زمان بندی یکپارچه ی تولید و توزیع در یک زنجیره تأمین تولید پروتزهای دندانی در محیط تولید افزایشی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
در جهان رقابتی، یکی از مهم ترین راهکارها برای بهبود عملکرد زنجیره تأمین شرکت های تولیدی، برنامه ریزی یکپارچه ی بخش های تولید و توزیع است. دو معضل اصلی برای دندان پزشکان و بیماران، عدم تحویل به موقع پروتزهای دندانی و فرآیند چندباره تولید و اصلاحات پروتزها می باشد. در این پژوهش، در راستای حل این معضلات، یک مدل ریاضی برای حل مسئله برنامه ریزی یکپارچه تولید و توزیع در یک زنجیره تأمین تولید پروتزهای ثابت دندان در محیط تولید افزایشی توسعه یافته است. توابع هدف این مدل شامل کمینه کردن هزینه تولید و ارسال سفارشات و کمینه کردن مجموع وزن دار تأخیرات است. همچنین، تصمیمات مرتبط با ارسال دسته ای سفارشات و یافتن بهترین مسیر برای ارسال هر دسته نیز در نظر گرفته شده است. به منظور تک هدفه کردن مدل ریاضی و یافتن جواب های پارتو، از روش اپسیلون محدودیت تقویت شده استفاده شده است. در نهایت، به منظور اعتبارسنجی مدل ریاضی، یک مثال عددی و یک مطالعه موردی ارائه و همچنین، تحلیل حساسیت روی پارامترهای کلیدی مدل پیشنهادی انجام شده است. نتایج به دست آمده بیانگر کاهش قابل ملاحظه ی هزینه های تولید و توزیع و همچنین افزایش سطح رضایت مشتریان با توجه به کاهش میزان تأخیرات در تحویل محصولات به مشتریان است.Integrated production and distribution scheduling in a dental prosthetics supply chain under additive manufacturing environment
In a competitive world, one of the most crucial ways to enhance the supply chain performance of manufacturing companies is through integrated scheduling of production and distribution activities. Two significant concerns for dentists and patients include delayed denture deliveries and the multiple production and correction processes for dentures. This research addresses these concerns by developing a mixed-integer linear programming model for solving the integrated production and distribution scheduling problem in a fixed denture supply chain operating under an additive manufacturing environment. The objective functions of this model aim to minimize the cost of production and distribution orders while reducing weighted delays. The Augmented Epsilon Constraint Method is employed to identify Pareto-optimal solutions. To validate the mathematical model, a numerical example and a case study are presented, and various sensitivity analyses are conducted on key model parameters. The numerical results demonstrate substantial improvements in total costs and customer satisfaction levels.IntroductionA supply chain (SC) comprises several interconnected echelons and processes, where an integrated perspective can lead to optimal overall SC performance. Simply improving an organization's internal processes is insufficient for competitiveness in the market; establishing effective relationships with suppliers, distributors, and other SC stakeholders is essential. Achieving maximum value along the SC involves focusing on cost reduction through cost-effective decision-making. In the past decade, the rising adoption of 3-D printing and additive manufacturing technologies in SCs, as a prominent disruptive technology in the Industry 4.0 era, has created numerous opportunities for improving manufacturing SCs compared to traditional production methods. These opportunities include reduced setup and production times, lower safety stock levels, and fewer processing steps. Additive manufacturing has found applications in various fields, particularly in denture production. This research addresses two primary concerns in the field: timely denture delivery and the multiple production and correction processes associated with dentures. A novel mathematical model is developed to tackle these issues, aiming to solve the integrated production and distribution scheduling problem in a fixed denture supply chain operating within an additive manufacturing environment. The objective functions of this model aim to minimize the costs associated with production and order distribution while minimizing the weighted total delays.Materials and MethodsA mixed-integer linear programming model is devised to address the problem outlined in this paper. The Augmented Epsilon Constraint Method is applied to identify Pareto-optimal solutions. To validate the mathematical model, a numerical example and a case study are presented, and several sensitivity analyses are conducted on key model parameters to elucidate their critical roles in the final solutions.Discussion and ResultsA case study is provided to demonstrate the practical applicability of the developed model. Sensitivity analyses on demand data highlight the substantial impact of demand management on final solutions. This research presents a two-objective optimization model to address the simultaneous scheduling of production and order delivery in a three-tier dental prosthesis supply chain. The first tier comprises a dental prosthesis production laboratory, while the second and third tiers include distributors and dentists (final customers). The objective functions include the minimization of total order delivery costs and the average weighted lateness of delivered products from a fixed dental prosthesis production laboratory. Constraints encompass delivery time delays, order allocation to customers, capacity limitations, calculations of time to reach each customer, and vehicle routing. Given that this research problem falls into the category of multi-objective problems, the Augmented Epsilon Constraint Method is employed to obtain Pareto-optimal solutions. To investigate and implement the proposed model, a fixed dental prosthesis production laboratory in Neka City is examined. The numerical results indicate the existence of a trade-off between the problem's objectives.ConclusionsThis paper presents a bi-objective model to address the integrated production and distribution scheduling problem in a three-tier dentures supply chain, aiming to minimize total delivery costs and the average weighted tardiness. The first tier includes a dentures production laboratory, while the second and third tiers comprise distributors and dentists, respectively. Numerical results based on a real case study demonstrate the practical applicability of the model. Several avenues for future research include considering uncertainty in input data and developing efficient meta-heuristic algorithms for solving large-scale instances.
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