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Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

Production Efficiency Improvement Through Preventive Maintenance and Production Scheduling

Optimization Rahmat Nurcahyo

Industrial Engineering, Faculty of Engineering Universitas Indonesia

Depok, 16424, Indonesia [email protected]

Amar Rachman Industrial Engineering, Faculty of Engineering

Universitas Indonesia Depok, 16424, Indonesia

Tommy Agustino Industrial Engineering, Faculty of Engineering

Universitas Indonesia Kampus Baru UI Depok, 16424, Indonesia

Abstract— Main purpose of optimization in industry is to increase efficiency in production. Optimization in this research is related with preventive maintenance and production scheduling. A mathematical model of the preventive maintenance and production scheduling is developed. Preventive maintenance model aims to optimize the periodic replacement of significant parts while production scheduling model aims to meet the demand for distributors. Mathematical models used for preventive maintenance is a simple optimation model, while the mathematical model used for production scheduling is a Integer Linear Programming. The result of this research shows that the optimization increases the production efficiency by 38.8%.

Keywords— Optimization, Preventive Maintenance, Production Scheduling, Integer Linear Programming

I. INTRODUCTION

According to Ministry of Industry, one industry that has negative investment in Indonesia is beverages industry that contain alcholol. It follows government regulation number 36 year 2010 about List of Negative Investment in Indonesia. It means that factories of the industry that already exist in Indonesia does not have a chance to add production capacity using capital investment. The only option they have is to optimize the current production facilities.

In general, the machinery faicilities in beverages industry include washer machine, filler machine, coding machine, labeling machine and packaging machine. As an impact of government regulation, most of the machinery facilities are old machines, some even more than 20 years old. Old machines require more intensive maintenance activities because of the potential breakdown that can occur. Preventive maintenance become a challenge in Indonesia beverage industry.

Preventive maintenance and production scheduling are related to each other due to well scheduled preventive maintenance will reduce the likelihood of machine down time that can interfere production. Both topics also have received serious attention in manufacturing industry and research.

The method that is believed to be the most relevant for this research is through optimization of both preventive maintenance and production scheduling. Selected focus of optimization in preventive maintenance is to determine effective periodic replacement scheduling of significant part. While focus of optimization in production scheduling is to determine an effective scheduling production based on distributor demand.

II. LITERATURE REVIEW

Nguyen and Bagajewicz (2008) using Monte Carlo simulation method and Genetic Algorithm to optimize preventive maintenance planning. Monte Carlo method used to evaluate the expected maintenance costs as a maintenance performance indicator. The research developed a new model of preventive maintenance by considering three things that are not taken into account in previous studies, such as the various types of damage to the equipment, equipment repaired priority, and limited human and material resources.

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Kyriakidis and Dimitrakos, (2006) using the Markov decision algorithm to optimize preventive maintenance on production installations. This literature also introduces a system buffer built in between the installation to prevent production reduced due to damage to the engine. Gosavi (2011) used two models to determine preventive maintenance budget, such as renewal theory and models of Markov Decision Processes (MDP). Renewal theory is more simple then MDP. Renewal theory is used for manufacturing components such as conveyor systems or heat treatment units, while the MDP is more suitable for larger systems such as production line consisting of many machines.

Caldeira and Guedes (2007) used Weibull Hazard method to determine the optimum frequency of preventive maintenance on plant equipment to ensure availability. The main objective of this research is to create optimization model that is easily understood by the practitioners of the field. Previous research discusses preventative maintenance policy to determine the optimal preventive maintenance interval to reduce engine maintenance costs, but there are a lot of complaints by practitioners of the field due to the difference between theory and practice. Practitioners say that the field of maintenance optimization models are difficult to understand and apply so much maintenance models are rarely applied. Almost all of these studies are abstract models that can be used and difficult to be identified among the many models are recommended.

Cassady and Kutanoglu (2005) integrate preventive maintenance planning and production scheduling for a single machine to develop a mathematical model, and optimize it by using Weighted Shortest Processing Time (WSPT). Machine used as the object of study is considered the most important machines in the production process. The results of the study will explain the advantages of combining two activities into decision-making process.

Research that incorporates preventive maintenance planning and production scheduling is very limited. Graves and Lee (2007) proposed solving the single machine scheduling problem with Total Weighted Completion Time as the objective function, but only on the scheduling of the maintenance activity planning.

