Pull-based supply chain allocation for multi-product in multi-echelon distribution system

In today\u27s highly competitive market environment, only companies with a highly efficient supply chain management, which integrates all decisions in various levels of planning and operations, can survive. These decisions must be coordinated and under the same goal, which is to minimize the total systemwide costs of the firm while products are manufactured and distributed to end-customers or retailers. In this study, the focus is on a pull-based supply chain, customer demand driven, multiple products and multiple echelon distribution system consisting of m manufacturing centers, n distribution centers, and p retailers or customers. The objectives of this study can be categorized into two parts. The first objective is to present a general framework of the design and configuration of the supply chain network at strategic and tactical planning levels in a single-product and multi-product multi-echelon supply chain systems. The problems deal with determining the appropriate number, location, and size of each manufacturing facility and distribution center/warehouse that should be used within the logistics network. The second objective of the research is to present a methodology for using a pull-based supply chain system both for a single-product system and multi-product system at the operational planning level. The problems deal with determining which products customers will receive from each available manufacturing facility and distribution center, what production quantities of the products should be manufactured by a particular manufacturing facility, and what quantities of each product and ways of shipment should be used from manufacturing facilities to distribution centers and to customers. Based on the nature of these large-scale mixed integer programming problems, decomposition heuristic algorithms based on relationships between primal and dual decompositions are developed. The mathematical models and heuristic algorithms are then demonstrated and evaluated on several sets of randomly generated problems. Although the heuristic algorithms do not guarantee optimum solutions, their results of the test problems suggest that the heuristics are effective in solving fairly large problems with reasonable computational time. Furthermore, they produce superior performances as compared to the other techniques that are tested

Reliable multi-product multi-vehicle multi-type link logistics network design: A hybrid heuristic algorithm

Abstract This paper considers the reliable multi-product multi-vehicle multi-type link logistics network design problem (RMLNDP) with system disruptions, which is concerned with facilities locating, transshipment links constructing, and also allocating them to the customers in order to satisfy their demand with minimum expected total cost (including locating costs, link constructing costs, and also expected transshipment costs in normal and disruption conditions). The motivating application of this class of problem is in multi-product, multi-vehicle, and multitype link logistics network design regarding to system disruptions simultaneously. In fact, the decision makers in this area are not only concerned with the facility locating costs, link constructing costs, and logistical costs of the system but also by focusing on the several system disruption states in order to be able to provide a reliable sustainable multi configuration logistic network system. All facility location plans, link construction plans and also link transshipment plans of demands in the problem must be efficiently determined while considering the several system disruptions. The problem is modeled as a mixed integer programming (MIP) model. Also, a hybrid heuristic, based on linear programming (LP) relaxation approach, is proposed. Computational experiments illustrate that the provided algorithm will be able to substantially outperform the proposed integer programming model in terms of both finding and verifying the efficient optimal (or near optimal) solution at a reasonable processing time

A computer graphics approach to logistics strategy modelling

This thesis describes the development and application of a decision support system for logistics strategy modelling. The decision support system that is developed enables the modelling of logistics systems at a strategic level for any country or area in the world. The model runs on IBM PC or compatible computers under DOS (disk operating system). The decision support system uses colour graphics to represent the different physical functions of a logistics system. The graphics of the system is machine independent. The model displays on the screen the map of the area or country which is being considered for logistic planning. The decision support system is hybrid in term of algorithm. It employs optimisation for allocation. The customers are allocated by building a network path from customer to the source points taking into consideration all the production and throughput constraints on factories, distribution depots and transshipment points. The system uses computer graphic visually interactive heuristics to find the best possible location for distribution depots and transshipment points. In a one depot system it gives the optimum solution but where more than one depot is involved, the optimum solution is not guaranteed. The developed model is a cost-driven model. It represents all the logistics system costs in their proper form. Its solution very much depends on the relationship between all the costs. The locations of depots and transshipment points depend on the relationship between inbound and outbound transportation costs. The model has been validated on real world problems, some of which are described here. The advantages of such a decision support system for the formulation of a problem are discussed. Also discussed is the contribution of such an approach at the validation and solution presentation stages

Location, inventory and testing decisions in closed-loop supply chains: a multimedia company

Our partnering firm is a Chinese manufacturer of multimedia products that needs guidance developing its imminent Closed-Loop Supply Chain (CLSC). To study this problem, we take into account location, inventory, and testing decisions in a CLSC setting with stochastic demands of new and time-sensitive returned products. Our analysis pays particular attention to the different roles assigned to the reverse Distribution Centers (DCs) and how each option affects the optimal CLSC design. The roles considered are collection and consolidation, additional testing tasks, and direct shipments with no reverse DCs. The problem concerning our partnering firm is formulated as a scenario-based chance-constrained mixed-integer program and it is reformulated to a conic quadratic mixed-integer program that can be solved efficiently via commercial optimization packages. The completeness of the model proposed allows us to develop a decision support tool for the firm and to offer several useful managerial insights. These insights are inferred from our computational experiments using data from the Chinese firm and a second data set based on the U.S. geography. Particularly interesting insights are related to how changes in the reverse flows can impact the forward supply chain and the inventory dynamics concerning the joint DCs.This research is partially supported by the National Natural Science Foundation of China under grants 71771135, 71371106 and 71332005

Optimal Decision Making for Capacitated Reverse Logistics Networks with Quality Variations

Increasing concerns about the environmental impact of production, product take-back laws and dwindling natural resources have heightened the need to address the impact of disposing end-of-life (EOL) products. To cope this challenge, manufacturers have integrated reverse logistics into their supply chain or chosen to outsource product recovery activities to third party firms. The uncertain quality of returns as well as uncertainty in return flow limit the effectiveness of planning, control and monitoring of reverse logistics networks. In addition, there are different recovery routes for each returned product such as reuse, repair, disassembling, remanufacturing and recycling. To determine the most profitable option for EOL product management, remanufacturers must consider the quality of returns and other limitations such as inventory size, demand and quantity of returns. The work in this dissertation addresses these pertinent aspects using two models that have been motivated by two remanufacturing facilities whereby there are uncertainties in the quality and quantity of return and capacitated inventories. In the first case, a disposition decision making model is developed for a remanufacturing process in which the inventory capacity of recoverable returns is limited and where there\u27s a constant demand to be met, for remanufactured products that meet a minimum quality threshold. It is assumed that the quality of returns is uncertain and remanufacturing cost is dependent on the quality grade. In this model, remanufacturing takes place when there is demand for remanufactured products. Accepted returns that meet the minimum quality threshold undergo the remanufacturing processes, and any unacceptable returns are salvaged. A continuous time Markov chain (CTMC) is presented as the modeling approach. The Matrix-Geometric solution methodology is applied to evaluate several key performance metrics for this system, to result in the optimal disposition policy. The numerical study shows an intricate trade-off between the acceptable quality threshold value and the recoverable product inventory capacity. Particularly, there are periodic system starvation whenever there is a mis-match between these two system metrics. In addition, the sensitivity analysis indicates that changes to the demand rate for remanufactured products necessitates the need to re-evaluate the existing system configuration. In the second case, a general framework is presented for a third party remanufacturer, where the remanufacturer has the alternative of salvaging EOL products and supplying parts to external suppliers, or remanufacture the disassembled parts to \u27as new\u27 conditions. The remanufacturing processes of reusable products and parts is studied in the context of other process variables such as the cost and demand of remanufactured products and parts. The goal of this model is to determine the return quality thresholds for a multi-product, multi-period remanufacturing setting. The problem is formulated as a mixed integer non-linear programming (MINLP) problem, which involves a discretization technique that turns the problem turns into a quadratic mixed integer programming (QMIP) problem. Finally, a numerical analysis using a personal computer (PC) remanufacturing facility data is used to test the extent to which the minimum acceptance quality threshold is dependent on the inventory level capacities of the EOL product management sites, varying operational costs and the upper bound of disposal rate

Constructive solution methodologies to the capacitated newsvendor problem and surrogate extension

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor\u27s overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, it becomes a constrained newsvendor problem. In the past few decades, many researchers have proposed solution methods to solve the newsvendor problem. The literature is first reviewed where the performance of each of existing model is examined and its contribution is reported. To add to these works, it is complemented through developing constructive solution methods and extending the existing published works by introducing the product substitution models which so far has not received sufficient attention despite its importance to supply chain management decisions. To illustrate this dissertation provides an easy-to-use approach that utilizes the known network flow problem or knapsack problem. Then, a polynomial in fashion algorithm is developed to solve it. Extensive numerical experiments are conducted to compare the performance of the proposed method and some existing ones. Results show that the proposed approach though approximates, yet, it simplifies the solution steps without sacrificing accuracy. Further, this dissertation addresses the important arena of product substitute models. These models deal with two perishable products, a primary product and a surrogate one. The primary product yields higher profit than the surrogate. If the demand of the primary exceeds the available quantity and there is excess amount of the surrogate, this excess quantity can be utilized to fulfill the shortage. The objective is to find the optimal lot sizes of both products, that minimize the total cost (alternatively, maximize the profit). Simulation is utilized to validate the developed model. Since the analytical solutions are difficult to obtain, Mathematical software is employed to find the optimal results. Numerical experiments are also conducted to analyze the behavior of the optimal results versus the governing parameters. The results show the contribution of surrogate approach to the overall performance of the policy. From a practical perspective, this dissertation introduces the applications of the proposed models and methods in different industries such as inventory management, grocery retailing, fashion sector and hotel reservation

Continuous time control of make-to-stock production systems

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Ph. D.) -- Bilkent University, 2010.Includes bibliographical references leaves 117-120.We consider the problem of production control and stock rationing in a make-tostock production system with multiple servers –parallel production channels--, and several customer classes that generate independent Poisson demands. At decision epochs, in conjunction with the stock allocation decision, the control specifies whether to increase the number of operational servers or not. Previously placed production orders cannot be cancelled. We both study the cases of exponential and Erlangian processing times and model the respective systems as M /M /s and M /Ek /s make-to-stock queues. We characterize properties of the optimal cost function, and of the optimal production and rationing policies. We show that the optimal production policy is a state-dependent base-stock policy, and the optimal rationing policy is of state-dependent threshold type. For the M /M /s model, we also prove that the optimal ordering policy transforms into a bang-bang type policy when we relax the model by allowing order cancellations. Another model with partial ordercancellation flexibility is provided to fill the gap between the no-flexibility and the full-flexibility models. Furthermore, we propose a dynamic rationing policy for the systems with uncapacitated replenishment channels, i.e., exogenous supply systems. Such systems can be modeled by letting s --the number of replenishment channels-- go to infinity. The proposed policy utilizes the information on the status of the outstanding replenishment orders. This work constitutes a significant extension of the literature in the area of control of make-to-stock queues, which considers only a single server. We consider an arbitrary number of servers that makes it possible to cover the spectrum of the cases from the single server to the infinite servers. Hence, our work achieves to analyze both the exogenous and endogenous supply leadtimes.Bulut, ÖnderPh.D

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Aerospace Manufacturing-Remanufacturing System Modeling and Optimization

In recent years, increasing environmental concerns, costs of raw materials, and stricter government regulations have resulted in companies striving to reduce their waste materials. An earlier approach adopted was the recycling of materials such as waste paper, glass and metals. However, recycled products typically lose a portion of their added values. Different waste reduction options such as direct reuse, repair, refurbishing, cannibalization, and remanufacturing were studied to overcome this drawback. Remanufacture recaptures the value added to materials when a product was first manufactured. In the aerospace industry, where safety and performance are the overriding concerns and repairs are highly regulated, it could be perceived that remanufacturing has minimal appeal. However, the very low design tolerance of manufactured components results in a high percentage of defects. Due to the high price of raw materials, remanufacturing and components saving through “transforming” could be applied in imperfect production systems to reduce the amount of scrap materials. In this thesis, a general model is first proposed for a closed-loop supply chain network which includes the following processes: repairs, remanufacturing and transforming of selected defective components and end-of-life products, and cannibalization. A mixed integer linear programming formulation is developed to investigate the effect of various factors on profit, inventory carrying cost, and number of scrap components. Uncertainty in demand and lead-time is one of the major issues in any manufacturing supply chain. Uncertainty is incorporated into an extended model through the scenario-analysis approach and outsourcing is considered as an option for remanufacturing of the customer owned components. Demand of final products is assumed to be deterministic. The defect rate of disassembled components, however, is considered to be variable which makes the demand for spares to be variable. The lead-time of in-house remanufacturing of the customer owned components is also considered to be variable. Sensitivity analysis is performed to investigate the effect of capacity, inventory carrying cost, outsourcing cost, lead-time, and defect rate variation on profit and amount of scraps. The inventory carrying cost variations have direct effect on the inventory turnover ratio. The maximum capacity of the outsourced company and process costs per unit have significant effect on the profitability. Maintaining a long-term relationship with third-party service providers, designing the components with a longer life cycle, and transforming and remanufacturing of defective components directly impact the profitability over the life cycle of a product