KAPALI DÖNGÜ TEDARİK ZİNCİRİNDE YEŞİL LOJİSTİK VE YER SEÇİMİ İÇİN BİR KARMA TAMSAYILI PROGRAMLAMA MODELİ ÖNERİSİ
Öz
Günümüzde, iklim değişikliği ve küresel ısınma öneminin artmasıyla, dünyada tedarik zincirlerinin çevresel etkilerini azaltmayı amaçlayan birçok mevzuat ve düzenleme yayınlanmıştır. Bu nedenle, Yeşil Tedarik Zinciri Yönetimi (YTZY) şirketler için önemli bir paradigma olarak ortaya çıkmıştır. Bu çalışmada, çevresel hususlar yeni bir Kapalı Döngü Tedarik Zinciri (KDTZ) Modeli ile incelenmiştir. Potansiyel tesislerin teknoloji seviyelerine (düşük, orta, yüksek) ilişkin toplam tedarik zinciri maliyeti ve karbondioksit emisyonunu minimize etmek amacıyla yeni ve kullanılmış ürünler için tesisler, toplama merkezleri ve yenileme merkezlerinin optimal yerleşimine karar vermek üzere yeşil veya sürdürülebilir yer seçimi ve ulaşım konuları ele alınmıştır. Burada, düşük teknoloji seviyesi, tesislerde daha düşük tesis sabit maliyeti, ancak daha yüksek karbondioksit emisyon seviyesini; yüksek teknoloji seviyesi ise tesislerde daha yüksek tesis sabit maliyeti ancak daha düşük çevresel maliyetleri ifade etmektedir. Önerilen model, yeşil ulaşım vasıtasıyla taşıma maliyeti ve karbondioksit emisyon maliyeti arasında bir denge oluşturmaktadır. Şebekedeki tüm ulaşımların, taşıma için üç farklı tipte taşıtın kullanıldığı bir lojistik firmasından sağlandığı kabul edilmiştir. Bu araç tiplerinin her biri ile taşınabilen yük aralıkları önceden belirlenmiştir. Büyük boyutlu kamyonlar seçilerek taşıma masrafları azaltılabilmektedir ancak bu durum küçük boyutlu kamyonlarla karşılaştırıldığında olumsuz çevresel etkilere neden olmaktadır. Bu tür çatışmalarla başa çıkmak için bir Karma Tamsayılı Doğrusal Programlama (MILP) Modeli önerilmiştir. Geliştirilen modelin etkinliğini göstermek için sayısal bir örnek uygulanmış ve analiz edilmiştir.
Anahtar Kelimeler
Yeşil Tedarik Zinciri Yönetimi Karbondioksit Emisyonu Yeşil Yer Seçimi Yeşil Ulaşım Yük Aralığı.
Kaynakça
- • DEMİREL, N. Ö. ve GÖKÇEN, H., (2008), “A mixed integer programming model for remanufacturing in reverse logistics environment”, The International Journal of Advanced Manufacturing Technology, c. 39, s. 11–12, ss. 1197–1206.
- • DUES, C. M., TAN, K. H. & LIM, M., (2013), “Green as the new Lean: How to use Lean practices as a catalyst to greening your supply chain”, Journal of Cleaner Production, c. 40, ss. 93-100.
- • EPA, UNITED STATES ENVIRONMENTAL PROTECTION AGENCY, (2017), “Fast Facts on Transportation Greenhouse Gas Emissions”, https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions.
- • FAHIMNIA, B., SARKIS, J. & ESHRAGH, A., (2015), “A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis”, Omega, c. 54, ss. 173-190.
- • GOVINDAN, K., SHANKAR, K. M. & KANNAN, D., (2016), “Application of fuzzy analytic network process for barrier evaluation in automotive parts remanufacturing towards cleaner production - a study in an Indian scenario”, Journal of Cleaner Production, c.114, ss. 199-213.
- • MITRA, S., (2013), “Periodic review policy for a two-echelon closed-loop inventory system with correlations between demands and returns”, OPSEARCH, c. 50, s. 4, ss. 604–615.
- • PISHVAEE, M. S., RABBANI, M. & TORABI, S. A., (2011), “A robust optimization approach to closed-loop supply chain network design under uncertainty”, Applied Mathematical Modelling, c. 35, s. 2, ss. 637–649.
- • TAHIROV, N., HASANOV, P. & JABER, M. Y., (2016), “Optimization of closed-loop supply chain of multi-items with returned subassemblies”, International Jourmnal of Production Economics, c. 174, ss. 1–10.
- • TOKTAY, L. B. & WEI, D., (2011), “Cost allocation in manufacturing–Remanufacturing operations”, Production and Operations Management, c. 20, s. 6, ss. 841–847.
- • TOPCU, A., BENNEYAN, J. C. & CULLINANE, T. P., (2013), “A simulation–optimisation approach for reconfigurable inventory space planning in remanufacturing facilities”, International Journal of Business Performance and Supply Chain Modelling, c. 5, s. 1, ss. 86–114.
- • TRANSPORTATION COST and BENEFIT ANALYSIS II –Air Pollution Costs (2017), http://www.vtpi.org/tca/tca0510.pdf.