Integer Linear Programming is mathematical model to maximize profits and minimize costs based on a mathematical model involving the variables of type integer is represented in a form that is a linear relationship (Kobetski, 2006). Completion of a mathematical equation usually produces output in the form of fractions, if the system is expecting output integer, then the ILP is the most appropriate algorithm as the use of rounding the result obtained is often not an optimal solution. Based on the type and amount of integernya, ILP can be divided into three categories, namely (a) Pure ILP, whereas all variables in the mathematical model in the form of an integer; (b) Mix ILP, which is only partially in the form of an integer; (c) Zero-one ILP, where the existing integer variable is binary, it can only receive the value of one or zero (Fogle, 1991).

ILP is widely used to determine the maximum and minimum conditions of a mathematical model involving many variables and constraints which are required in calculating the minimum and maximum conditions (Papadomanolakis, 2011). ILP calculation begins by determining assumptions, decision variables, the problem constraint, and the objective function is determined before the optimal solution (Ribic, 2010). Many optimization problems in the fields of scientific, engineering, and public sector applications involving both discrete decisions and non-linear dynamical systems that affect the quality of the design or the end of the plan. Integer non-linear programming combines discrete variable optimization with non-linear function. Integer non-linear programming is more difficult to resolve than the integer linear programming (Abhishek, 2010).

III. MODEL DEVELOPMENT

To make equation model of preventive maintenance, several related date was used namely broken bottles percentage data, plastic insert cost data, and overtime data. Percentage of broken bottles data was made into charts to obtain the equation of broken bottles. Figure 1 graph shows that the more frequent maintenance activity, the percentage of broken bottles will be smaller.

Figure 1. Graph of Broken Bottles .

y = 0.005x2 + 0.070x + 0.024

00.20.40.60.8

11.2

1 2 3 4 5 6 7 8



Data of plastic insert costs and overtime costs are also made into the equation. From the equation, constraint functions can be made as follows:

Yij = B1.Xij + B2.Fij . + B3 [0,0058 2 + 0,0705 . + 0,0243] . kj.Whereas: Overtime cost (B1) = {1.272.000, 856.000, 1.284.000, 1.712.000} i = {1,2,3,4} j = {1,2,3,4,5,6,7,8} Xij = {1,2,3,4,5,6,7,8} Plastic insert cost (B2) = 1.772.400 Coeficient of plastic insert cost decreasing (fij) = {1; 1,5; 2; 2,4; 2,8; 3,2; 3,6; 4} x = {40,32,48,64} B3 = Cost of broken bottles kj = {1; 0,9; 0,85; 0,8; 0,75; 0,7; 0,7; 0,65}

fij and kj based on discussion result with the maintenance team in the field. Fij which shows that the more frequent preventive maintenance, the plastic insert that replaced will be more, but not doubled. The more frequent we maintenance, the plastic insert replacement will be less.

kj might indicate that more frequent we maintenance the machine, the percentage of broken bottles will be smaller than before. There are 4 different types of preventive maintenance as follows:

Type 1 : If preventive maintenance performed by 4 persons with 10 hours Y1j = 1.272.000 X1j + 1.772.400 F1j . + . . . . X1j [0,0058 2 + 0,0705 . + 0,0243] . kj .

Type 2 : If preventive maintenance performed by 4 persons with 8 hours Y2j = 856.000 X2j + 1.772.400 F2j . + . . . . X2j [0,0058 2 + 0,0705 . + 0,0243] . kj .

Type 3 : If preventive maintenance performed by 6 persons with 8 hours work Y3j = 1.284.000 X3j + 1.772.400 F3j . + . . . . X3j [0,0058 2 + 0,0705 . + 0,0243] . kj .

Type 4 : If preventive maintenance performed by 8 persons with 8 hours work Y4j = 1.712.000 X4j + 1.772.400 F4j . + . . . . X4j [0,0058 2 + 0,0705 . + 0,0243] . kj .

The objective function : Min Z = Y

To make equation model of production scheduling, the data needed are monthly production data, distributor demand for a month, and the cost of products storage. The mathematical model as follows:

Constraint functions: Iij+1 = Iij + 100.000 Xij + 12.500 Yij + 12.500 Zij – Dij ≥ 100.000

Yij ≤ Xij Zij ≤ Yij∑ ij = 1 Whereas: Iij+1 = Supplies on the next day Iij = Incoming supplies on the j day Xij = Production not overtime Yij = Production used overtime on the first hour Dij = Distributor demand Zij = Production used overtime on the second hour 100.000 = Total production/day 12.500 = Total production/hour



Objective function: Min Z = ∑ 30 . I1j + 32 . I2j + 38 I3j + 40 . I4j) Whereas: I1j = Product 1 I2j = Product 2 I3j = Product 3 I4j = Product 4

Cost of product storage = {30,32,38,40} The method used to solve the scheduling model of production is a method of integer linear programming.

