Aggregate production planning: A literature review and future research directions

Aggregate production planning (APP) is concerned with determining the optimum production and workforce levels for each period over the medium term planning horizon. It aims to set overall production levels for each product family to meet fluctuating demand in the near future. APP is one of the most critical areas of production planning systems. After the state-of-the-art summaries in 1992 by Nam and Logendran [ Nam, S. J., & Logendran, R. (1992). Aggregate production planning—a survey of models and methodologies. European Journal of Operational Research, 61(3), 255-272. ], which specifically summarized the various existing techniques from 1950 to 1990 into a framework depending on their abilities to either produce an exact optimal or near-optimal solution, there has not been any systematic survey in the literature. This paper reviews the literature on APP models to meet two main purposes. First, a systematic structure for classifying APP models is proposed. Second, the existing gaps in the literature are demonstrated in order to extract future directions of this research area. This paper covers a variety of APP models’ characteristics including modeling structures, important issues, and solving approaches, in contrast to other literature reviews in this field which focused on methodologies in APP models. Finally some directions for future research in this research area are suggested

Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model

With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to re-examine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model

Modelo para la optimización del plan agregado de producción de empresas textiles aplicando técnicas de inteligencia artificial

Diseñar un modelo para optimizar el Plan Agregado de Producción que conduzca al mejoramiento de la productividad y competitividad de empresas textiles mediante la aplicación de técnicas de inteligencia artificial.En condiciones reales, el desarrollo de un Plan Agregado de Producción reviste un carácter complejo y dinámico, debido a que involucra múltiples variables productivas que se interrelacionan con la finalidad de responder a las fluctuaciones en la demanda y las condiciones inciertas a las cuales está sujeta la industria textil y de confección de la provincia de Imbabura. Para superar tales limitaciones, el empleo de técnicas de inteligencia artificial bioinspiradas han demostrado un desempeño óptimo frente a metodologías tradicionales que generan planes de producción que, en varios escenarios, llegan a comprometer la obtención de costos totales mínimos y altos niveles de servicio. Basado en lo anterior, en este trabajo, se estudia a la Planificación de Producción Agregada y las técnicas que, bajo el contexto de inteligencia artificial, permiten optimizarlo y, además, se propone un modelo de optimización de enjambre de partículas (PSO) para optimizar el Plan Agregado de Producción de tres familias de productos de una empresa textil y de confección de la provincia de Imbabura. Los resultados experimentales demuestran que el comportamiento colectivo, robusto e inteligente de la técnica de inteligencia artificial propuesta, sobre un modelo matemático de programación lineal de enteros mixtos, permitió minimizar los costos totales en USD 3644,45 anuales e incrementar los niveles de servicio en 9,84%, 2,77% y 16.72% para las familias de productos 1, 2 y 3, respectivamente.Ingenierí

Towards Optimal Application Mapping for Energy-Efficient Many-Core Platforms

Siirretty Doriast

A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset

شعاع الدعم الالي (SVM) هو أحد تطبيقات معادلة الانحدار للتعليم الاستنتاجي الذي يحلل البيانات ويستخدم في التصنيف ومعادلة الانحدار. في التصنيف، يستخدم SVM بشكل واسع بأختيار مقطع مثالي للفصل بين مجموعتين. وهو يمتلك دقة عالية و مستقر بصورة هائلة بالمقارنة مع طرق التصنيف الأخرى مثل الانحدار اللوجستي الخطي، random forest ،  k-nearest neighbor و  naïve model.على أي حال، عند العمل على بيانات هائلة تتولد مشاكل كبيرة كاستهلاك للوقت وأيضا النتائج  تكون غير دقيقة.  في هذا البحث SVM طورت بأستخدام طريقة الانحدار العشوائي. الطريقة المحدثة، SGD-SVM اختبرت بأستخدام مجموعتين من البيانات. ولأن تصنيف أنواع السرطان مهم بالنسبة لتشخيص السرطان واستكشاف الدواء. SGD-SVM طبقت لتصنيف بيانات تكسر كريات الدم الشهيرة. النتائج التي حصلنا عليها من طريقة SGD-SVM كانت دقتها اعلى من النتائج التي تم الحصول عليها من بعض الدراسات السابقة التي استخدمت نفس البيانات.Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different cancer types is important for cancer diagnosis and drug discovery, SGD-SVM is applied for classifying the most common leukemia cancer type dataset. The results that are gotten using SGD-SVM are much accurate than other results of many studies that used the same leukemia datasets

Nature-inspired optimisation: Improvements to the Particle Swarm Optimisation Algorithm and the Bees Algorithm

This research focuses on nature-inspired optimisation algorithms, in particular, the Particle Swarm Optimisation (PSO) Algorithm and the Bees Algorithm. The PSO Algorithm is a population-based stochastic optimisation technique first invented in 1995. It was inspired by the social behaviour of birds flocking or a school of fish. The Bees Algorithm is a population-based search algorithm initially proposed in 2005. It mimics the food foraging behaviour of swarms of honey bees. The thesis presents three algorithms. The first algorithm called the PSO-Bees Algorithm is a cross between the PSO Algorithm and the Bees Algorithm. The PSO-Bees Algorithm enhanced the PSO Algorithm with techniques derived from the Bees Algorithm. The second algorithm called the improved Bees Algorithm is a version of the Bees Algorithm that incorporates techniques derived from the PSO Algorithm. The third algorithm called the SNTO-Bees Algorithm enhanced the Bees Algorithm using techniques derived from the Sequential Number-Theoretic Optimisation (SNTO) Algorithm. To demonstrate the capability of the proposed algorithms, they were applied to different optimisation problems. The PSO-Bees Algorithm is used to train neural networks for two problems, Control Chart Pattern Recognition and Wood Defect Classification. The results obtained and those from tests on well known benchmark functions provide an indication of the performance of the algorithm relative to that of other swarm-based stochastic optimisation algorithms. The improved Bees Algorithm was applied to mechanical design optimisation problems (design of welded beams and coil springs) and the mathematical benchmark problems used previously to test the PSO-Bees Algorithm. The algorithm incorporates cooperation and communication between different neighbourhoods. The results obtained show that the proposed cooperation and communication strategies adopted enhanced the performance and convergence of the algorithm. The SNTO-Bees Algorithm was applied to a set of mechanical design optimisation problems (design of welded beams, coil springs and pressure vessel) and mathematical benchmark functions used previously to test the PSO-Bees Algorithm and the improved Bees Algorithm. In addition, the algorithm was tested with a number of deceptive multi modal benchmark functions. The results obtained help to validate the SNTO-Bees Algorithm as an effective global optimiser capable of handling problems that are deceptive in nature with high dimensions

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research