- • UGARTE, G. M., GOLDEN, J. S. & DOOLEY, K. J., (2016), “Lean versus green: The impact of lean logistics on greenhouse gas emissions in consumer goods supply chains”, Journal of Purchasing and Supply Management, c. 22, s. 2, ss. 98-109.
- • WEI, J., ZHAO, J. & SUN, X., (2013), “Reverse channel decisions for a fuzzy closed-loop supply chain”, Applied Mathematical Modelling, c. 37, s. 3, ss. 1502–1513.
A MIXED-INTEGER PROGRAMMING MODEL FOR GREEN LOCATION AND TRANSPORTATION IN A CLOSED-LOOP SUPPLY CHAIN
Öz
Nowadays, due to increasing importance of climate change and global warming, numerous legislations and regulations aiming to reduce environmental impact of supply chains have been published throughout the world. Therefore, Green Supply Chain Management (GSCM) has emerged as an important paradigm for the companies. In this paper, we investigate the environmental subjects by the help of a new Closed-Loop Supply Chain (CLSC) model. Green or sustainable location and transportation issues are discussed while deciding on the optimal locations of plants, collection centers and refurbishing centers for new and used products in order to minimize the total supply chain cost and carbon-dioxide emission considering the technology levels (low, medium, high) of potential plants. Herein, low technology level refers to lower fixed facility costs but higher carbon-dioxide emission levels at plants. Contrarily, high technology refers to higher fixed facility costs but lower environmental costs at plants. By the means of green transportation, the proposed model includes a trade-off between transportation cost and carbon-dioxide emission cost. It is assumed that all transportation in the network is out-sourced to a logistics firm where three different types of vehicles are used. Load ranges that can be transported with each of these vehicle types are pre-determined. Transportation costs can be reduced by choosing large-sized trucks but it causes negative environmental effects compared to small-sized ones. To handle these kinds of conflicts, a mixed-integer linear programming (MILP) model is proposed. A numerical example is implemented and analysed in order to demonstrate the efficiency of the developed model.
Anahtar Kelimeler
Green Supply Chain Management Carbon Dioxide Emission Green Location Green Transportation Load Ranges.
Kaynakça
- • DEMİREL, N. Ö. ve GÖKÇEN, H., (2008), “A mixed integer programming model for remanufacturing in reverse logistics environment”, The International Journal of Advanced Manufacturing Technology, c. 39, s. 11–12, ss. 1197–1206.
- • DUES, C. M., TAN, K. H. & LIM, M., (2013), “Green as the new Lean: How to use Lean practices as a catalyst to greening your supply chain”, Journal of Cleaner Production, c. 40, ss. 93-100.
- • EPA, UNITED STATES ENVIRONMENTAL PROTECTION AGENCY, (2017), “Fast Facts on Transportation Greenhouse Gas Emissions”, https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions.
- • FAHIMNIA, B., SARKIS, J. & ESHRAGH, A., (2015), “A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis”, Omega, c. 54, ss. 173-190.
- • GOVINDAN, K., SHANKAR, K. M. & KANNAN, D., (2016), “Application of fuzzy analytic network process for barrier evaluation in automotive parts remanufacturing towards cleaner production - a study in an Indian scenario”, Journal of Cleaner Production, c.114, ss. 199-213.
- • MITRA, S., (2013), “Periodic review policy for a two-echelon closed-loop inventory system with correlations between demands and returns”, OPSEARCH, c. 50, s. 4, ss. 604–615.
- • PISHVAEE, M. S., RABBANI, M. & TORABI, S. A., (2011), “A robust optimization approach to closed-loop supply chain network design under uncertainty”, Applied Mathematical Modelling, c. 35, s. 2, ss. 637–649.
- • TAHIROV, N., HASANOV, P. & JABER, M. Y., (2016), “Optimization of closed-loop supply chain of multi-items with returned subassemblies”, International Jourmnal of Production Economics, c. 174, ss. 1–10.
- • TOKTAY, L. B. & WEI, D., (2011), “Cost allocation in manufacturing–Remanufacturing operations”, Production and Operations Management, c. 20, s. 6, ss. 841–847.
- • TOPCU, A., BENNEYAN, J. C. & CULLINANE, T. P., (2013), “A simulation–optimisation approach for reconfigurable inventory space planning in remanufacturing facilities”, International Journal of Business Performance and Supply Chain Modelling, c. 5, s. 1, ss. 86–114.
- • TRANSPORTATION COST and BENEFIT ANALYSIS II –Air Pollution Costs (2017), http://www.vtpi.org/tca/tca0510.pdf.
- • UGARTE, G. M., GOLDEN, J. S. & DOOLEY, K. J., (2016), “Lean versus green: The impact of lean logistics on greenhouse gas emissions in consumer goods supply chains”, Journal of Purchasing and Supply Management, c. 22, s. 2, ss. 98-109.
- • WEI, J., ZHAO, J. & SUN, X., (2013), “Reverse channel decisions for a fuzzy closed-loop supply chain”, Applied Mathematical Modelling, c. 37, s. 3, ss. 1502–1513.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 26 Haziran 2020 |
| Gönderilme Tarihi | 21 Ocak 2019 |
| Yayımlandığı Sayı | Yıl 2020 Sayı: 3 |
Verimlilik Dergisi Creative Commons Atıf-GayrıTicari 4.0 Uluslararası Lisansı (CC BY-NC 4.0) ile lisanslanmıştır.