The new TOPCO hybrid algorithm to solve multi-objective optimisation problems: the integrated stochastic problem of production-distribution planning in the supply chain

2021 ◽  
Vol 6 (3) ◽  
pp. 143
Author(s):  
Elham Shadkam ◽  
Shahrzad Khajooei ◽  
Reza Rajabi
Keyword(s):  
Supply Chain ◽  
Hybrid Algorithm ◽  
Stochastic Problem ◽  
Distribution Planning ◽  
Multi Objective ◽  
Production Distribution

scholarly journals A Multiobjective Stochastic Production-Distribution Planning Problem in an Uncertain Environment Considering Risk and Workers Productivity

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
S. M. J. Mirzapour Al-e-Hashem ◽  
A. Baboli ◽  
S. J. Sadjadi ◽  
M. B. Aryanezhad
Keyword(s):  
Supply Chain ◽  
Hybrid Algorithm ◽  
Planning Horizon ◽  
Planning Problem ◽  
Distribution Planning ◽  
Total Cost ◽  
Multi Objective ◽  
Production Distribution ◽  
Demand Fluctuations ◽  
Cost Parameters

A multi-objective two stage stochastic programming model is proposed to deal with a multi-period multi-product multi-site production-distribution planning problem for a midterm planning horizon. The presented model involves majority of supply chain cost parameters such as transportation cost, inventory holding cost, shortage cost, production cost. Moreover some respects as lead time, outsourcing, employment, dismissal, workers productivity and training are considered. Due to the uncertain nature of the supply chain, it is assumed that cost parameters and demand fluctuations are random variables and follow from a pre-defined probability distribution. To develop a robust stochastic model, an additional objective functions is added to the traditional production-distribution-planning problem. So, our multi-objective model includes (i) the minimization of the expected total cost of supply chain, (ii) the minimization of the variance of the total cost of supply chain and (iii) the maximization of the workers productivity through training courses that could be held during the planning horizon. Then, the proposed model is solved applying a hybrid algorithm that is a combination of Monte Carlo sampling method, modified -constraint method and L-shaped method. Finally, a numerical example is solved to demonstrate the validity of the model as well as the efficiency of the hybrid algorithm.

scholarly journals A multi-objective particle swarm optimization for production-distribution planning in supply chain network

2012 ◽  
Vol 2 (2) ◽  
pp. 603-614 ◽  
Author(s):  
alireza Pourrousta ◽  
Saleh dehbari ◽  
Reza Tavakkoli-Moghaddam ◽  
Mohsen sadegh amalnik
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Particle Swarm ◽  
Supply Chain Network ◽  
Swarm Optimization ◽  
Distribution Planning ◽  
Multi Objective ◽  
Production Distribution

An integrated production-distribution planning with a routing problem and transportation cost discount in a supply chain

2019 ◽  
Vol 39 (5) ◽  
pp. 783-802 ◽  
Author(s):  
Behzad Karimi ◽  
Mahsa Ghare Hassanlu ◽  
Amir Hossein Niknamfar
Keyword(s):  
Supply Chain ◽  
Heuristic Algorithms ◽  
Planning Problem ◽  
Distribution Planning ◽  
Content Type ◽  
Integrated Production ◽  
Multi Objective ◽  
Proposed Model ◽  
Production Distribution ◽  
Problem Instances

Purpose The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today’s global marketplace. In this regard, this paper aims to deal with an integrated production-distribution planning problem in deterministic, multi-product and multi-echelon supply chain network. The bi-objective mixed-integer linear programming model is constructed to minimize not only the total transportation costs but also the total delivery time of supply chain, subject to satisfying retailer demands and capacity constraints where quantity discount on transportation costs, fixed cost associated with transportation vehicles usage and routing decisions have been included in the model. Design/methodology/approach As the proposed mathematical model is NP-hard and that finding an optimum solution in polynomial time is not reasonable, two multi-objective meta-heuristic algorithms, namely, non-dominated sorting genetic algorithm II (NSGAII) and multi-objective imperialist competitive algorithm (MOICA) are designed to obtain near optimal solutions for real-sized problems in reasonable computational times. The Taguchi method is then used to adjust the parameters of the developed algorithms. Finally, the applicability of the proposed model and the performance of the solution methodologies in comparison with each other are demonstrated for a set of randomly generated problem instances. Findings The practicality and applicability of the proposed model and the efficiency and efficacy of the developed solution methodologies were illustrated through a set of randomly generated real-sized problem instances. Result. In terms of two measures, the objective function value and the computational time were required to get solutions. Originality/value The main contribution of the present work was addressing an integrated production-distribution planning problem in a broader view, by proposing a closer to reality mathematical formulation which considers some real-world constraints simultaneously and accompanied by efficient multi-objective meta-heuristic algorithms to provide effective solutions for practical problem sizes.

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