scholarly journals A Multiobjective Fuzzy Aggregate Production Planning Model Considering Real Capacity and Quality of Products

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Najmeh Madadi ◽  
Kuan Yew Wong
Keyword(s):  
Production Planning ◽  
Customer Service ◽  
Optimal Solution ◽  
Service Level ◽  
Aggregate Production Planning ◽  
Production Lines ◽  
Aggregate Production ◽  
Customer Service Level ◽  
Quality Of Products ◽  
Parts Manufacturing

In this study, an attempt has been made to develop a multiobjective fuzzy aggregate production planning (APP) model that best serves those companies whose aim is to have the best utilization of their resources in an uncertain environment while trying to keep an acceptable degree of quality and customer service level simultaneously. In addition, the study takes into account the performance and availability of production lines. To provide the optimal solution to the proposed model, first it was converted to an equivalent crisp multiobjective model and then goal programming was applied to the converted model. At the final step, the IBM ILOG CPLEX Optimization Studio software was used to obtain the final result based on the data collected from an automotive parts manufacturing company. The comparison of results obtained from solving the model with and without considering the performance and availability of production lines, revealed the significant importance of these two factors in developing a real and practical aggregate production plan.

Dual Approach to the Harmonized Model between Inventory Reduction and Heijunka (Production Leveling) based on the Minimum Average-energy Principle

2019 ◽  
Vol 01 (02) ◽  
Author(s):  
Tsuyoshi Kurihara ◽  
Takaaki Kawanaka ◽  
Hiroshi Yamashita
Keyword(s):  
Production Planning ◽  
Inventory Management ◽  
Average Energy ◽  
Optimal Solution ◽  
Exponential Smoothing ◽  
Aggregate Production Planning ◽  
Energy Principle ◽  
Dual Approach ◽  
Aggregate Production ◽  
Proposed Model

A major issue in manufacturing is the balance between inventory reduction and heijunka (i.e., production leveling). To address this issue in aggregate production planning, linear programming models that consider many factors and use “exponential smoothing” as an approximate leveling method have been mainly studied. However, this methodology has problems that may limit its use as an optimal solution approximate method, and impair the timeliness required for aggregate production planning by the complexity of these models. To solve this issue, we have been developing harmonized models to balance between lowering the inventory management energy and increasing the heijunka entropy, based on demand and inventory quantities as simple optimization models. In this study, we develop a dual approach to the previously proposed model to maximize the heijunka entropy and propose a new model to minimize the inventory management energy based on the “minimum average-energy principle.” We show that the proposed model’s inventory state is lower than that of traditional exponential smoothing through numerical experiments. This study, therefore, theoretically enables a new optimal solution to the harmonized (balancing) problem, based on the concept of entropy and energy, and practically enables aggregate production planning in a timely and simple manner.

scholarly journals Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Agung Mustika Rizki ◽  
Afina Lina Nurlaili
Keyword(s):  
Particle Swarm Optimization ◽  
Production Planning ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Efficient Solutions ◽  
Production Costs ◽  
Aggregate Production Planning ◽  
Swarm Optimization ◽  
Aggregate Production

In the industrial world, companies need to manage their production areas well. One way is to implement aggregate production planning. The goal is that the production costs incurred by the company can be controlled properly. However, production planning cannot be formulated quickly. The problem is more complicated if the company has several production locations. The difference in location also affects the production references and standards applied in each location. Based on these problems, the authors propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the problem of aggregate production planning in order to obtain the optimal solution for each production location. As a result, the algorithm proposed by the author can produce optimal and efficient solutions for 6 production sites. This is evidenced by the relatively short time required compared to the previous planning by the company.

scholarly journals Integrating Mathematical and Simulation Approach for Optimizing Production and Distribution Planning With Lateral Transshipment in a Supply Chain

2022 ◽  
Vol 15 (1) ◽  
pp. 0-0
Keyword(s):  
Supply Chain ◽  
Customer Service ◽  
Optimal Solution ◽  
Integrated Approach ◽  
Service Level ◽  
Computational Time ◽  
Customer Service Level ◽  
Simulation Based ◽  
Production And Distribution ◽  
Lateral Transshipment

