Tedarik zinciri optimizasyon çalışmaları: Literatür araştırması ve sınıflama

Supply chain planning is an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together. It comprises procurement, production, distribution and demand planning topics. These topics require taking strategical, tactical and operational decisions. This research aims to reveal which supply chain topics, which decision levels, and which optimization methods are mostly studied in supply chain planning. This paper presents a total of 77 reviewed works published between 1993 and 2016 about supply chain planning. The reviewed works are categorized according to following elements: decision levels, supply chain optimization topics, objectives, optimization models.Tedarik Zinciri, tedarikçiler, üreticiler, dağıtıcılar ve toptancılar gibi bir grup organizasyonu birleştiren entegre bir süreçtir. Tedarik, üretim, dağıtım ve talep planlama konularını içerir. Bu konular stratejik, taktik ve operasyonel kararlar almayı gerektirir. Bu araştırma tedarik zinciri planlamasında hangi tedarik zinciri konularının, hangi karar/planlama seviyelerinin ve hangi optimizasyon metotlarının literatürde en çok çalışıldığını göstermektedir. Çalışma 1993 ve 2016 yılları arasındaki tedarik zinciri planlama konusundaki 77 adet çalışmanın incelenmesine ait sonuçları sunmaktadır. İncelenen çalışmalar şu kriterlere gore kategorize edilmiştir: karar seviyesi, tedarik zinciri optimizasyon konuları, amaçlar, optimizasyon modelleri

Supply Chain Optimization Studies: A Literature Review and Classification

Supply chain planning is an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together. It comprises procurement, production, distribution and demand planning topics. These topics require taking strategical, tactical and operational decisions. This research aims to reveal which supply chain topics, which decision levels, and which optimization methods are mostly studied in supply chain planning. This paper presents a total of 77 reviewed works published between 1993 and 2016 about supply chain planning. The reviewed works are categorized according to following elements: decision levels, supply chain optimization topics, objectives, optimization models. 

Supply Chain Optimization Studies: A Literature Review and Classification

Supply chain planning is an integrated process in which a group of several organizations, such as suppliers, producers, distributors and retailers, work together. It comprises procurement, production, distribution and demand planning topics. These topics require taking strategical, tactical and operational decisions. This research aims to reveal which supply chain topics, which decision levels, and which optimization methods are mostly studied in supply chain planning. This paper presents a total of 77 reviewed works published between 1993 and 2016 about supply chain planning. The reviewed works are categorized according to following elements: decision levels, supply chain optimization topics, objectives, optimization models. 

Integrated tactical planning in the lumber supply chain under demand and supply uncertainty

Lumber supply chain includes forests as suppliers, sawmills as production sites, distribution centers, and different types of customers. In this industry, the raw materials are logs that are shipped from forest contractors to sawmills. Logs are then sawn to green/finished lumbers in sawmills and are distributed to the lumber market through different channels. Unlike a traditional manufacturing industry, the lumber industry is characterized by a divergent product structure with the highly heterogeneous nature of its raw material (logs). Moreover, predicting the exact amount of the product demand and the availability of logs in the forest is impossible in this industry. Thus, considering random demand and supply in the lumber supply chain planning is essential. Integrated tactical planning in a supply chain incorporates the synchronized planning of procurement, production, distribution and sale activities in order to ensure that the customer demand is satisfied by the right product at the right time. Briefly, in this dissertation, we aim at developing integrated planning tools in lumber supply chains for making decisions in harvesting, material procurement, production, distribution, and sale activities in order to obtain a maximum robust profit and service level in the presence of uncertainty in the log supply and product demand. In order to gain the latter objectives, we can categorize this research into three phases. In the first phase, we investigate the integrated annual planning of harvesting, procurement, production, distribution, and sale activities in the lumber supply chain in a deterministic context. The problem is formulated as a mixed integer programming (MIP) model. The proposed model is applied on a real-size case study, which leads to a large-scale MIP model that cannot be solved by commercial solvers in a reasonable time. Consequently, we propose a Lagrangian Relaxation based heuristic algorithm in order to solve the latter MIP model. While improving significantly the convergence, the proposed algorithm also guarantees the feasibility of the converged solution. In the second phase, the uncertainty is incorporated in the lumber supply chain tactical planning problems. Thus, we propose a multi-stage stochastic mixed-integer programming (MS-MIP) model to address this problem. Due to the complexity of solving the latter MS-MIP model with commercial solvers or relevant solution methodologies in the literature, we develop a Hybrid Scenario Cluster Decomposition (HSCD) heuristic algorithm which is also amenable to parallelization. This algorithm decomposes the original scenario tree into a set of smaller sub-trees. Hence, the MS-MIP model is decomposed into smaller sub-models that are coordinated by Lagrangian terms in their objective functions. By embedding an ad-hoc heuristic and a Variable Fixing algorithm into the HSCD algorithm, we considerably improve its convergence and propose an implementable solution in a reasonable CPU time. Finally, due to the computational complexity of multi-stage stochastic programming approach, we confine our formulation to the robust optimization method. Hence, at the third phase of this research, we propose a robust planning model formulated based on cardinality-constrained method. The latter provides some insights into the adjustment of the level of robustness of the proposed plan over the planning horizon and protection against uncertainty. An extensive set of experiments based on Monte-Carlo simulation is also conducted in order to better validate the proposed robust optimization approach applied on the harvesting planning in lumber supply chains

