Capacity sharing among truck owners: A collaborative approach to overcome overloading

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1 Capacity sharing among truck owners: A coaborative approach to overcome overoading Arindam Debroy 1 Research Schoar debroyarindam1@gmai.com S. P. Sarmah 1 Professor 1 Department of Industria and Systems Engineering Indian Institute of Technoogy Kharagpur India Abstract Capacity shortage is a serious probem for the Indian trucking industry for which overoading is foowed as a soution by the carriers. However this practice is egay banned as it gives rise to many other probems. A coaborative approach has been proposed to overcome capacity shortage probem without overoading. Keywords: Capacity echange price Overoading Coaboration. INTRODUCTION A strong ogistic sector serves as the backbone for the overa economic deveopment of any country. In a vast country ike India it is vita to have an efficient ogistic system which enabes connecting the remote parts of the country. India s economy is growing at a brisk pace. It has been predicted that in coming years there wi be a huge boom in both manufacturing as we as retai sector in the country as shown by Deoitte survey report [1]. To add to that India has a very arge agricutura sector with most of the agricutura and being ocated far away from the main market and consumers. For proper distribution of goods manufactured or produced the country shoud have a highy efficient ogistic system. In India freight movement is carried out through a three means of transport namey road water and air. However roads are the most preferred means of freight movement as they are much cheaper as compared to the other means of transport. The roadways carry a higher percentage of freight as compared to rai as every part of the country is connected by roads. There are over 40akhs trucks and orries as on 31st March 2012 operating across the country [2]. This number is graduay increasing at an enormous rate. Though the trucking industry is arge it is totay fragmented and unorganized. About 75% of the trucking sector is owned by those peope whom we can ca micro-truck owners [3] having ess than five trucks. With such ess number of trucks a very sma amount of capacity is avaiabe with them. Due to this reason these truck owners cannot take arge orders. In case a truck owner gets a arge order he has to resort to overoading in order to carry out the order. Overoading decrease the ife of the truck and increases the maintenance cost of the truck. 1

2 RELATED WORK Coaborative ogistics (CL) has become an important part of suppy chain management studies. CL can be broady cassified into two casses namey horizonta and vertica coaborative ogistics. Vertica coaborative ogistics traditionay refers to inkage between the members at different eve of the same suppy chain in order to improve the performance of the suppy chain. Horizonta coaborative ogistics on the other hand has the coaborating partners working at the same eve of suppy chain performing amost simiar type of functions as shown by Crussen et. a. [4].The concept of horizonta coaborative ogistics has been used in maritime and aviation industry which is we documented. However Leitner et.a. showed in their paper that it has not been used in road transport industry to that etent[5]. Horizonta coaboration in ogistics can be achieved through sharing of orders sharing of information and sharing of resources. Crussen and Saomon [6] have shown that by sharing of information about their individua orders can aow the transport companies to reduce up to 15% of their tota transportation costs and sometimes even more. Literature eists which shows the use of this concept in Mari-time and aviation industry. Aggarwa and Ergun [7] deveoped a mied integer programming mode for the carrier coaboration in ship iners. The authors deveoped a muti commodity fow probem using the concept of network fow. A simiar mode was deveoped by Houghtaen et.a. [8] and appied for carrier coaboration in airines. They formuated the probem as muti commodity fow probem. The utiization of unused spaces for ess-than-truck oad through carrier coaboration has been taken up by Zhou et. a. [9]. They considered two coaborative modes namey strategic aiance and fu coaboration and tried to identify the factors which affect the profit and thereby the coaborative decisions. PROBLEM BACKGROUND The high fragmentation of the trucking industry resuts in various probems for the truck owners. The micro-truck owners have to compete for getting orders with other micro-truck owners. With ess number of trucks capacity shortage becomes a major probem for these truck owners. These truck owners are dependent on transporters (brokers) to get orders which aso don t have any certainty of arriva. These probems cause the truck owners to accept orders more than their capacity and in order to fufi the order they have to resort to overoading. MATHEMATICAL MODEL In this study a coaborative practice of capacity sharing among carriers has been proposed which woud not ony aow them to accept orders arger than their capacity but aso fufi the order without overoading. The concept of capacity echange price is being used in trucking industry for the first time in this paper. Two mied integer inear programming mathematica modes using network theory have been formuated. The first mode depicts the present scenario in the Indian trucking industry considering overoading and the second mode has been formuated using the concept of capacity sharing and coaboration. A comparative study has been carried out between the two practices on the basis of profit earned. The deveopment of the mathematica modes is based on the foowing notations and assumptions: 2

