CLSP: Real Life Applications and Motivation to Study Lot Sizing Problems in Process Industries

2019 ◽  
pp. 33-45
Author(s):  
Ravi Ramya ◽  
Chandrasekharan Rajendran ◽  
Hans Ziegler ◽  
Sanjay Mohapatra ◽  
K. Ganesh
Keyword(s):  
Lot Sizing ◽  
Real Life ◽  
Process Industries

The multi resource leveling and materials procurement problem: an integrated approach

2020 ◽  
Vol 27 (9) ◽  
pp. 2135-2161
Author(s):  
Hessa Almatroushi ◽  
Moncer Hariga ◽  
Rami As'ad ◽  
AbdulRahman Al-Bar
Keyword(s):  
Renewable Resources ◽  
Project Scheduling ◽  
Lot Sizing ◽  
Real Life ◽  
Integrated Approach ◽  
Project Schedule ◽  
Sequential Approach ◽  
Content Type ◽  
Ordering Policy ◽  
Solution Methods

PurposeThis paper proposes an integrated approach that seeks to jointly optimize project scheduling and material lot sizing decisions for time-constrained project scheduling problems.Design/methodology/approachA mixed integer linear programming model is devised, which utilizes the splitting of noncritical activities as a mean toward leveling the renewable resources. The developed model minimizes renewable resources leveling costs along with consumable resources related costs, and it is solved using IBM ILOG CPLEX optimization package. A hybrid metaheuristic procedure is also proposed to efficiently solve the model for larger projects with complex networks structure.FindingsThe results confirmed the significance of the integrated approach as both the project schedule and the material ordering policy turned out to be different once compared to the sequential approach under same parameter settings. Furthermore, the integrated approach resulted in substantial total costs reduction for low values of the acquiring and releasing costs of the renewable resources. Computational experiments conducted over 240 test instances of various sizes, and complexities illustrate the efficiency of the proposed metaheuristic approach as it yields solutions that are on average 1.14% away from the optimal ones.Practical implicationsThis work highlights the necessity of having project managers address project scheduling and materials lot sizing decisions concurrently, rather than sequentially, to better level resources and minimize materials related costs. Significant cost savings were generated through the developed model despite the use of a small-scale example which illustrates the great potential that the integrated approach has in real life projects. For real life projects with complex network topology, practitioners are advised to make use of the developed metaheuristic procedure due to its superior time efficiency as compared to exact solution methods.Originality/valueThe sequential approach, wherein a project schedule is established first followed by allocating the needed resources, is proven to yield a nonoptimized project schedule and materials ordering policy, leading to an increase in the project's total cost. The integrated approach proposed hereafter optimizes both decisions at once ensuring the timely completion of the project at the least possible cost. The proposed metaheuristic approach provides a viable alternative to exact solution methods especially for larger projects.

A model for lot sizing and sequencing in process industries

1988 ◽  
Vol 26 (4) ◽  
pp. 647-674 ◽  
Author(s):  
V. L. SMITH-DANIELS ◽  
LARRY P. RITZMAN
Keyword(s):  
Lot Sizing ◽  
Process Industries

scholarly journals Discounted costs and obsolescence with the Joint replenishment problem

2022 ◽  
Vol 13 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Ricardo Afonso ◽  
Pedro Godinho ◽  
João Paulo Costa
Keyword(s):  
Lot Sizing ◽  
Real Life ◽  
Progressive Increase ◽  
Sensitivity Analyses ◽  
Joint Replenishment ◽  
Total Cost ◽  
Joint Replenishment Problem ◽  
Ordering Policy ◽  
The Impact ◽  
Item Types

Real life inventory lot sizing problems are frequently challenged with the need to order different types of items within the same batch. The Joint Replenishment Problem (JRP) addresses this setting of coordinated ordering by minimizing the total cost, composed of ordering (or setup) costs and holding costs, while satisfying the demand. The complexity of this problem increases when some or all item types are prone to obsolescence. In fact, the items may experience an abrupt decline in demand because they are no longer needed, due to rapid advancements in technology, going out of fashion, or ceasing to be economically viable. This article proposes an extension of the Joint Replenishment Problem (JRP) where the items may suddenly become obsolete at some time in the future. The model assumes constant demand and the items’ lifetimes follow independent negative exponential distributions. The optimization process considers the time value of money by using the expected discounted total cost as the minimization criterion. The proposed model was applied to some test cases, and sensitivity analyses were performed, in order to assess the impact of obsolescence on the ordering policy. The increase in the obsolescence risk, through the progressive increase of the obsolescence rates of the item types, determines smaller lot sizes on the ordering policy. The increase in the discount rate causes smaller quantities to be ordered as well.

