[17]. This issue gradually became public in the society. Environmental efficiency and recycling processes are now considered as important assets in order to reach to the best method (Srivastava, 2007)[17]. Rao & Holt (2005)[12] believed that the concept of green supply chain management includes the following environmental plans. 1- Inbound logistics, 2- Internal production or supply chain, - Outbound logistics; and in some cases, 4- Reverse logistics. Where raw materials' suppliers, service contractors, vendors, distributers, and final consumers cooperate to reduce or eliminate harmful environmental effects of their works. As Srivastava (2007)[17] believes, a green supply chain is defined by considering environmental problems in supply chain management including product design, material and source selection, construction and production processes, final product's delivery to the customer, and product's management after its consumption and the end of its useful life cycle. Amemba et al. (2013)[2] modeled green supply chain elements as green purchasing, green design, green operation, reverse logistics, green production, and waste management. As the first researchers suggested a system to organizations for products which can potentially be recycled. Further researches investigated other aspects ISBN:

2 of green supply chain management considering environmental efficiency. Reverse logistics is one of green supply chain fields on which many researches have been conducted. Of the researchers conducting case studies in this field, Shih (2001)[16], Tibben -Limbke (2002)[20], Srivastava & Srivastava (2005)[18], Nagorney & Toyasaki (2005)[11], Min et al. (2006)[9], and Mutingi (2014)[10] can be named. Waste management is another concept hidden in green supply chain management. Roy & Whelan (1992)[13] were the first researchers working in this field. In their research, they presented a model to reduce electronic waste. Other researchers such as Sarkis & Cordeiro (2001)[15] and Nagorney & Toyasaki (2005)[11] focused their efforts on recycling. A green supply chain model in terms of marine transportation containers has been proposed to overcome issue of green production (Fortes, 2009)[5]. The concept of green production continued to live by researchers such as White et al. (2003)[21]. Today, environmental awareness in integrated design and operations of supply chain networks has become vital at global levels. Sundarakani et al. (2010)[12] have investigated carbon footprints, relying on Eulerian transportation method throughout the supply chain; they realized that carbon footprints in any of supply chain stages can be considered as a serious threat which requires attention in the supply chain design. Eltayeb et al. (2011)[4] evaluated real environmental, economic, and intangible consequences of supply chain design approval and showed that green supply chain designs have positive consequences on environmental and economic consequences, cost reduction, and intangible results. The results of their researches showed that in addition to economic advantages and the reduction of their cost through designing environmentally friendly products and their packaging, companies have benefits for the environment in the form of waste reduction and better exploitation of resources. Kumar & Chandrakar (2012)[8] regarded the importance of resorting to green supply chains from the viewpoint of supply chain harmful effects on the environment in the form of waste production, global warming, and acid rains; therefore, they reviewed systems' supply chains, focusing on their optimization and implementation. Presenting a conceptual model, Jabbour et al. (2014)[6] proved that attention must be simultaneously paid to quality management, environmental management maturity, and green supply chain for green performance improvement. Green supply chain executives in pioneering companies attempt to benefit from green logistics and their environmental performance improvement in the whole supply chain as a strategic weapon to obtain a sustainable competitive advantage by creating satisfaction from an environmental viewpoint throughout the supply chain (Boks & Stevels, 2007)[3]. In general, it can be said that green supply chain management integrates supply chain management with environmental requirements in all stages of products design, raw materials selection and provision, production and construction, distribution and transfer, delivery to customers, and finally recycling management after consumption in order to maximize energy consumption efficiency along with supply chain total performance improvement (Sarkis, 2006)[14]. 2 STATEMENT OF THE PROBLEM Today, each country's sustainable development is guaranteed by optimally maintaining and using limited and non renewable resources in each country; ISBN:

