scholarly journals Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

2017 ◽  
Vol 5 (2) ◽  
pp. 85
Author(s):  
Kaveh Khalili-Damghani ◽  
Ayda Shahrokh ◽  
Alireza Pakgohar
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Financial Risk ◽  
Goal Attainment ◽  
Experimental Production ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Multi Objective ◽  
Proposed Model ◽  
Human Resource Training

<p>In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.</p>

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.

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