Skip to main content

Advertisement

SpringerLink
Log in

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Aliev RA, Fazlollahi B, Guirimov BG, Aliev RR (2007) Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Inf Sci 177:4241–4255

    Article  MATH  Google Scholar 

  • Aytug H, Khouja M, Vergara FE (2003) Use of genetic algorithms to solve production and operations management: a review. Int J Prod Res 41:3955–4009

    Article  Google Scholar 

  • Boese KD, Kahng AB (1994) Best-so-far vs. where-you-are: implications for optimal finite-time annealing. Syst Control Lett 22:71–78

    Article  MathSciNet  MATH  Google Scholar 

  • Boudia M, Louly MAO, Prins C (2008) Fast heuristics for a combined production planning and vehicle routing problem. Prod Plan Control 19:85–96

    Article  Google Scholar 

  • Chan FTS, Chung SH, Wadhwa S (2005) A hybrid genetic algorithm for production and distribution. Omega 33:345–355

    Article  Google Scholar 

  • Chang YC, Lee CY (2004) Machine scheduling with job delivery coordination. Euro J Operat Res 158:470–487

    Article  MathSciNet  MATH  Google Scholar 

  • Chen ZL (2010) Integrated production and outbound distribution scheduling: review and extensions. Operat Res 58:130–148

    Article  MATH  Google Scholar 

  • Chen ZL, Vairaktarakis GL (2005) Integrated scheduling of production and distribution operations. Manage Sci 51:614–628

    Article  MATH  Google Scholar 

  • Chitra C, Subbaraj P (2012) A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks. Expert Syst Appl 39:1518–1525

    Article  Google Scholar 

  • Cohen MA, Lee HL (1988) Strategic analysis of integrated production-distribution systems: models and methods. Operat Res 36:216–228

    Article  Google Scholar 

  • Cohen MA, Moon S (1991) An integrated plant loading model with economies of scale and scope. Euro J Operat Res 50:266–279

    Article  MATH  Google Scholar 

  • Dantzig GB, Fulkerson DR, Johnson SM (1959) On a linear programming, combinatorial approach to the travelling salesman problem. Operat Res 7:58–66

    Article  MathSciNet  Google Scholar 

  • Dhaenens-Flipo C, Finke G (2001) An integrated model for an industrial production-distribution problem. IIE Trans 33:705–715

    Google Scholar 

  • Farahania RZ, Elahipanah M (2008) A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. Int J Prod Econ 111:229–243

    Article  Google Scholar 

  • Gen M, Cheng R (2000) Genetic algorithms and engineering optimizations. Wiley, New York

    Google Scholar 

  • Gen M, Syarif A (2005) Hybrid genetic algorithm for multi-time period production/distribution planning. Comput Ind Eng 48:799–809

    Article  Google Scholar 

  • Goldberg DE (2002) Genetic algorithms in search, optimization and machine learning. Pearson Education, Singapore

    Google Scholar 

  • Han XH, Chang XM (2011) Genetic algorithm assisted wavelet noise reduction scheme for chaotic signals. J Optim Theory Appl 151:646–653

    Article  MathSciNet  MATH  Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  • ILOG SA (2002) ILOG Ltd. ILOG OPL studio 3.6 User’s Manual, France

  • Jiao JR, Zhang LL, Wang Y (2007) A heuristic genetic algorithm for product portfolio planning. Comput Operat Res 34:1777–1799

    Article  MATH  Google Scholar 

  • Kara I, Bektas T (2006) Integer programming formulations of multiple salesman problems and its variances. Europ J Operat Res 174:1449–1458

    Article  MathSciNet  MATH  Google Scholar 

  • Karthikeyan P, Baskar S (2014) Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks. Soft Computing, online first

  • Kratica J, Milanović M, Stanimirović Z, Tošić D (2011) An evolutionary-based approach for solving a capacitated hub location problem. Appl Soft Comput 11:1858–1866

    Article  Google Scholar 

  • Kuno T, Utsunomiya T (1997) A pseudo-polynomial primal-dual algorithm for globally solving a production-transportation problem. J Glob Optim 11:163–180

    Article  MathSciNet  MATH  Google Scholar 

  • Lee AHI, Kang HY, Lai CM (2013) Solving lot-sizing problem with quantity discount and transportation cost. Int J Syst Sci 44:760–774

    Article  MathSciNet  MATH  Google Scholar 

  • Lee YH, Kim SH, Moon C (2002) Production-distribution planning in supply chain using a hybrid approach. Prod Plan Control 13:35–46

    Article  Google Scholar 

  • Lu H, Liu J, Niu R, Zhu Z (2013) Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem. Soft Computing, online first

  • Maiti AK, Bhunia AK, Maiti M (2006) An application of real-coded genetic algorithm (RCGA) for mixed integer non-linear programming in two-storage multi-item inventory model with discount policy. Appl Math Comput 183:903–915

    Article  MathSciNet  MATH  Google Scholar 

  • Megala N, Jawahar N (2006) Genetic algorithm and Hopfield neural network for a dynamic lot sizing problem. Int J Adv Manuf Technol 27:1178–1191

    Article  Google Scholar 

  • Melo RA, Wolsey LA (2010) Optimizing production and transportation in a commit-to-delivery business mode. Europ J Operat Res 203:614–618

    Article  MATH  Google Scholar 

  • Michalewicz Z (1996) Genetic algorithm + data structures = evolution programs. Springer, New York

    Book  MATH  Google Scholar 

  • Moin NH, Salhi S, Aziz NAB (2011) An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. Int J Prod Econ 133:334–343

  • Phadke M (1989) Quality engineering using robust design. Englewood Cliffs, Prentice Hall, NJ

  • Poon PW, Carter JN (1995) Genetic algorithm crossover operators for ordering applications. Comput Operat Res 22:135–147

    Article  MATH  Google Scholar 

  • Rostamian Delavar M, Hajiaghaei-Keshteli M, Molla-Alizadeh-Zavardehi S (2010) Genetic algorithms for coordinated scheduling of production and air transportation. Expert Syst Appl 37:8255–8266

    Article  Google Scholar 

  • Sakawa M, Nishizaki I, Uemura Y (2001) Fuzzy programming and profit and cost allocation for a production and transportation problem. Europ J Operat Res 131:1–15

    Article  MathSciNet  MATH  Google Scholar 

  • Singh A, Baghel AS (2009) A new grouping genetic algorithm approach to the multiple travelingsalesperson problem. Soft Comput 13:95–101

    Article  Google Scholar 

  • Steinrücke M (2011) An approach to integrate production-transportation planning and scheduling in an aluminum supply network. Int J Prod Res 49:6559–6583

    Article  Google Scholar 

  • Stern H, Chassidim Y, Zofi M (2006) Multiagent visual area coverage using a new genetic algorithm selection scheme. Europ J Operat Res 175:1890–1907

    Article  MATH  Google Scholar 

  • Tai YT, Pearn WL, Lee JH (2012) Cycle time estimation for semiconductor final testing processes with Weibull-distributed waiting time. Int J Prod Res 50:581–592

    Article  Google Scholar 

  • Taguchi G, Wu Y-I (1979) Introduction to off-line quality control. Central Japan Quality Control Association, Meieki Nakamura-Ku Magaya, Japan

    Google Scholar 

  • Taleizadeh AA, Niaki STA, Barzinpour F (2011) Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: a harmony search algorithm. Appl Math Comput 217:9234–9253

    Article  MathSciNet  MATH  Google Scholar 

  • The MathWorks, Inc. (2007) MATLAB User’s Manual, version 4.7. Massachusetts

  • Tsai HK, Yang JM, Tsai YF, Kao CY (2004) Some issues of designing genetic algorithms for traveling salesman problems. Soft Comput 8:689–697

    Article  MATH  Google Scholar 

  • Ullrich CA (2013) Integrated machine scheduling and vehicle routing with time windows. Euro J Operat Res 227:152–165

    Article  MathSciNet  MATH  Google Scholar 

  • Van Buer MG, Woodruff DL, Olson RT (1999) Solving the medium newspaper production/distribution problem. Euro J Operat Res 115:237–253

    Article  MATH  Google Scholar 

  • Wang G, Cheng TCE (2000) Parallel machine scheduling with batch delivery costs. Int J Prod Econ 68:177–183

    Article  Google Scholar 

  • William J (1981) Heuristic techniques for simultaneous scheduling of production and distribution in multi-echelon structures: theory and empirical comparisons. Manag Sci 27:336–351

    Article  Google Scholar 

  • Yang S, Tinós R (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Automat Comput 4(3):243–254

    Article  Google Scholar 

  • Yang W, Chan FTS, Kumar V (2012) Optimizing replenishment polices using genetic algorithm for single-warehouse multi-retailer system. Expert Syst Appl 39:3081–3086

    Article  Google Scholar 

  • Zegordi SH, Kamal Abadi IN, Beheshti Nia MA (2010) A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Comput Ind Eng 58:373–381

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Ministry of Science and Technology in Taiwan under Grant MOST: 103-2410-H-167 -007 -MY2. We thank the guest editor and the anonymous referees for their helpful comments, which improved this article.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chin Yi University of Technology, Taichung, Taiwan

    He-Yau Kang

  2. Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan

    W. L. Pearn & I-Ping Chung

  3. Department of Technology Management, Chung Hua University, Hsinchu, Taiwan

    Amy H. I. Lee

Authors
  1. He-Yau Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. L. Pearn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I-Ping Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amy H. I. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amy H. I. Lee.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, HY., Pearn, W.L., Chung, IP. et al. An enhanced model for the integrated production and transportation problem in a multiple vehicles environment. Soft Comput 20, 1415–1435 (2016). https://doi.org/10.1007/s00500-015-1595-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1595-7

Keywords

Search

Navigation