A risk-based availability estimation using Markov method

2014 ◽  
Vol 31 (2) ◽  
pp. 106-128 ◽  
Author(s):  
Qadeer Ahmed ◽  
Faisal I. Khan ◽  
Syed A. Raza
Keyword(s):  
Markov Model ◽  
Block Diagram ◽  
Real Life ◽  
Vital Role ◽  
Gas Absorption ◽  
State Equations ◽  
Functional Block Diagram ◽  
Process Industries ◽  
Content Type ◽  
Efficient Planning

Purpose – Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues. Design/methodology/approach – In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization. Findings – A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed. Originality/value – A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

New Methods for Adaptation to Degeneration in Process Models for Process Industries

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Christer P Karlsson ◽  
Anders Avelin ◽  
Erik Dahlquist
Keyword(s):  
Process Parameters ◽  
Process Model ◽  
Real Life ◽  
Process Models ◽  
Raw Material ◽  
Diagnostics And Control ◽  
Process Industries ◽  
New Methods ◽  
On Line ◽  
And Control

The implementation of model-based control and diagnostics suffer strongly from the fact that models deteriorate as a function of process and sensor deterioration. Also, changes in the raw material (i.e. wood) may occur and often the process control is not addressing these variations in reality. It is thus vital for the model system to be robust in the sense that it is transparent and easy for the operator to maintain. Robustness is essential in many parts of the system, including measurement, process model validation, the ability of the model to adapt to changes in the process, optimization algorithms, and of course the model itself. In this paper, we first show three real-life applications of the utilization of models for diagnostics and control. Thereafter conditions for on-line adaptation of the models are discussed. The challenges when designing such a system are in achieving operator confidence, filtering of misleading measured data, adaptation of process parameters when the process parameters change, and combining validation of measurements and process models. These challenges are met by using a combination of physical and statistical models and methods based on them such as model predictive control (MPC) and parameter estimation. The model should be maintained by a qualified engineer who should be able to explain the system to the operator so that it is understood and confidence can be maintained.

Capacitated Lot Sizing Problems in Process Industries

2019 ◽  
Author(s):  
Ravi Ramya ◽  
Chandrasekharan Rajendran ◽  
Hans Ziegler ◽  
Sanjay Mohapatra ◽  
K. Ganesh
Keyword(s):  
Lot Sizing ◽  
Process Industries ◽  
Capacitated Lot Sizing

scholarly journals A Heuristic Solution of Multi-Item Single Level Capacitated Dynamic Lot-Sizing Problem with Setup Time

2011 ◽  
Vol 264-265 ◽  
pp. 1794-1801
Author(s):  
Sultana Parveen ◽  
Md. Ahsan Akthar Hasin
Keyword(s):  
Lot Sizing ◽  
Research Work ◽  
Real Life ◽  
Optimal Solution ◽  
Setup Time ◽  
Heuristic Solution ◽  
Single Level ◽  
Solution Algorithms ◽  
Lot Sizing Problem ◽  
Dynamic Lot Sizing

The multi-item single level capacitated dynamic lot-sizing problem consists of scheduling N items over a horizon of T periods. The objective is to minimize the sum of setup and inventory holding costs over the horizon subject to a constraint on total capacity in each period. No backlogging is allowed. Only one machine is available with a fixed capacity in each period. In case of a single item production, an optimal solution algorithm exists. But for multi-item problems, optimal solution algorithms are not available. It has been proved that even the two-item problem with constant capacity is NP-hard, that is, it is in a class of problems that are extremely difficult to solve in a reasonable amount of time. This has called for searching good heuristic solutions. For a multi-item problem, it would be more realistic to consider the setup time, since switching the machine from one item to another would require a setup time. This setup time would be independent of item sequences and this could be a very important parameter from practical point of view. The current research work has been directed toward the development of a model for multiitem problem considering this parameter. Based on the model a program has been executed and feasible solutions with some real life data have been obtained.

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