Abstract
In this article, we study the economic lot and delivery scheduling problem for a four-stage supply chain that includes suppliers, fabricators, assemblers, and retailers. All of the parameters such as demand rate are deterministic and production setup times are sequence-dependent. The common cycle time and integer multipliers policies are adapted as replenishment policies for synchronization throughout the supply chain. A new mixed integer nonlinear programming model is developed for both policies, the objective of which is the minimization of inventory, transportation, and production setup costs. We propose a new hybrid algorithm including a modified imperialist competitive algorithm which is purposed to the assimilation policy of imperialist competitive algorithm and teaching learning–based optimization which is added to improve local search. A hybrid modified imperialist competitive algorithm and teaching learning–based optimization is applied to find a near-optimum solution of mixed integer nonlinear programming in large-sized problems. The results denoted that our proposed algorithm can solve different size of problem in reasonable time. This procedure showed its efficiency in medium- and large-sized problems as compared to imperialist competitive algorithm, modified imperialist competitive algorithm, and other methods reported in the literature.
Highlights
The coordination of the chain members is one of the important objectives of supply chain since members of each stage have different and even opposite profit
We have focused on the coordination among the members of a four-stage supply chain
In the common cycle time policy, we propose modified imperialist competitive algorithm (MICA)– teaching learning–based optimization (TLBO) and it is compared with genetic algorithm (GA) relative to the solution time and solution quality
Summary
The coordination of the chain members is one of the important objectives of supply chain since members of each stage have different and even opposite profit. Torabi et al.[6] studied a two-stage supply chain including one supplier and one assembler and multiple items with the common cycle time They proposed a hybrid genetic algorithm (GA) for the solution of ELDSP. Nikandish et al.[7] examined a three-stage supply chain including one supplier and multiple assemblers and multiple retailers with common cycle time and presented the optimal solution for medium scale problems. For this type of linearization, by considering suppliers, fabricators, and assemblers stage, we add equations (41)–(46) to the model. We propose a hybrid algorithm including MICA and TLBO technique (MCIA–TLBO) to solve defined problem in the common cycle time and integer multipliers policy. In MICA-TLBO, the solution time is decreased by increasing power of each empire and improving the position of imperialist and colonies. As shown in this table, the integer multipliers policy outperforms the common cycle time policy in all instances
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