3 various attempts have been performed in this regard by governments of which the application of green rules and principles such as using environmentally friendly raw materials in productive and industrial centers, reducing fossil and oil energy resources, and recycling papers and waste can be mentioned. The application of governmental regulations in order to receive environmental standards and the increasing demands of consumers for supply chain green products, which include all the processes related with production ranging from raw materials provision to products delivery to the final consumers throughout the chain, has resulted in the creation of a new concept of green supply chain management. Adopting investment strategies aiming to improve supply chain environmental performance has many advantages such as saving energy resources, reducing pollutants, reducing or eliminating waste, creating value for customers and therefore, increasing productivity for productive and services organizations; in this paper, the effect of queuing theory on reducing waiting time and optimizing energy consumption in the green supply chain will be investigated. Queuing Theory Generally speaking, nobody likes to be waited; reducing waiting time, however, requires extra investment. In order to decide whether this investment must take place or not, it is important to know the effect of this investment on the waiting time (Adan & Resing, 2002)[1]. How can returns and cost reach to a balance? Here, queuing theory is defined to solve the problem. Queuing theory includes mathematical studying of waiting queues and their related random processes. A queue system can be defined as customers who enter the system to receive services and if no service is available, they wait for it. Important fields to apply queuing models are production systems, warehousing and transportation systems, communication systems, and information processing systems. Queuing models can be especially applied to designing these systems in terms of design, capacity, and control (Adan & Resing, 2002)[1]. Queue formation is a social phenomenon; if it can be managed such that both the unit and the person that deliver services benefit from it the most, it will be useful for the society. Whether a human being or not, the unit asking for a service is a customer and the unit presenting the service is known as the server. Queue length, customers' waiting time in the queue, and server non-occupation are of the criteria evaluating queuing systems. According to the above mentioned explanations, it is clear that each process of the green supply chain acts as a queuing system. As an instance, if organizations asking to purchase raw materials are considered as the customer and the suppliers of such materials are considered as the server, the employment of queuing problem solving approaches seems possible and suitable. There are different organizations demanding raw materials; they definitely expect to receive them in a specific time period. A supplier can prioritize organizations based on their policies and present special customers' services under different conditions. This is so while a special capacity has been considered for the supplier and serving speed is also measurable, since a green purchasing process, like other queuing systems, provides each organization with the possibility of evaluation through measuring mean waiting time to receive raw materials or the queue length. The above analysis can also be employed in considering each process of green production, distribution, and marketing. ISBN:

4 Queuing Process Characteristics Customer Entrance Process - Customers may enter the system one by one or in groups (Adan & Resing, 2002)[1]. The arrival process is usually introduced as the arrival rate which is defined by the average number of arrivals over a second (Stallings, 2000)[19]. The time intervals between two arrivals are usually considered as independent (Adan & Resing, 2002)[1]. Distributions of arrival process are regarded as exponential distribution, uniform distribution, and binomial distribution. Customer Behavior - Customers may be patient and tend to wait (for a long time). Some customers, however, may not be patient and leave the place after a while (Adan & Resing, 2002)[1]. Serving Time - When a unit is allocated to a specific canal, the time it takes until that unit receives the service may be a fixed or variable time. This component is considered as the mean serving time to each arrival without calculating its waiting time in the queue (Stallings, 2000)[19]. Serving times can be considered as dependent on the queue length (Adan & Resing, 2002)[1]. The Number of Servers - There may be one or several servers for serving customers. System Capacity (Waiting Room) - Regarding the number of customers, there may be some limitations in the waiting room (Adan & Resing, 2002). It means that if there are m severs and no more waiting rooms, m is known as the system capacity. System capacity component is not defined for an unlimited waiting room. Population Size - Customers entering the system to receive services build the population. Queue Order - Customers may enter the system one by one or in groups (Adan & Resing, 2002)[1]. There are different probabilities for the serving order. A- FCFS; where the first customer entering the system is also served first. B- LCFS; where the last customer entering the system is served first. C- Priority Discipline; where some priorities are defined to serve customers in which customers with shorter serving processes, customers who are in rush, etc. can determine serving priorities. D- Random Order; There is no special order for serving. In order to describe a queuing process, some letters and slashes are used as A/B/X/Y/Z, where A refers to time intervals' distribution of customers' arrival; B refers to serving type; i.e. service time probability; X refers to serving time canals; Y refers to system capacity; and Z refers to order (Kendall, 1953)[7]. 3 METHODOLOGY In order to investigate queuing theory function in energy consumption reduction in a green supply chain, queuing theory in this paper is examined in the green supply chain s transportation sector. It is clear that analyzing more complicated systems of a green supply chain requires the employment of efficient software in modeling analysis and simulation of the supply chain. LINGO Software as a powerful tool provides supply chain engineers and analyzers with a framework which help them model, simulate, and analyze the obtained results of the model development. Then, some feedbacks can be entered into the system and some modifying efforts can be applied in order to improve system efficiency. Besides, LINGO Software can provide the possibility of sensitivity analysis and scenario writing in the system. The aim of this research is to allocate transportation vehicles to different centers routes of green supply chain layers using the queuing ISBN:

5 theory. In this model, service distribution function is probabilistic and in the form of Poisson distribution regarding the variability of vehicles entrance and exit times. As a result, the time between the entrances of two vehicles in this server follows an exponential distribution. In this model, several parallel servers have been used, since this model has the flexibility of selecting the number of its servers. Furthermore, system capacity is unlimited in the selected model. Consequently, M/M/S/ /Pri model has been used. Figure 1 shows a holistic view of the supply chain in question. Figure 1. A Holistic View of the Supply Chain in Question One of the samples in a queue form has been shown in figure 2, which in both sides of warehouse a long queue has formed. Figure 2. A Sample of the Formed Queue in a Supply Chain Center Here, a supply chain of a dairy products company with 1 warehouse, 2 companies, 2 distributers, and 1 recycling center is considered where the total number of its vehicles is equal to 24 with 10 ton capacity and the total raw materials going from the warehouse to the companies is about 360 tons. The following are true for this supply chain: 1- The queuing model is in the form of M/M/S/ /Pri. 2- The total number of transportation vehicles with their capacities is known. 3- Severs in each center include two sectors of loading and unloading vehicles whose queuing systems act separately. 4- Severs of loading or unloading sectors can include several parallel servers. 5- The distances between servers must be specific. 6- There is a direct relation between unloading rates in centers with the distance between two centers. 7- The number of parallel servers, whether in unloading or loading sector, is fixed and specific. 8- The waste amount from market to the recycling center is specific. 9- The speed of vehicles between different centers is clear. 10- Production speed rates in companies' centers are clear. 11- The waste percentages of companies are clear. 12- The proportion of the final product which must be kept in each order in companies to the order type is clear. 13- The exit rate of the loaded vehicles is clear in each center. 14- Vehicles commute between centers. 15- It is hypothesized that the system is sustainable in each center: < 1 ISBN:

6 The model s indices are as follow: I: Warehouses' Centers K: Distribution Centers J: Companies' Centers L: Recycling Centers The Model s Parameters have been presented below: Z= The transfer volume between two centers Z 0 = The waste amount from the market to the waste warehouse = Waste percentage in company j = Production speed in company j j= The product amount which is stored in company j after production TNV= The total number of the available vehicles in the entire chain VC= The Vehicle transportation capacity = The vehicles' arrival rate to be unloaded = The unloaded vehicles' exit rate S= The number of parallel servers in each center's unloading sector = The vehicles' arrival rate to be loaded = The loaded vehicles' exit rate =The number of parallel servers in each center's loading sector = The non-occupation percentage in the zero situation LQ= The mean length of queue WQ= The mean waiting time in the queue W= The mean waiting time in the system P= The time percentage of servers' occupation D= The distance from the center (m) T= The displacement time unit in terms of distance unit C= The percentage of pollution creation in terms of transportation vehicles' functioning time Decision Making Variables NV= The number of vehicles in different centers routes between supply chain layers NT= The number of vehicles' trade between centers The used model in this research is as follows: In this pattern, the time function is used. C coefficient has been used in calculating pollutants' consumption in terms of time. It means that the shorter the transportation and waiting times, the less the amount of consumed energy will be. Limitation 1 is a minimizing function with coefficient c which minimizes vehicles' transportation time and waiting time in supply chain centers; limitation 2 specifies the total size of vehicles in the chain; limitations 3-5 have specified transfer volume between the centers; limitations 6-9 show that multiplying travel numbers with vehicles' numbers in the path of two centers is greater than transfer volume fraction in proportion with each vehicle's capacity; limitations show the arrival rate of vehicles to each center; limitations show servers' occupation percentages; limitations show servers' nonoccupation percentages in the zero situation; limitations show each server's mean queue length; limitations show each server's mean waiting time; limitations show each vehicle's waiting time in each center. Min C= (1) s.t. + + = TNV + (2) ISBN:

7 (17) (3) (18) (4) (5) (19) (20) (6) (7) (8) (9) (21) (22) (10) (23) (11) (24) (12) (25) (13) (26) (14) (27) (15) (16) ISBN:

8 (36) (33) (37) (28) (34) (40) (29) (41) (42) (30) (43) (31) (44) (32) (45) (46) (47) (35) (48) (38) (49) (39) (50) (51) NT, NV: Integer (52) ISBN:

9 The queue length and waiting time have been obtained according to Table 1. The amount of pollution production due to serving in each center's queue (including loading and unloading) and vehicles' displacement between the centers have been presented in Tables 1 and 2. As has been shown in Table 1, the highest pollution production has been occurred in distribution center 2 due to its less distance from other centers and also this center's service rate and fewer number of parallel servers; this center's service rate can be increased by increasing the number of servers or employing better technologies for loading and unloading vehicles; as a result, the amount of the produced pollutants can be reduced. Table 2. The Amount of Produced Pollutants in Queues of Each Center Regarding the Queue Length Table 1. The Amount of Produced Pollutants by the Transportation Vehicles Regarding the Distance between the Two Centers from each Other and the Required Time The Amount Vehicles Vehicles of NV NT Destination Source Produced Pollutant * * * * * * * * subsequently, the amount of harmful pollution production can be decreased by increasing the number of vehicles. It must be noted that correctly selecting the location of the centers and the distance between them is effective and must be precisely considered at the beginning of constructing such centers. Table 1 shows the number of available vehicles in each route and the number of the required commutes for the products complete transportation. Besides, the amount of pollutants produced by vehicles in terms of the distance between the two centers and the required time has also been determined. As has been shown, the number of transportations in this path and 4 CONCLUSION AND RECOMMENDATIONS In order to take advantage from the green supply chain competitive advantage, organizations have to regard environmental considerations in their supply chains. One of these considerations is precision in optimizing energy consumption regarding limited and non- renewable resources. Using ISBN:

