On the integrated production, inventory, and distribution routing problem

2006 ◽  
Vol 38 (11) ◽  
pp. 955-970 ◽  
Author(s):  
Lei Lei ◽  
Shuguang Liu ◽  
Andrzej Ruszczynski ◽  
Sunju Park
2022 ◽  
pp. 244-265
Author(s):  
Besma Zeddam ◽  
Fayçal Belkaid ◽  
Mohammed Bennekrouf

Production routing problem is one of the problems of the integrated planning that interests in optimizing simultaneously production, inventory, and distribution planning. This chapter has the purpose of developing two mono-objective models for the production-routing problem: one of them minimizes the total costs which is the classical objective while the other one minimizes the energy consumed by the production system. A bi-objective model is then proposed to combine the two objectives mentioned previously using LP-metric method. To solve big instances of the problem in reasonable time, an approximate approach is proposed using the rolling horizon-based fix and relax heuristic. Finally, computational results are presented to compare the solutions obtained by both approaches.


2019 ◽  
Vol 52 (13) ◽  
pp. 523-528 ◽  
Author(s):  
A. Ghasemkhani ◽  
R. Tavakkoli-Moghaddam ◽  
S. Shahnejat-Bushehri ◽  
S. Momen ◽  
H. Tavakkoli-Moghaddam

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Noor Hasnah Moin ◽  
Titi Yuliana

This paper proposes the use of scatter search metaheuristic to solve an integrated production, inventory, and distribution routing problem. The problem is based on a single production plant that produces a single product that is delivered toNgeographically dispersed customers by a set of homogenous fleet of vehicles. The objective is to construct a production plan and delivery schedule to minimize the total costs and ensuring each customer’s demand is met over the planning horizon. We assumed that excess production can be stored at the plant or at customer’s sites within some limits, but stockouts due to backordering or backlogging are not allowed. Further testing on a set of benchmark problems to assess the effectiveness of our method is also carried out. We compare our results to the existing metaheuristic algorithms proposed in the literature.


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