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363. Decision trees for supervised multi-criteria inventory classification
Francesco Lolli1, Alessio Ishizaka2, Rita Gamberini1, Elia Balugani1, Bianca Rimini1
1University of Modena and Reggio Emilia, Italy; 2Centre of Operations Research and Logistics, Portsmouth Business School, University of Portsmouth, Richmond Building, Portland Street, Portsmouth PO1 3DE, United Kingdom
A multi-criteria inventory classification (MCIC) approach based on supervised classifiers (i.e. decision trees and random forests) is proposed, whose training is performed on a sample of items that has been previously classified by exhaustively simulating a predefined inventory control system. The goal is to classify automatically the whole set of items, in line with the fourth industrial revolution challenges of increased integration of ICT into production management. A case study referring to intermittent demand patterns has been used for validating our proposal, and a comparison with a recent unsupervised MCIC approach has shown promising results.
338. An efficient order-picking route planning based on a fuzzy set method with a multiple-aisle in a distribution center
Teng-Sheng Su, Ming-Hon Hwang
Chaoyang University of Technology, Taiwan
In this paper, we propose a methodology based on the fuzzy set theory to solve the order-picking route planning problem with a multiple-aisle in a distribution center. Unlike traditional route planning strategies that only cope with quantitative data, fuzzy set-based methods provide proper mechanism for expressing planners’ linguistic terms to determine the level of factors that affect the order-picking route planning. A proposed order-picking route planning procedure that applies the fuzzy logic and the fuzzy clustering algorithm is presented here. The objective aimed to achieve is to reduce the traveling distance and time of order picking on the dynamic and elaborate order fulfillment operations. An example is given to illustrate the proposed route planning procedure. It is our hope that the proposed fuzzy approaches from this study can assist order pickers to deal with both quantitative and linguistic factors in improving the overall performance of their order-picking operations.
167. Real-Time Dispenser Replenishment Robust Optimization Based on Receding Horizon Control
Husam Dauod, Haifeng Wang, Nourma Khader, Sang Won Yoon, Krishnaswami Srihari
State University of New York at Binghamton, United States of America
This paper presents a real-time robust optimization approach to enhance drug dispenser replenishment planning in central fill pharmacy (CFP) systems. Dispenser replenishment is a critical process that influences the efficiency of CFP automated operations. However, dispenser replenishment planning depends on various stochastic variables such as demand, counting speed, and medication size. These variables increase the complexity of inventory planning, especially when the demand volume is high. The objective of this research is to find a real-time dispenser replenishment strategy by applying robust optimization and receding horizon control approaches. A mixed integer programming model is proposed to minimize the total replenishment process cost, which includes the costs of inventory holding, shortage, device setup, and operation. Uncertainties in demand and process operation times are captured using box uncertainty sets to enhance the model robustness. A receding horizon control mechanism is applied to divide the practical situation into separate time windows to reduce the computational burden and enhance the solution’s quality. The model is tested based on actual CFP data. The impacts of uncertainty and receding horizon control settings on total operation cost are investigated and discussed. The results indicate that the proposed model not only gives robust recommendations for dispenser replenishment planning, but also enhances the efficiency of the real-time decision making process.