3 Notation: Sets and indices: E- Set of a edges in the network Eo- Set of overoaded edge m L- Set of carriers d D- Set of demand t T- Set of Trucks Ke - Set of capacity of each edge i Origin node in the fow j- Destination node in the fow γ- Fraction of capacity of each node aocated to each carrier R- Revenue per unit fow in each edge Ro- Revenue per unit overoaded fow in each edge Ce- Capacity echange price Cap- Capacity of each truck considered to be 9 Tons. d - Demand of carrier from node i to node j Decision variabes: E - Fow from ith node to jth node for carrier (i j) E L / o E - Ecess fow from ith node to jthnode for carrier whie considering capacity sharing () o L o o E - Ecess overoaded fow from ith node to jthnode for carrier ()o L / m o E - Ecess fow from ith node to jthnode for carrier m whie considering capacity sharing () o m L X - Binary number when carrier has demand more than the capacity of the edge / X - Binary number when carrier has demand ess than the capacity of the edge. Assumptions: A the carriers have same no. of trucks 5 trucks. A the trucks return on the same day to the carrier s depot. There is fu information sharing among a the carriers. Demand is deterministic. Objective function: Mode 1: Considering overoading Maimize Profit= * R * R )* X Fue Cost (1) o o t t t T ( i j) t T o ( i j) 3

4 Constraints t T ( i j) t t - t T ( j i) Cap jit 0 L( i j) ( i j)( j i) (2) (3) t t T ( i j) + t T o ( i j) o t Ke L( i j) E o ( i j) E * X d o L( i j) d ( ) D i j o t t t T ( i j) t T o ( i j) (4) (5) Equation (1) gives the objective function in which profit has been maimized considering the practice of overoading. The fow baance across nodes has been taken care of by equation (2). The fow baance equation has been considered ony when carriers have to pass through the depot of other carriers eg. when carrier A has to move through the depot of carrier B or C. The constraint guarantees that no materia wi be unoaded or added whie passing through the depot of other carriers. Equation (3) represents the capacity constrain of fow through a truck. It guarantees the maimum fow through one particuar truck cannot eceed the maimum capacity of the truck. Constraint on capacity of an edge has been shown using equation (4). The capacity of the edge is divided among a the carriers. The tota fow for a particuar carrier cannot eceed the fraction of edge capacity aocated to him. This constraint has been used to put a imit to the amount of overoaded quantity that can be carried by a particuar carrier. Equation (5) ensures that the fow through an edge is never higher than the demand of the corresponding node for a particuar carrier. Mode 2: Considering Capacity sharing Maimize / / m / Profit= * R *( R C )* X * C * X Fue cos t Constraints t T ( i j) t Cap t T ( i j) (6) t t e t e t T ( i j) t T o o ( i j) t T ( i j) t t + - t T ( j i) jit L( i j) 0 ( i j)( j i) * X * X K L( i j) E o ( i j) E / / m / t t e t T o o ( i j) t T ( i j) / t ( t )* X d t T ( i j) t T o ( i j) X X / 1 L( i j) E o ( i j) E (11) Equation (6) gives the objective function which maimizes profit considering capacity echange. Constraint shown in equation (7) considers baance of fow through nodes. The fow baance equation has been considered ony when carriers have to pass through the depot of other carriers eg. when carrier A has to move through the depot of carrier B or C. The constraint guarantees that no materia wi be unoaded or added whie passing through the depot of other 4 (7) (8) (9) (10)

5 330 carriers. Equation (8) represents the capacity constraint of fow through a truck. The fow through a truck or the amount carried by a truck cannot eceed the maimum capacity of that truck. Constraint on capacity of an edge has been shown using equation (9). The tota capacity of an edge has been divided among the truck owners depending upon the no. of trucks that they have. The tota fow through any edge for a particuar carrier must be ess than the fraction of edge capacity aocated to him. The demand constraint has been shown by equation (10). The tota fow through any edge for a particuar carrier shoud not eceed the demand of the corresponding node for that carrier. Equation (11) represents the binary number constraint. ILLUSTRATIVE EXAMPLE The mathematica mode deveoped in the previous section has been soved using an iustrative eampe. Figure 2 shows the network of the iustrative eampe. The data set for the probem was randomy generated. Three carriers have been considered and their demand for one week has been shown in Tabe1. In the eampe five working days have been considered in one week. The body weight of a 9 tons truck is 4 tons and its oad carrying capacity is 5 tons. Eight destination nodes were considered for each carrier. Tabe2 shows the distances of each node from the carrier s depots. The capacity of fue tank of a 9 ton truck is appro. 200ts. It has been considered in the eampe that each carrier has customer base in every node. The demands from the eight different nodes for each carrier have been shown in Tabe A B C ABC are the carriers depot. Node 1-9 are the nodes where these carriers carry out their operations at. 7 8 Figure 1-Network of operation for a the carriers 5