Supply chain planning aims to maximize the chain's profit and find an effective way to integrate production and distribution. A mathematical and simulation-based optimizations are two common disciplines in which this study integrates both of them together to consolidate their advantages. A mathematical model is formulated to find an optimal production-distribution plan. Then, the result is fed into a simulation model operating under uncertainty to verify the feasibility of the plan. Our integrated approach tries to find a feasible plan that satisfies both required customer service level and makespan limitation where safety stock is used to hedge against uncertainties, and lateral transshipment is used for emergency measures against excessive fluctuation of customer demand. A case study that optimizes the profit of an entire chain is used to demonstrate the algorithm. The outcomes of the study show that our proposed approach can yield feasible results (with near or even optimal solution) with much faster computational time as compared to the traditional simulation-based optimization.

scholarly journals An Uncertain APP Model with Allowed Stockout and Service Level Constraint for Vegetables

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2332
Author(s):  
Yufu Ning ◽  
Na Pang ◽  
Shuai Wang ◽  
Xiumei Chen
Keyword(s):  
Production Planning ◽  
Equivalent Form ◽  
Service Level ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Hybrid Intelligent Algorithm ◽  
Uncertain Variables ◽  
Optimal Output ◽  
Deterioration Rate ◽  
Proposed Model

Volatile markets and uncertain deterioration rate make it extremely difficult for manufacturers to make the quantity of saleable vegetables just meet the fluctuating demands, which will lead to inevitable out of stock or over production. Aggregate production planning (APP) is to find the optimal yield of vegetables, shortage and overstock symmetry, are not conducive to the final benefit.The essence of aggregate production planning is to deal with the symmetrical relation between shortage and overproduction. In order to reduce the adverse effects caused by shortage, we regard the service level as an important constraint to meet the customer demand and ensure the market share. So an uncertain aggregate production planning model for vegetables under condition of allowed stockout and considering service level constraint is constructed, whose objective is to find the optimal output while minimizing the expected total cost. Moreover, two methods are proposed in different cases to solve the model. A crisp equivalent form can be transformed when uncertain variables obey linear uncertain distributions and for general case, a hybrid intelligent algorithm integrating the 99-method and genetic algorithm is employed. Finally, two numerical examples are carried out to illustrate the effectiveness of the proposed model.

What should be lean buffer threshold for the forward-reserve warehouse?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bhavin Shah
Keyword(s):  
Mathematical Model ◽  
Customer Service ◽  
Value Added ◽  
Service Level ◽  
Buffer Size ◽  
Value Stream Mapping ◽  
Content Type ◽  
Customer Service Level ◽  
The Impact ◽  
Just Right

PurposeThe assorted piece-wise retail orders in a cosmetics warehouse are fulfilled through a separate fast-picking area called Forward Buffer (FB). This study determines “just-right” size of FB to ensure desired Customer Service Level (CSL) at least storage wastages. It also investigates the impact of FB capacity and demand variations on FB leanness.Design/methodology/approachA Value Stream Mapping (VSM) tool is applied to analyse the warehouse activities and mathematical model is implemented in MATLAB to quantify the leanness at desired CSL. A comprehensive framework is developed to determine lean FB buffer size for a Retail Distribution Centre (RDC) of a cosmetics industry.FindingsThe CSL increases monotonically; however, the results concerning spent efforts towards CSL improvement gets diminished with raised demand variances. The desired CSL can be achieved at least FB capacity and fewer Storage Waste (SW) as it shifts towards more lean system regime. It is not possible to improve Value Added (VA) time beyond certain constraints and therefore, it is recommended to reduce Non-Value Added (NVA) order processing activities to improve leanness.Research limitations/implicationsThis study determines “just-right” capacity and investigates the impact of buffer and demand variations on leanness. It helps managers to analyse warehouse processes and design customized distribution policies in food, beverage and retail grocery warehouse.Practical implicationsProposed buffering model offers customized strategies beyond pre-set CSL by varying it dynamically to reduce wastages. The mathematical model deriving lean sizing and mitigation guidelines are constructive development for managers.Originality/valueThis research provides an inventive approach of VSM model and Mathematical algorithm endorsing lean thinking to design effective buffering policies in a forward warehouse.

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