Metropolitan Cities s Waste Transportation Model

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1 Available olie at Procedia - Social ad Behavioral Scieces 65 ( 0 ) Iteratioal Cogress o Iterdiscipliary Busiess ad Social Sciece 0 (ICIBSoS 0) Metropolita Cities s Waste Trasportatio Model Asrul. H. Ismail, Yulita V. Usma, Nur Y. Hidayah, Laela Chairai * Idustrial Egieerig Departmet, Pacasila Uiversity, Sregseg Sawah, Jaarta 640, Idoesia Abstract Metropolita cities have the may problem to maage their waste, particularly i trasportatio maagemet. Oe of critical factors to be cosidered at the time of trasportig waste is traffic jam. It meas usig more resources ad high cost. Through system egieerig process approach as research methodology, the optimizatio model is developed usig liear programmig method to assig specific type of fleet i the right time ad destiatio based o the best route to meet maximum savig of cost where traffic jam factor is iserted as a idepedet variable. 0 The Published Authors. by Published Elsevier by Ltd. Elsevier Selectio Ltd. Ope ad/or access peer-review uder CC BY-NC-ND uder resposibility licese. of JIBES Uiversity Selectio ad peer-review uder resposibility of JIBES Uiversity, Jaarta Jaarta Key words: liear programmig; optimizatio; trasportatio model; waste maagemet; travellig salesma problem. Itroductio Characteristics of trasportig waste i a urba ad rural areas have differet characters. Maily the metropolita cities, especially the chaotic metropolis lie Jaarta, have higher traffic jam rates tha rural oes. Cosiderig the traffic jam rates factor i maagig waste, particularly i the areas of trasportatio maagemet, will meet the optimal solutio. I a fleet assigmet pla of urba waste districts hygiee sectio does t ito curretly cosideratio this jam factor. I waste maagemet i Jaarta every trasportig waste from ladfill (origial) to the trasfer statios or the fial disposal site (destiatio) maaged by each district uder hygiee sectio. Each sectio has their ow fleets that are maaged idepedetly. The problem today is the hygiee sectio do ot have decisio maig model to cofigure * Correspodig author. Tel.: ext. 9; fax: address: asrul@uivpacasila.ac.id, yulita@uivpacasila.ac.id, ur.yuliati@uivpacasila.ac.id, laela@uivpacasila.ac.id The Authors. Published by Elsevier Ltd. Ope access uder CC BY-NC-ND licese. Selectio ad peer-review uder resposibility of JIBES Uiversity, Jaarta doi:0.06/j.sbspro

2 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) the assigmet of fleet that led to low productivity. This research will aswer how cofiguratio assigmet each fleet from the origial to the destiatio will meet the maximum savig of cost.. Biary Iteger Programmig Theory Numbers 0 ad, or more commoly ow as a biary, it ca be used i liear programmig. This biary umbers with such characteristics as a decisio variable will create a liear programmig model that ca be used to solve various problems. 0- variable characteristics allow us to pour ito the causality costrait fuctios. If the positive decisio value is, the the egative decisio value is 0. The simplest way to do this is to assume 0 x is a iteger variable where is a iteger limit. The with a ote that y, y,..., y are biary variables 0-. x y y... y The equatio is a exact biary represetatio of all feasible values of x. A represetatio of a more ecoomical where the umber of biary variables is usually smaller tha are ow. () x y y y... 0 y () Where is the smallest iteger that satisfies The fact that every problem ca be a biary iteger alog with the simplicity of calculatios i dealig with variable 0- (each variable has oly two values) have directed attetio to the use of these properties to develop a effective algorithm (Taha. 006, Siswato 007). Nomeclature i Origial poits = Ladfills i a village =,,..., m. j Destiatio poits = Trasfer statios or the fial disposal site =,,...,. Vehicle type. r Trip scheduled. X ij The distaces betwee the origial i ad destiatio j. Y Average vehicle Speed by vehicle type. tj Traffic jam rates (%). Z ijr - tj is levels of smooth traffic from i to j o trip r. C Costs per hour usig the vehicle type. H ijr U ijr M Biary decisio variables, it would be worth for assigmet of a vehicle type to trasport waste from the origial i towards destiatio j o the trip r. Iteger decisio variables, it would be represetig the umber of uits fleet that assiged from the origial i towards destiatio j usig type o the trip r. Huge umber.

