142 research outputs found
A simulation approach to determine the probability of demand during lead-time when demand distributed normal and lead-time distributed gamma
Globalization and advances in information and production technologies make inventory management can be very difficult even for organizations with simple structures. The complexities of inventory management increase in multi-stage networks, where inventory appears in multiple tiers of locations. Due to massive practical applications in the reality of the world, an efficient inventory system policy whether single location or multi-stage location will avoid falling into overstock inventory or under stock inventory. However, the optimality of inventory and allocation policies in a supply chain is still unknown for most types of multi-stage systems. Hence, this paper aims to determine the probability distribution function of demand during lead-time by using a simulation model when the demand distributed normal and the lead-time distributed gamma. The simulation model showed a new probability distribution function of demand during lead-time in the considered inventory system, which is, Generalized Gamma distribution with 4 parameters. This probability distribution function makes the mathematical expression more difficult to build the inventory model especially in multistage or multi-echelon inventory model
A Simulation Approach to Determine the Probability of Demand during Lead-Time When Demand Distributed Normal and Lead-Time Distributed Gamma
Globalization and advances in information and production technologies make inventory management can be very difficult even for organizations with simple structures. The complexities of inventory management increase in multi-stage networks, where inventory appears in multiple tiers of locations. Due to massive practical applications in the reality of the world, an efficient inventory system policy whether single location or multi-stage location will avoid falling into overstock inventory or under stock inventory. However, the optimality of inventory and allocation policies in a supply chain is still unknown for most types of multi-stage systems. Hence, this paper aims to determine the probability distribution function of demand during lead-time by using a simulation model when the demand distributed normal and the lead-time distributed gamma. The simulation model showed a new probability distribution function of demand during lead-time in the considered inventory system, which is, Generalized Gamma distribution with 4 parameters. This probability distribution function makes the mathematical expression more difficult to build the inventory model especially in multistage or multi-echelon inventory model
An enhanced approximation mathematical model inventorying items in a multi-echelon system under a continuous review policy with probabilistic demand and lead-time
An inventory system attempts to balance between overstock and understock to reduce the total cost and achieve customer demand in a timely manner. The
inventory system is like a hidden entity in a supply chain, where a large complete network synchronizes a series of interrelated processes for a manufacturer, in order to transform raw materials into final products and distribute them to customers. The optimality of inventory and allocation policies in a supply chain for a cement
industry is still unknown for many types of multi-echelon inventory systems. In multi-echelon networks, complexity exists when the inventory issues appear in multiple tiers and whose performances are significantly affected by the demand and lead-time. Hence, the objective of this research is to develop an enhanced approximation mathematical model in a multi-echelon inventory system under a continuous review policy subject to probabilistic demand and lead-time. The probability distribution function of demand during lead-time is established by developing a new Simulation Model of Demand During Lead-Time (SMDDL) using simulation procedures. The model is able to forecast future demand and demand during lead-time. The obtained demand during lead-time is used to develop a Serial
Multi-echelon Inventory (SMEI) model by deriving the inventory cost function to compute performance measures of the cement inventory system. Based on the performance measures, a modified distribution multi-echelon inventory (DMEI) model with the First Come First Serve (FCFS) rule (DMEI-FCFS) is derived to determine the best expected waiting time and expected number of retailers in the system based on a mean arrival rate and a mean service rate. This research established five new distribution functions for the demand during lead-time. The
distribution functions improve the performance measures, which contribute in reducing the expected waiting time in the system. Overall, the approximation model provides accurate time span to overcome shortage of cement inventory, which in turn fulfil customer satisfaction
A Spreadsheet Model that Estimates the Impact of Reduced Distribution Time on Inventory Investment Savings: What is a Day Taken out of the Pipeline Worth in Inventory?
In most of the literature dealing with inventory problems, either with a deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable that is not subject to control. But in many practical situations, lead time can be reduced by an extra crashing cost; in other words, it is controllable. This study proposes a repeatable spreadsheet optimization model that estimates the impact of reduced replenishment lead time on inventory investment savings at forward and strategic locations to motivate decision makers to support enterprise-wide distribution process improvement. The study provides users with a means of automatically calculating inventory control parameters such as safety stocks and reorder points, and automatically estimating the savings caused by lead time mean or variability reduction. A trade-off analysis can be done to determine whether reducing lead time would override the lead time crashing cost. First, the model finds the optimal safety factor of an item based on a fill rate goal using Excel Solver. Then, Excel\u27s VBA automates the process of finding safety factors for other items before and after lead time reduction. Finally, the model is applied to three different supply support activities to illustrate its superior features, which include allowing the user to change and upgrade it for future research
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Optimal control methodologies for the optimisation of maintenance scheduling and production in processes using decaying catalysts
In this thesis, optimal control methodologies are developed for solving problems involving the optimisation of maintenance scheduling and production in processes using decaying catalysts. Previously, such problems were solved using a category of methods which involve making decisions of discrete as well as continuous nature, called mixed-integer optimisation techniques. However, these techniques are combinatorial in nature and can solve differential equations only by approximations as collections of steady state equality constraints, and such features can cause these techniques difficulties in obtaining optimal and accurate solutions for these problems. The goal behind developing optimal control methodologies is to effectively solve these problems while overcoming the drawbacks that mixed-integer optimisation techniques face or would face in solving these problems.
First, an optimal control methodology is developed to optimise maintenance scheduling and production in a process containing a reactor using decaying catalysts. This methodology involves using a multistage mixed-integer optimal control problem (MSMIOCP) formulation and obtaining solutions as a standard nonlinear optimisation problem, without using mixed-integer optimisation techniques. Two different solution implementations are required, each which has its own relative advantages. The methodology using the second procedure is particularly successful in effectively obtaining solutions within the stipulated tolerances. Further, the methodology possesses features of robustness because it enables a relatively small problem size, reliability because it solves differential equations using state-of-the-art integrators, and efficiency because it is not combinatorial in nature. These features indicate the methodology’s success in overcoming the drawbacks of using mixed-integer optimisation techniques to solve this problem.
Next, the abovementioned methodology is extended to form an optimal control methodology to optimise maintenance scheduling and production in a process containing parallel lines of reactors using decaying catalysts. This methodology, when applied to a case study of such a process, is also able to effectively obtain solutions within the stipulated tolerances. Further, the solutions obtained, once again, possess features of robustness, reliability and efficiency, which indicate that the methodology can overcome the drawbacks that mixed-integer optimisation techniques would face, if used to solve such problems.
And lastly, an optimal control methodology is developed for considering uncertainties in kinetic parameters in the optimisation of maintenance scheduling and production in a process containing a reactor using decaying catalysts. The methodology involves using a multiple scenario approach to consider parametric uncertainties and formulating a stochastic MSMIOCP, which is solved as a standard nonlinear optimisation problem as per the previously developed procedure. The results obtained provide insights into the effects of parametric uncertainties and the number of scenarios generated on the optimal operations, and indicate that the methodology is capable of solving this problem. Further, the robust, reliable and efficient nature of the results obtained suggest that the methodology can overcome the disadvantages that mixed-integer methods would introduce in the conventional methodologies, if such methodologies are used to solve such problems.Cambridge India Ramanujan Scholarship awarded by the Cambridge Trust and the Science and Engineering Research Board of Indi
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An optimal control approach to scheduling and production in a process using decaying catalysts
Models and algorithms for deterministic and robust discrete time/cost trade-off problems
Ankara : The Department of Management, Bilkent University, 2008.Thesis (Ph.D.) -- Bilkent University, 2008.Includes bibliographical references leaves 136-145Projects are subject to various sources of uncertainties that often negatively
impact activity durations and costs. Therefore, it is of crucial importance to develop
effective approaches to generate robust project schedules that are less vulnerable to
disruptions caused by uncontrollable factors. This dissertation concentrates on robust
scheduling in project environments; specifically, we address the discrete time/cost
trade-off problem (DTCTP).
Firstly, Benders Decomposition based exact algorithms to solve the deadline
and the budget versions of the deterministic DTCTP of realistic sizes are proposed.
We have included several features to accelerate the convergence and solve large
instances to optimality. Secondly, we incorporate uncertainty in activity costs. We
formulate robust DTCTP using three alternative models. We develop exact and
heuristic algorithms to solve the robust models in which uncertainty is modeled via
interval costs. The main contribution is the incorporation of uncertainty into a
practically relevant project scheduling problem and developing problem specific
solution approaches. To the best of our knowledge, this research is the first
application of robust optimization to DTCTP.
Finally, we introduce some surrogate measures that aim at providing an
accurate estimate of the schedule robustness. The pertinence of proposed measures is
assessed through computational experiments. Using the insight revealed by the
computational study, we propose a two-stage robust scheduling algorithm.
Furthermore, we provide evidence that the proposed approach can be extended to
solve a scheduling problem with tardiness penalties and earliness rewards.Hazır, ÖncüPh.D
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