IV. RESULTS The results of model calculations show that the most optimal preventive maintenance periodic is Y42 (Rp 26,535,235) that

means preventive maintenance is done by 8 people with 8 hours work time once a month. When compared with the older periodic preventive maintenance (Rp. 43,343,300), the efficiency obtained is 38.8%.

Table 1. One Month Production Scheduling

Date Product 1 Product 2 Product 3 Product 4 1 07.00-16.00 - - -

2 - 07.00-18.00 - -

3 07.00-18.00 - - -

4 - - 07.00-16.00 -

5 07.00-18.00 - - -

6 - 07.00-17.00 - -

7 - 07.00-16.00 - -

8 - - - 07.00-16.00

9 07.00-18.00 - - -

10 - - - 07.00-16.00

11 - 07.00-18.00 - -

12 07.00-18.00 - - -

13 - - 07.00-16.00 -

14 - 07.00-18.00 - -

15 07.00-18.00 - - -

16 - 07.00-18.00 - -

17 - - 07.00-16.00 -

18 07.00-18.00 - - - 19 - - - 07.00-16.00 20 07.00-18.00 - - - 21 - 07.00-16.00 - - 22 - - 07.00-16.00 -

The main purpose of the production scheduling model is to find the most effective production schedules. The table

shows that factory can only produce one type of product every day. Production schedule in the table is optimal because it does not use more than 2 hours over time every day.

V. CONCLUSION The results of data processing by using a simple optimization calculations indicate that the most optimal of preventive

maintenance planning model for washer machine if performed by 8 people with 8 hours of work. Maintenance is done once a month. The efficiency achieved is 38.8%. Production scheduling model provides the optimal production scheduling solutions in the beverages factory production that has operating hours from Monday to Friday because of the limited production quotas from the government. The production scheduling models may ensure production schedules are not using more than 2 hours over time every day.



REFERENCES Abhishek, Leyffer and Linderoth. 2010. An outer-approximation based solver for nonlinear mixed integer programs. Informs

Journal on Computing, 22:555–567. Ballou. 2004. Business Logistics/Supply Chain Management” Fifth Edition. Ohio: Prentice Hall. Cassady Richard, Kutanoglu Erhan. 2005. Integrating Preventive Mainenance Planning and Production Scheduling for a

Single Machine., IEEE Transactions on Reliability, Vol. 54, No.2. Chang & Waight. 1999. A Mixed Integer Linear Programming Based Hydro Unit Commitment. In Power Engineering

Society Summer Meeting, IEEE, pp. 924-928. Fogle & Lovett Jr. 1991. An Improved Exploratory Search Technique for Pure Integer Linear Programming Problems.

Systems, Man, and Cybernetics,. “Decision Aiding for Complex Systems” IEEE International Conference vol 1, pp. 573-577.

Kadayifm M. Kandemir, & Sezer. 2002. An Integer Linear Programming Based Approach for Parallelizing Applications in On-Chip Multiprocessors. 39th Design Automation Conference, pp. 703-708.

Kobetski & Fabian. 2006. Scheduling of Discrete Event Systems Using Mixed Integer Linear Programming. Discrete Event Systems, 2006 8th International Workshop pp.76-81.

Papadomanolakis & Ailamaki. 2007. An Integer Linear Programming Approach to Database Design. Data Engineering Workshop, IEEE 23th International Conference, pp. 442-449.

Ribic & Konjicija. 2010. A Two Phase Integer Linear Programming Approach to Solving the School Timetable Problem. Information Technology Interfaces 32nd International Conference, pp. 651-656.

Taylor. 2008. Economics Tradeoffs of Substituting Transportation for Inventory in the Department of Defense”.

BIOGRAPHY

Rahmat Nurcahyo is an senior lecturer in Industrial Engineering Department, Faculty of Engineering Universitas Indonesia.He holds a Bachelor of Engineering degree in Mechanical Engineering from Universitas Indonesia, a Master of Engineering Science degree in Industrial Management from University of New South Wales Austrial and Doctoral degree in Strategic Management from Universitas Indonesua. His research interest in total quality management, production system, lean system and maintenane management. He served as faculty advisor of IEOM student chapter Universitas Indonesia.


production efficiency improvement through preventive ...ieomsociety.org/ieom_2016/pdfs/273.pdf ·...

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