3 048 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) J The umber of vehicles type. Cap The amout of capacities of vehicle type. U i The miimum amout of udistributed waste. 3. State of The art State of the art this research whe we compare with other researches of the waste maagemet system i metropolita city, see table. Table. State of the art Authors, Year Locatio Output Method Lisye Fitria, Susy Susaty, Suprayogi, 009 Badug, Idoesia Trasportatio route optimizatio Vehicle Routig Problem with Multiple Trips ad Itermediate Facility Daijel Marovi, Dragoslav Jao evi, Miomir Jovaovi, Vesa Nioli, 00 NI, Serbia Solid waste maagemet Aalitic Hierarchy Process, Liear Programmig, Mixed Iteger Programmig, Geetic Algorithms, Iexact Quadratic Programmig, No- Liear, Multi-Objective Iteger Programmig, Geographical Iformatio Systems (GIS) ad etwor aalysis This Research, 0 DKI Jaarta, Idoesia Model of assigmet of waste trasportatio s fleet Liear Programmig, Trasportatio Model, Travellig Salesma Problem 4. Methodology The system egieerig process approach is used i this research. After defiig the objective, documetig the requiremet from staeholder by collectig ad aalyzig them will lead to the model requiremet list eeded, the aalyzig the existig process usig Fuctioal Flow Bloc Diagram (FFBD) to desig the optimizatio model. The model requiremet list states that the model has to be cofigured the capacities, based o fleet type, ad st ad d arrival-departure schedule, ad the cost that ca be reduced (System egieerig fudametal, 00). The flow process illustrated i figure.

4 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) Fig.. The system egieerig process 5. Developig Optimizatio Model I this research a model has bee developed to solve the waste distributio problem usig the approach of a biary system with iteger liear programmig. As it has bee oted above that the mai objective of this study was to determie the lowest cost of trasportatio fleet assigmet. This assigmet icludes the type ad departure schedule based o ay of its trip. 5.. Decisio variables The values of these variables provide the solutio to the problem. This paper s aswer to this questio should be fleet type assiged to a specific trip scheduled (H ij ). 5.. The data eeded Data eeded to build the model: The amout of distaces from origial to destiatio poit. The amout of costs from trasportig usig each type of fleets. The umber of percetages of traffic jam rates. The amout of average speeds of each type of fleets.

5 050 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) The amout of capacities of each type of fleets. The demad to assig resources from ladfills to fial disposal site is assumed to deped o trasportatio fleet, the amout vehicle type, trip scheduled, traffic jam rates, the distace betwee ladfills to fial disposal site, average vehicle speed ad costs per hour usig the vehicle type. Waste trasportatio access affects to reduce the amout of household waste i each house. I this case assigmet resources will be focused o the fleet (tool carrier) used by each district to trasport waste from ladfills i each village to the best of fial disposal site. Purpose of this assigmet models is assumed to maximize trasportatio cost savigs with umber ad capacity of fleet, capacity of fial disposal per day ad capacity of ladfills per day as costrait. Variables were defied to represet distace from ladfills to fial disposal, average vehicle speed by vehicle type, levels of smooth traffic from ladfills to fial disposal site o trip scheduled, ad costs per hour usig the vehicle type. This research usig mathematical model with biary iteger programmig approach to assig fleet, based o the purpose ad costrait Objective fuctio Iitially the objective fuctio was desiged to miimize the total trasportatio cost stated below. Verbal form: Trasportatio cost is the umber of hours of trasportig waste from origial to destiatio poit usig certai vehicle type o trip schedule multiple by costs per hour. Decompositio: Trasportatio cost = [(The distaces betwee i ad j) x {(Average speed of ) x (Levels of smooth traffic i-j o r)} - ] x ( Costs per hour usig ) (3) Sice biary iteger programmig could ot miimize it, so the objective fuctio is modified as to maximize the trasportatio cost savigs. Trasportatio Cost Savig is a iitial trasportatio cost (iitial assigmet) reduced by trasportatio costs miimal (ew assigmet). Assume that iitial trasportatio costs are a huge umber represeted by M umber. Verbal form: Trasportatio cost savig is trasportatio cost before optimizig mius trasportatio cost after optimizig Decompositio: Trasportatio cost savig = Huge umber [(The distaces betwee i ad j) x {(Average speed ) x (levels of smooth traffic i-j o r)} - x (Costs per hour usig )] (4) 5.4. Mathematical model ad its costraits There is oly oe destiatio for every origial ladfill usig certai vehicle. The total umber of assigmet caot exceed the umber of owed fleets. The total amout of uloadig waste capacities caot exceed the amout of the trasfer statios or the fial disposal site capacities. The costraits are