10 queuing theory in green supply chain management activities provides organizations with the opportunity to improve activities such as purchasing the raw materials, production, inbound logistics, outbound logistics, and reverse logistics to reduce energy waste. Prioritizing activities related with each green supply chain management subset results in the optimization of transportation time and waiting time in the process, and therefore, the reduction of energy consumption waste. Regarding Tables 1 and 2, all the mentioned parameters in this research such as correct location of the centers, the number of the available vehicles in the paths between the centers, and also the centers' service rate to vehicles, the number of travels between centers, and even the number of centers and the available servers in centers, the service technology, etc. can affect the reduction of pollution production, while each of these parameters can affect other parameters. Consequently, the consideration of each parameter alone and in relation with others in reducing pollution production in transportation and vehicles routing in a green supply chain can be the subject of future researches. References: [1] Adan, I, Resing, J.,2002. Queueing Theory.Department of Mathematics and Computing Science, Eindhoven University of Technology. [2] Amemba, C.S., Nyaboke, P.G., Osoro, A. & Mburu, N., Elements of Green Supply Chain Management. European Journal of Business and Management, Vol. 5, No. 12, pp [3] Boks, C., Stevels, A., Essential Perspectives for Design for Environment. Experiences from the Electronics Industry, International Journal of Production Research, Vol. 45, No , pp [4] Eltayeb, T.K., Zailani, S. & Ramayah, T., Green Supply Chain Initiatives among Certified Companies in Malaysia and Environmental Sustainability: Investigating the Outcomes. Resources, Conservation and Recycling, Vol. 55, Issue. 5, pp [5] Fortes, J., Green Supply Chain Management: A LiteratureReview. Otago Management Graduate Review, Vol. 9, pp [6] Jabbour, A.B.L.S., Jabbour, C.J.C., Latan, H., Teixeira, A.A. & Oliveira, J.H.C., Quality Management, Environmental Management Maturity, Green Supply Chain Practices and Green Performance of Brazilian Companies with ISO Certification: Direct and Indirect Effects. Transportation Research Part E: Logistics and Transportation Review, Vol 67, pp [7] Kendall, M., The Analysis of Economic Time Series - Part 1: Prices, Journal of the Royal Statistical Society, Vol. 96, pp [8] Kumar, R., Chandrakar, R., Overview of Green Supply Chain Management: Operation and Environmental Impact at Different Stages of Supply Chain. International Journal of Engineering and Advanced Technology, Vol. 1, Issue. 3. [9] Min, H., Ko, H. J., & Ko, C. S., A Genetic Algorithm Approach to Developing the Multi-echelon Reverse Logistics Network for Product Returns. Omega, Vol. 34, pp [10] Mutingi, M., The Impact of Reverse Logistics in Green Supply Chain Management: A System Dynamics Analysis. International Journal of Industrial and Systems Engineering, Vol. 17, No. 2/2014, pp [11] Nagorney, A., Toyasaki, F., Reverse Supply Chain Management and Electronic Waste Recycling: A Multi-tiered Network Equilibrium Framework for E-cycling. Transportation Research Part E: Logistics and Transportation Review, Vol. 41, pp ISBN:

11 [12] Rao, P., Holt, D., "Do Green Supply Chains lead to Competitiveness and Economic Performance?". International Journal of Operations & Production Management, Vol. 25, No. 9, pp [13] Roy, R., Whelan, R. C., Successful Recycling through Value-Chain Collaboration. Long Range Planning, Vol. 25, pp [14] Sundarakani, B., De Souza, R., Goh, M., Wagner, S. M., & Manikandan, S., Modeling Carbon Footprints across the Supply Chain". International Journal of Production Economics, Vol. 128, No. 1, pp Sarkis, J., "Greening the Supply Chain". Springer link-verlag Londen limited. [15] Sarkis, J., Cordeiro, J., An Empirical Evaluation of Environmental Efficiencies and Firm Performance: Pollution Prevention Versus End-of Pipe Practice. European Journal of Operational Research, Vol. 135, pp [16] Shih, L., Reverse Logistics System Planning for Recycling Electrical Appliances and Computers in Taiwan. Resources, Conservation, and Recycling, Vol. 32, pp [17] Srivastava, S.A.K., Green Supply- Chain Management: A State-of-The-Art Literature Review. International Journal of Management Reviews, Vol. 9, No. 1, pp [18] Srivastava, S. K., Srivastava, R. K., Managing Product Returns for Reverse Logistics. International Journal of Physical Distribution and Logistics Management, Vol. 36, pp [19] Stalling, W., Queuing Analysis.Derived from: WilliamStallings.com/ StudentSupport.html. [20] Tibben-Lembke, R. S., Life after Death: Reverse Logistics and the Product Life Cycle. International Journal of Physical Distribution &Logistics Management, Vol. 32, pp [21] White, C.D., Masanet, E., Rosen, C. & Beckman, S., Product Recovery with Some Byte: An Overview of Management Challenges and Environmental Consequences in Reverse Manufacturing for the Computer Industry. Journal of Cleaner Production, Vol. 11, No. 4, pp ISBN: