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  • یکپارچه سازی پیک سفارش در انبار و توزیع به خطوط تولیدی جهت حداقل کردن هزینه با رویکرد الگوریتم وال انطباقی

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کد مقاله : IMJ-2112-1721 (R1) بازدید : 90 صفحه: 71 - 91

10.30495/imj.2022.1946572.1721

نوع مقاله: پژوهشی

یکپارچه سازی پیک سفارش در انبار و توزیع به خطوط تولیدی جهت حداقل کردن هزینه با رویکرد الگوریتم وال انطباقی

محورهای موضوعی : مدیریت صنعتی

Amir Reza Ahmadi Keshavarz 1 (Department of Industrial Engineering, Parand Branch Islamic Azad University, Parand, Iran)
davood jaafari 2 (Department of Industrial Engineering, Parand Branch Islamic Azad University, Parand, Iran)
mehran khalaj 3 (مدیر گروه)
Parshang Dokouhaki 4 (Department of Industrial Engineering, Parand Branch Islamic Azad University, Parand, Iran)

تاریخ دریافت : 1400/09/14 تاریخ پذیرش : 1401/04/05 تاریخ انتشار : 1401/04/26

کلید واژه: برنامه‌ریزی غیرخطی, میسر‌یابی پیک‌کننده, الگوریتم‌ بهینه‌سازی وال انطباقی, هزینه تأخیر پیکینگ, حداقل کردن فرآیندیکپارچه پیکینگ‌و‌توزیع,

چکیده مقاله :

فرآیند پیکینگ در انبار, یکی از پرهزینه‌ترین فعالیت‌های لجستیکی محسوب می‌شود. توجه به جنبه های لجستیک داخلی‌با توجه به محدودیتها و منابع در دسترس در راستای کاهش هزینه ها با افزایش سطح توانایی‌سیستم های تغذیه مواد و قطعات به کنار خط حاصل خواهند شد. باتوجه به تأثیر زمان اتمام عملیات پیک بر زمان آغاز عملیات توزیع و هزینه بر بودن تأخیر در آماده‌سازی سفارش، پژوهش حاضر برآن است تا مسئله جدیدی را در ارتباط با فرآیند یکپارچه پیک سفارشات در انبار و تحویل درموعد مقرر به کنار خطوط تولیدی، جهت حداقل‌کردن هزینه‌ها، با توجه به داده های یک شرکت خوردو سازی مورد بررسی قرار دهد. در این راستا یک مدل برنامه ریزی غیرخطی عدد صحیح، با هدف حداقل‌ نمودن هزینه‌های ناشی از تأخیر پیشنهاد شده ‌است. به منظور اعتبار سنجی مدل ، مسئله در ابعاد کوچک به روش دقیق حل شده است و سپس با توجه به NP-Hard بودن مسئله، در نمونه مسئله بزرگ از روش الگوریتم وال استفاده شده است. به منظور بهبود راه حل های بهینه با ایجاد تغییرات در الگوریتم اولیه و با طراحی الگوریتم وال انطباقی، با در نظر گرفتن هزینه و زمان بازدید از ایستگاه های کاری به عنوان تابع برازش ، مسئله بررسی شده است. جهت ارزیابی کارآمدی الگوریتم وال انطباقی پیشنهادی، نتایج با دو الگوریتم فراابتکاری ازدحام ذرات و گرگ خاکستری مقایسه شد. نتایج بررسی ها نشان دهنده عملکرد بهتر الگوریتم وال انطباقی پیشنهادی، نسبت به سایر روش های مورد بررسی بوده که موجب بهبود و کاهش هزینه ها شده است.

چکیده انگلیسی:

One of the most costly logistics activities is the picking process in the warehouse. Considering the internal logistics aspects, due to the limitations and resources available in order to reduce costs by increasing the level of capability, the supply systems of materials and components will be achieved along the line. Considering the effect of the completion time of pick operations on the start time of distribution operations and the cost of order preparation tardiness, the present study aimed to investigate a new issue related to the integrated process of order preparation in the warehouse and delivery on time to minimize cost according to the data of a car companys. In this regard, an integer nonlinear programming model is proposed to minimize the costs caused by tardiness. In order to validate the model, the small problem is solved in exact way. To solve the model, since the problem is NP-Hard, the method of whale optimization algorithm was used and to improve the optimal routing solutions, the problem was investigated by designing an adaptive whale algorithm considering the cost and time of visiting workstations as a fitting function. Also, to assess the proposed adaptive whale algorithm, the results were compared with two meta-heuristic algorithms of particle swarm optimization and gray wolf. The results show that the proposed adaptive wall algorithm performs better than other methods, which improves and reduces costs.

منابع و مأخذ:


  1. Ahmadi Keshavarz, A. R., Jaafari, D., Khalaj, M., & Dokouhaki, P. (2021a). A Survey of the Literature on Order-Picking Systems by Combining Planning Problems. Applied Sciences, 11(22), 10641. https://doi.org/10.3390/app112210641
    2.
  2. Ahmadi Keshavarz, A. R., Jaafari, D., Khalaj, M., & Dokouhaki, P. (2021b). Model Presentation to Emptying the Picking Warehouse with Heterogeneous Containers in Emergency Situations with Swarm Intelligence Algorithms. Strategic Management in industrial systems, 16(57).
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  3. Ardjmand, E., & Huh, D. W. (2017, November). Coordinated warehouse order picking and production scheduling: A nsga-ii approach. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI)(pp. 1-8). IEEE.
    4.
  4. Ardjmand, E., Shakeri, H., Singh, M., & Bajgiran, O. S. (2018). Minimizing order picking makespan with multiple pickers in a wave picking warehouse. International Journal of Production Economics, 206, 169-183.
    5.
  5. Ardjmand, E., Ghalehkhondabi, I., Young II, W. A., Sadeghi, A., Weckman, G. R., & Shakeri, H. (2020). A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations. Expert Systems with Applications, 159, 113566.
    6.
  6. Cano, J. A., Correa-Espinal, A. A., & Gómez-Montoya, R. A. (2020). Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems. Journal of King Saud University-Engineering Sciences, 32(3), 219-228.
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  7. Cergibozan, Ç., & Tasan, A. S. (2019). Order batching operations: an overview of classification, solution techniques, and future research. Journal of Intelligent Manufacturing, 30(1), 335-349.
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  8. Chen, T. L., Cheng, C. Y., Chen, Y. Y., & Chan, L. K. (2015). An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. International Journal of Production Economics, 159, 158-167.
    9.
  9. Cortés, P., Gómez-Montoya, R. A., Muñuzuri, J., & Correa-Espinal, A. (2017). A tabu search approach to solving the picking routing problem for large-and medium-size distribution centres considering the availability of inventory and K heterogeneous material handling equipment. Applied Soft Computing, 53, 61-73.
    10.
  10. Feng, X., & Hu, X. (2021). A Heuristic Solution Approach to Order Batching and Sequencing for Manual Picking and Packing Lines considering Fatiguing Effect. Scientific Programming, 2021. https://doi.org/10.1155/2021/8863391

  11. Geismar, H. N., Laporte, G., Lei, L., & Sriskandarajah, C. (2008). The integrated production and transportation scheduling problem for a product with a short lifespan. INFORMS Journal on Computing, 20(1), 21-33.
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  17. Kübler, P., Glock, C. H., & Bauernhansl, T. (2020). A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses. Computers & Industrial Engineering, 147, 1
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  19. Matusiak, M., de Koster, R., & Saarinen, J. (2017). Utilizing individual picker skills to improve order batching in a warehouse. European Journal of Operational Research, 263(3), 888-899.
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  20. Menéndez, B., Bustillo, M., Pardo, E. G., & Duarte, A. (2017a). General variable neighborhood search for the order batching and sequencing problem. European Journal of Operational Research, 263(1), 82-93.
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  21. Menéndez, B., Pardo, E. G., Alonso-Ayuso, A., Molina, E., & Duarte, A. (2017b). Variable neighborhood search strategies for the order batching problem. Computers & Operations Research, 78, 500-512.
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  22. Menendez, B., Pardo, E. G., Sánchez‐Oro, J., & Duarte, A. (2017c). Parallel variable neighborhood search for the min–max order batching problem. International Transactions in Operational Research, 24(3), 635-662.
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  24. Muter, İ., & Öncan, T. (2021). Order batching and picker scheduling in warehouse order picking. IISE Transactions, 1-13.
    25.
  25. Pansart, L., Catusse, N., & Cambazard, H. (2018). Exact algorithms for the order picking problem. Computers & Operations Research, 100, 117-127.
    26.
  26. Scholz, A., Schubert, D., & Wäscher, G. (2017). Order picking with multiple pickers and due dates–simultaneous solution of order batching, batch assignment and sequencing, and picker routing problems. European Journal of Operational Research, 263(2), 461-4
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  27. Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. John Wiley & Sons.
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  28. Valle, C. A., Beasley, J. E., & Da Cunha, A. S. (2017). Optimally solving the joint order batching and picker routing problem. European Journal of Operational Research, 262(3), 817-834.
    29.
  29. Van Gils, T., Caris, A., Ramaekers, K., & Braekers, K. (2019). Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse. European Journal of Operational Research, 277(3), 814-830.
    30.
  30. Van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research, 267(1), 1-15.
    31.
  31. Zhang, J., Wang, X., Chan, F. T., & Ruan, J. (2017). On-line order batching and sequencing problem with multiple pickers: A hybrid rule-based algorithm. Applied Mathematical Modelling, 45, 271-284.
    32.
_||_

  1. Ahmadi Keshavarz, A. R., Jaafari, D., Khalaj, M., & Dokouhaki, P. (2021a). A Survey of the Literature on Order-Picking Systems by Combining Planning Problems. Applied Sciences, 11(22), 10641. https://doi.org/10.3390/app112210641
    2.
  2. Ahmadi Keshavarz, A. R., Jaafari, D., Khalaj, M., & Dokouhaki, P. (2021b). Model Presentation to Emptying the Picking Warehouse with Heterogeneous Containers in Emergency Situations with Swarm Intelligence Algorithms. Strategic Management in industrial systems, 16(57).
    3.
  3. Ardjmand, E., & Huh, D. W. (2017, November). Coordinated warehouse order picking and production scheduling: A nsga-ii approach. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI)(pp. 1-8). IEEE.
    4.
  4. Ardjmand, E., Shakeri, H., Singh, M., & Bajgiran, O. S. (2018). Minimizing order picking makespan with multiple pickers in a wave picking warehouse. International Journal of Production Economics, 206, 169-183.
    5.
  5. Ardjmand, E., Ghalehkhondabi, I., Young II, W. A., Sadeghi, A., Weckman, G. R., & Shakeri, H. (2020). A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations. Expert Systems with Applications, 159, 113566.
    6.
  6. Cano, J. A., Correa-Espinal, A. A., & Gómez-Montoya, R. A. (2020). Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems. Journal of King Saud University-Engineering Sciences, 32(3), 219-228.
    7.
  7. Cergibozan, Ç., & Tasan, A. S. (2019). Order batching operations: an overview of classification, solution techniques, and future research. Journal of Intelligent Manufacturing, 30(1), 335-349.
    8.
  8. Chen, T. L., Cheng, C. Y., Chen, Y. Y., & Chan, L. K. (2015). An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. International Journal of Production Economics, 159, 158-167.
    9.
  9. Cortés, P., Gómez-Montoya, R. A., Muñuzuri, J., & Correa-Espinal, A. (2017). A tabu search approach to solving the picking routing problem for large-and medium-size distribution centres considering the availability of inventory and K heterogeneous material handling equipment. Applied Soft Computing, 53, 61-73.
    10.
  10. Feng, X., & Hu, X. (2021). A Heuristic Solution Approach to Order Batching and Sequencing for Manual Picking and Packing Lines considering Fatiguing Effect. Scientific Programming, 2021. https://doi.org/10.1155/2021/8863391

