Abstract
In this paper, a tactical Production-Distribution Planning (PDP) has been handled in a fuzzy and stochastic environment for supply chain systems (SCS) which has four echelons (suppliers, plants, warehouses, retailers) with multi-products, multi-transport paths, and multi-time periods. The mathematical model of fuzzy stochastic PDP is a NP-hard problem for large SCS because of the binary variables which determine the transportation paths between echelons of the SCS and cannot be solved by optimization packages. In this study, therefore, two new meta-heuristic algorithms have been developed for solving fuzzy stochastic PDP: Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The proposed meta-heuristic algorithms are designed for route optimization in PDP and integrated with the GAMS optimization package in order to solve the remaining mathematical model which determines the other decisions in SCS, such as procurement decisions, production decisions, etc. The solution procedure in the literature has been extended by aggregating proposed meta-heuristic algorithms. The ACO and GA algorithms have been performed for test problems which are randomly generated. The results of the test problem showed that the both ACO and GA are capable to solve the NP-hard PDP for a big size SCS. However, GA produce better solutions than the ACO.
Highlights
In nowadays, as a result of globalization and technological developments, products have become more complex structure; the numbers and varieties of the components in products have increased, the number of suppliers has enlarged, and production systems have been transformed into a more complex system to be flexible
We considered a Production-Distribution Planning (PDP) for big size supply chain systems (SCS) by using centralized approach at tactical decision level in a fuzzy stochastic environment
A fuzzy stochastic PDP has been considered for an SCS which includes four echelons
Summary
As a result of globalization and technological developments, products have become more complex structure; the numbers and varieties of the components in products have increased, the number of suppliers has enlarged, and production systems have been transformed into a more complex system to be flexible. The number of echelons, which represents the components in SCS such as suppliers, plants, warehouses, and retailers, the number of transportation path between components, number of products and number of time periods affect the problem size and complexity. Bashiri and et al [21], and Raa and et al [22] handled multi products, plants, warehouses, end-users, time periods and single transport path models. Sakalli [1] proposed multi suppliers, products, plants, warehouses, retailers, transport paths, and time periods model. Khalifehzadeh and et al [46] developed a multi-objective mixed integer mathematical model for PDP in a fuzzy environment and proposed two meta-heuristic algorithms based on GA and Concessive Variable Neighborhood Search (CVNS).
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