6 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) the decisio variable have to be biary umber ad all of the variables must be o-egative umber. Mathematical formulatio for the problem i this paper shows i equatio 5-9. Objective: m o p Z Max [ M ( X. ( Y. Z ). C )]. U. H (5) i j r ij ijr ijr ijr st-: j H, i r,ad (6) ijr, st-: m i j H ijr J,, ad r (7) st-3: j o p r H. U. Cap U i (8) ijr ijr i, st-4: H ijr = biary (9) 6. Case Study Fleet schedulig of trasportatio model was developed by cosiderig the umber of fleet owed by the City Cleasig Departmet of East Jaarta as a case study. The fleet that owed by East Jaarta City Cleasig Departmet cosists of various types ad loadig capacity, such as: arm roll, dump truc, truc, etc., see table. The type ad loadig capacity of each fleet is oe of the costrai that are used to model schedulig. Aother costrai to cosider is the distace, based o the best of waste trasportatio routes, from district locatio to itegrated ladfills locatio or selected locatio for fial disposal sites, as well as assumptio of the levels of traffic jam ad operatioal costs of trasportig waste for each type. Trasportatio schedule is curretly divided ito (two) trip schedule (the maximum that ca be allocated). The schedule for each trip preseted i table 3 (City Cleasig Departmet of East Jaarta, 0). Trip schedule which has bee implemeted is also used i model developmet. Table. Type ad capacity of fleet owed by City Cleasig Departmet of East Jaarta No. Type Various type of fleet Capacity (m 3 ) Small Arm Roll 8 Small Coveyor 0 3 Large Arm Roll 4 4 Large Coveyor 0 5 Arm Roll Mobile 5

7 05 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) Table 3. Waste trasportatio schedule of East Jaarta Trip schedule Time schedule Departure schedule to itegrated ladfills (fial disposal sites) st d Table 4. Fleet schedulig of waste trasportatio i Matrama District st Trip d Trip Village Itegrated ladfills/ Itegrated ladfills/ Fleet Type Uits Fleet Type Uits Fial disposal sites Fial disposal sites Kebo Maggis Type PDUK Cacig PDUK Cacig Type PDUK Cacig Kayu Mais Type Type Uta Kayu Selata Type Type Pisaga Baru Type Type Uta Kayu Utara Trasportatio schedulig model is developed by usig data from the Matrama District. Matrama District cosists of 6 (six) villages, but it is used for modelig schedulig oly 5 (five) villages because they are directly maaged by the City Cleasig Departmet of East Jaarta, while (oe) village is maaged by private compay (excludig research scope). Matrama District has 3 (three) types of fleet, i. e.: 6 uits of type, 6 uits of type 4, ad uit of type 5. Based o the formulatio of mathematical models, equatio 5-9, obtaied fleet schedulig of Matrama District, see Table Coclusio The output of this research is optimizatio model to assig the fleet of waste trasportatios i metropolita cities. The model has bee developed ad validated i Matrama District, East of Jaarta by cosiderig the umber of fleet that owed by the City Cleasig Departmet of East Jaarta ad their trip schedules. The model s costraits are the type ad loadig capacity of each fleet, the distace from origial locatio to the trasfer statios or the fial disposal site based o the best waste trasportatio routes, the assumptio of the level of traffic jam i Jaarta, ad the operatioal costs of trasportig waste for each type of fleet. After all the fleet schedulig of waste trasportatio for each type of fleet ad its fial disposal site has bee determied at the maximum savig of cost. This model is also referred to actual coditio at the time of research or as close as it is.

8 Asrul. H. Ismail et al. / Procedia - Social ad Behavioral Scieces 65 ( 0 ) Refereces Taha, H. A. (006). Operatios Research, A Itroductio. (8th ed). New Jersey: Pretice Hall, (Chapter 9). Siswato. (007). Operatios Research. (st ed.). Jaarta: Erlagga, (pp ). Fitria, L., Susaty, S., & Suprayogi. (009). Peetua Rute Tru Pegumpula Da Pegaguta Sampah Di Badug. Jural Tei Idustri, Vol., No., pp Marovi, D., Jao evi, D., Jovaovi, M., & Nioli, V. (00). Applicatio Method For Optimizatio I Solid Waste Maagemet System I The City Of Ni. Facta Uiversitatis, Series: Mechaical Egieerig Vol. 8, No., pp (00). System Egieerig Fudametal (pp. 5-3). Fort Belvoir, Virgiia: Defese Acquisitio Uiversity Press.. (0). Waste Productio Data i East Jaarta Area. Jaarta: City Cleasig Departmet of East Jaarta.