6 Tabe 1-Distance of the nodes from the depots of a the carriers Carriers depots Distance of the nodes from the depots of a the carriers Node 1 Node 2 Node 3 Node 4 Node 5 Node 6 Node 7 Node 8 Bhagwati Transporters Hare Rama Hare Krishna Carriers Kharagpur Transporters ys Nodes Demand Of Bhagwati Transporters (Tons) Tabe 2-Demand of a the carriers for one week Demand Of HRHK Carriers (Tons) Demand of Kharagpur Transporters (Tons) y1 y2 y3 y4 y5 y1 y2 y 3 y4 y5 y1 y2 y3 y4 y 5 Node Node Node Node Node Node Node Node The mieage of a 9 ton truck which has been used for 3 years moving at an average speed of kmp has been shown in the Tabe 3. Tabe shows the change in mieage of a truck with increase in oad. It can be seen there is a drastic fa in the mieage when we overoad. Tabe 3-Mieage breakup with oad Loads(tons) Mieage(kmp) The fue cost for a the carriers were cacuated and have been shown in Tabe 4 and 5 respectivey. ys Nodes Tabe 4-Fue cost of carriers 1 2 & 3for one week with overoading Fue Cost of Bhagwati Transporters Fue Cost of HRHK with Fue Cost Kharagpur with overoading (Rs) overoading (Rs) Transporters with overoading (Rs) y y y y y y y y y y y y y y y Node Node

7 Node N ode Node Node Node Node Tabes 6 and 7 show the fue cost for each carrier after capacity sharing. It can be seen that there is a decrease in the tota fue cost after capacity sharing. Tabe 6-The fue cost of carriers 1 2 & 3 for one week with capacity sharing. Fue Cost of BhagwatiTransporterswith Capacity sharing (Rs) Fue Cost of sharing (Rs) HRHK withcapacity Fue Cost of Kharagpur Transporters Capacity sharing (Rs) ys Nodes y1 y2 y3 y4 y5 y1 y2 y3 y4 y5 y1 y2 y3 y4 y5 Node Node Node N ode Node Node Node Node RESULTS AND DISCUSSION The comparative resuts of the profit earned by each carrier over a period of one week with overoading and with capacity sharing after soving the probem have been shown in Tabe 8. Tabe aso shows how the profit varies for different vaues of capacity echange price. Capacity echange price has been considered as a fraction of revenue charged by the carrier. Varying the fraction different resuts have been obtained and shown in the Tabe. From the Tabe it can be seen that there is an increase in profit with capacity sharing as compared to overoading. Figure 2 shows the variation of profit for various carriers with different vaues of capacity echange price. Tabe 8-Comparison of profit earned with overoading and capacity sharing Carriers Profit with Profit with capacity sharing at different vaues of Capacity echange price (Rs) overoadi 0.1*R 0.2*R 0.3*R 0.4*R 0.5*R 0.6*R 0.7*R ng (Rs) Bhagwati Transporters Hare Rama Hare Krishna Carriers 7

8 Kharagpur Transporters Figure 2-Variation in profit for various carriers with different vaues of capacity echange price CONCLUSION AND FUTURE SCOPE In this paper a new approach of coaboration through sharing of oad has been proposed in order to enabe micro truck owners overcome the probem of overoading and capacity shortage. The concept of capacity echange price has been used in trucking industry for the first time and it has been shown that there is an increase in profit if the carriers coaborate among themseves and share their unused capacity. Random data have been considered whie soving the mode. The future research work can be done using rea time data and soving the mode in rea time scenario. The vaues of profit corresponding to various vaues of capacity echange price changes differenty for different carriers. A game theory based approach can be used to find out a particuar vaue of capacity echange price in which a the carriers wi have optimum profit as a future work. It can be seen from Tabe 8 the vaue of profit for carrier 2 and 3 increases with increase in capacity echange price however for carrier 1 there is a decrease in profit. The reason behind difference in changes in profit for different carriers can be worked on in the future. Reference Deoitte 2012 January Logistics sector: Present situation and way forward. Road Transport Year Book ( ) Transport research wing Ministry of road transport & highways Government of India New Dehi. IRADe. (2013). The Impacts of India s Diese Price Reforms on the Trucking Industry Integrated Research and Action for Deveopment New Dehi. Retrieved from: ffs_india_irade_trucking.pdf Crussen F. Coos M. &Duaert W. (2007) Horizonta cooperation in ogistics: Opportunities and impediments. Transportation Research Part E: Logistics and Transportation Review LeitnerR. MeiznerF. ProchazkaM. &SihnW. (2011) Structura concepts for horizonta cooperation to increase efficiency in ogistics CIRP Journa of Manufacturing Science and Technoogy

9 Crussen F. & Saomon M. (2004). Empirica study: Order sharing between transportation companies may resut in cost reductions between 5 to 15 percent (Discussion paper No. 80). Tiburg: Center Research Institute Tiburg University. Aggarwa R. Ergun O. (2010) Network design and aocation Mechanisms for carrier aiances in iner shipping Operations research 58(6) HoughtaenL. ErgunO. SokoJ. (2011) Designing Mechanisms for the Management of Carrier Aiances Transportation Science 45(4) Zhou G. HuiY. Liang L. (2011) Strategic aiance in freight consoidation Transportation research Part E

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