TWO MULTI-OBJECTIVE STOCHASTIC MODELS FOR PROJECT TEAM FORMATION UNDER UNCERTAINTY IN TIME REQUIREMENTS

Team formation is one of the key stages in project management. The cost associated with the individuals who form a team and the quality of the tasks completed by the team are two of the main concerns in team formation problems. In this study, two mathematical models to optimize simultaneously cost and quality in a team formation problem are developed. Because team formation problem arises in uncertain environment, different scenarios are defined for the time requirement of the project. Two-stage stochastic programming and multi-stage stochastic programming are applied to solve the first and the second model respectively. The presented models and their solution methodology can be applied in different types of projects. In this study, a project that involves an overhaul of an aircraft is presented as a case study in which the goals are to minimize staffing costs and maximize the reliability of the aircraft by staffing workforce with high competency

Closed-loop supply chain network design: Case of durable products

Closed loop supply chains comprise, in addition to the conventional forward flows from suppliers to end-users, a reverse flow of products, components, and materials from end-users to the manufacturers and secondary markets. Designing a closed-loop supply chain is a strategic level planning which considerably impacts on tactical and operational performance of the supply chain. It refers to the decisions taken on the location of facilities involved in the supply chain network along with the management of the physical flows associated with forward and product recovery channels. Our problem of interest is mainly motivated by the case of durable products including but not limited to large household appliances, computers, photocopying equipment, and aircraft engines. Such category of products has a modular structure, composed of independent components. As opposed to simple structured products, e.g., printer cartridges, that can only be recycled, each of the components in the reverse bill of materials of durable products can be recovered by a particular type of recovery process. Besides, durable products share a long life cycle characteristic which indeed makes designing their CLSC networks more complicated. In this thesis, in keeping with the abovementioned motivation, we focus on designing closed-loop and reverse supply chains in the context of durable products that are of various quality conditions. The recovery decisions for product return include remanufacturing, part harvesting, bulk recycling, material recycling, and landfilling/incineration. Moreover, we take into account environmental concerns regarding the harmful impacts of used products in the closed-loop supply chain planning. As the closed-loop supply chains typically encounter uncertainty in quality and quantity of the profitable return stream, we further aim to consider the impact of uncertainty in designing the recovery network. For such purposes, in the first phase, we address a closed-loop supply chain planning problem in the context of durable products with generic modular structures. The problem is formulated as a mixed-integer programming model which is then solved by an accelerated Benders decomposition-based algorithm. The performance of the proposed decomposition approach is enhanced through incorporating algorithmic features including valid inequalities, non-dominated optimality cuts, and local branching strategies. Next, in the second phase, we propose a precise approach to model the uncertain quality status of returns, in which the availability of each component in the reverse bill of materials is modeled as discrete scenarios. We propose a two-stage stochastic programming model to address this problem setting. Then, since the cardinality of the scenario set grows exponentially with the number of involved components, we detail on a scenario reduction scheme to alleviate the computational burden of the proposed model. The stochastic problem is solved by a L-shaped algorithm enhanced through valid inequalities and Pareto-optimal cuts. Finally, we investigate designing a dynamic reverse supply chain where the quantity of the return flows is uncertain. We introduce a multi-stage stochastic programming model and develop a heuristic inspired by scenario clustering decomposition scheme as the solution method. It revolves around decomposing the scenario tree into smaller sub-trees which consequently yields a number of sub-models in accordance with sub-trees. The resulting sub-models are then coordinated by Lagrangian penalty terms. On account of the fact that each sub-model per se is a hard to solve problem, a Benders decomposition-based algorithm is proposed to solve sub-models