Truck transportation scheduling in multi cross-dock systems with a soft time window with considering to uncertainty in time parameters

Document Type : Original Article

Authors

1 Department of industrial management, Faculty of management and accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Ph.d student In Industrial Management, , Faculty of Management and Accounting, Department of Industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

3 Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University

4 Assistant Prof. Faculty of Management and Accounting, Department of Industrial management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

Logistics strategy is an important advantage for supply chain management operations, which requires centralized planning of operations to reduce time and timely delivery of the product to increase the level of customer satisfaction. Cross-dock is an efficient method to control inventory flow that is essential in supply chain management. The other objectives of the cross dock are inventory reduction, increased levels of customer responsiveness and better control of the distribution operation. This paper focuses on the optimization of the transportation scheduling and the planning of the movement inbound and outbound trucks with a soft window, where time parameters are considered uncertain. Therefore, a mathematical model is presented with the goal of minimizing the total time of operation in the supply chain and using the possibility Linear programming model converts the mathematical model into a definite mathematical model. Since the article model is zero and one linear programming type of the integer and belongs to NP-hard issues, the time to solve them increases with increasing problem dimensions. Therefore, genetic algorithm and Simulated Annealing algorithm are used to find optimal solution problems. The parameters used in meta_ heuristic algorithms were determined by Taguchi method with mini-tab software and their optimal values were extracted. Then, according to constant parameters including the number of suppliers, customers, cross-dock, inbound and outbound trucks, and product types, sample problems were generated at three small, meddle and large production levels, and at each level, seven problem samples were generated and total of twenty one issues were solved.

Keywords

References

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