Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure

Edis, Emrah B.; Araz, Özlem Uzun; Eski, Özgür; Edis, Rahime Sancar

doi:10.1007/s11750-022-00625-5

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Therefore, to improve the computational performance of the SLAUPR, the main contribution of this research is the development of mathematical reformulations considering valid inequalities and single commodity flow constraints for sub-tours elimination, as well as the design of an adaptive multi-start heuristic (AMH) as an approximation method to solve real-size or large instances in a short computation time. The AMH is a relatively simple heuristic based on articles such as [44][45][46]. It consists of two main phases, construction and improvement, which require few parameters to be tuned, and it performs well on many combinatorial optimization problems [47,48].…”

Section: Related Workmentioning

confidence: 99%

Solving Location Assignment and Order Picker-Routing Problems in Warehouse Management

Bolaños-Zuñiga,

Salazar-Aguilar,

Saucedo-Martínez

2023

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One of the critical warehousing processes is the order-picking process. This activity consists of retrieving items from their storage locations to fulfill the demand specified in the pick lists. Therefore, the storage location assignment affects the picking time and, consequently, reduces the operating costs of the warehouse. This work presents two alternative mixed-integer linear models and an adaptive multi-start heuristic (AMH) for solving the integrated storage location and picker-routing problem. The problem considers a warehouse with a general layout and precedence constraints for picking according to the products weight. Experimental work confirms the efficiency of the proposed reformulations since we found out a total of 334 tested instances and optimal solutions for 51 new cases and 62 new feasible solutions. The proposed AMH improved more than 29% of the best-known solutions and required an average execution time of 117 s. Consequently, our proposed algorithm is an attractive decision-making tool to achieve efficiency when solving practical situations in a warehouse.

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