  11. Geismar, H. N., Laporte, G., Lei, L., & Sriskandarajah, C. (2008). The integrated production and transportation scheduling problem for a product with a short lifespan. INFORMS Journal on Computing, 20(1), 21-33.
    12.
  12. Golden, B. L., Magnanti, T. L., & Nguyen, H. Q. (1977). Implementing vehicle routing algorithms. Networks, 7(2), 113-148.
    13.
  13. Grosse, E. H., Glock, C. H., & Neumann, W. P. (2017). Human factors in order picking: a content analysis of the literature. International Journal of Production Research, 55(5), 1260-1276.
    14.
  14. Gupta, A., & Kumar, A. (2012). A new method for solving linear multi-objective transportation problems with fuzzy parameters. Applied Mathematical Modelling, 36(4), 1421-1430.
    15.
  15. Isler, C. A., Righetto, G. M., & Morabito, R. (2016). Optimizing the order picking of a scholar and office supplies warehouse. The International Journal of Advanced Manufacturing Technology, 87(5), 2327-2336.
    16.
  16. Koch, S., & Wäscher, G. (2016). A grouping genetic algorithm for the order batching problem in distribution warehouses. Journal of Business Economics, 86(1-2), 131-153.
    17.
  17. Kübler, P., Glock, C. H., & Bauernhansl, T. (2020). A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses. Computers & Industrial Engineering, 147, 1
    18.
  18. Lin, C. C., Kang, J. R., Hou, C. C., & Cheng, C. Y. (2016). Joint order batching and picker Manhattan routing problem. Computers & Industrial Engineering, 95, 164-174.
    19.
  19. Matusiak, M., de Koster, R., & Saarinen, J. (2017). Utilizing individual picker skills to improve order batching in a warehouse. European Journal of Operational Research, 263(3), 888-899.
    20.
  20. Menéndez, B., Bustillo, M., Pardo, E. G., & Duarte, A. (2017a). General variable neighborhood search for the order batching and sequencing problem. European Journal of Operational Research, 263(1), 82-93.
    21.
  21. Menéndez, B., Pardo, E. G., Alonso-Ayuso, A., Molina, E., & Duarte, A. (2017b). Variable neighborhood search strategies for the order batching problem. Computers & Operations Research, 78, 500-512.
    22.
  22. Menendez, B., Pardo, E. G., Sánchez‐Oro, J., & Duarte, A. (2017c). Parallel variable neighborhood search for the min–max order batching problem. International Transactions in Operational Research, 24(3), 635-662.
    23.
  23. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
    24.
  24. Muter, İ., & Öncan, T. (2021). Order batching and picker scheduling in warehouse order picking. IISE Transactions, 1-13.
    25.
  25. Pansart, L., Catusse, N., & Cambazard, H. (2018). Exact algorithms for the order picking problem. Computers & Operations Research, 100, 117-127.
    26.
  26. Scholz, A., Schubert, D., & Wäscher, G. (2017). Order picking with multiple pickers and due dates–simultaneous solution of order batching, batch assignment and sequencing, and picker routing problems. European Journal of Operational Research, 263(2), 461-4
    27.
  27. Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning. John Wiley & Sons.
    28.
  28. Valle, C. A., Beasley, J. E., & Da Cunha, A. S. (2017). Optimally solving the joint order batching and picker routing problem. European Journal of Operational Research, 262(3), 817-834.
    29.
  29. Van Gils, T., Caris, A., Ramaekers, K., & Braekers, K. (2019). Formulating and solving the integrated batching, routing, and picker scheduling problem in a real-life spare parts warehouse. European Journal of Operational Research, 277(3), 814-830.
    30.
  30. Van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research, 267(1), 1-15.
    31.
  31. Zhang, J., Wang, X., Chan, F. T., & Ruan, J. (2017). On-line order batching and sequencing problem with multiple pickers: A hybrid rule-based algorithm. Applied Mathematical Modelling, 45, 271-284.
    32.

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