This study evaluates correspondence analysis (CA) as a ranking tool for multi-criteria decision-making (MCDM). The proposed Correspondence Analysis–Weighted Load-Distance (CA-WLD) method converts alternative–criterion distances in the CA space into a weighted decision score. In a Monte Carlo simulation of 20,242 contingency tables, nine established MCDM techniques agree closely, whereas CA-WLD yields distinct information that depends on the interaction of weighting with data structure; a max-min baseline shows that this is not merely non-compensation. A controlled perturbation analysis shows that CA-WLD withholds first place from a classical leader whose lead rests on volume or on above-competitor concentration, and endorses it once that concentration falls or its peak criterion is weighted sufficiently. Applied to a real site-selection problem (241 respondents), CA-WLD diagnoses the popularity bias of count-based methods. It is a complementary diagnostic tool, not a rival.
The COVID-19 pandemic, volatile market conditions, shorter lifecycles, and cost-effective goods confront the reconfigurable manufacturing system (RMS). The RMS needs Industry 4.0 and circular economy technologies to boost performance. However, present literature does not integrate these technology and methods. This paper proposes a framework for adopting these technologies and practices. The SWARA method determines relative weights of specified practices, Pythagorean fuzzy COCOSO ranks performance indicators based on expert opinion, along with sensitivity analysis. The important performance metrics in key criteria practices are production lead time, advanced technology usage, and machine utilization rate, according to the report. Waste reduction and production costs are also important to experts. Managers can use the framework to increase RMS performance and handle COVID-19. It enables practitioners use sophisticated technology and CE practices and evaluate their success using metrics.
This study introduces the Multi-League Referee Assignment Problem (MRAP), a practically motivated optimization problem arising in the scheduling of referees for non-professional football. MRAP accounts for multiple concurrent leagues with distinct scheduling constraints, qualification requirements, and fairness considerations. We formulate MRAP as an integer programming model and assess its performance through extensive numerical experiments on realistic synthetic data. The monolithic model becomes computationally infeasible for large instances. Therefore, we propose two heuristic approaches that decompose the problem temporally by weeks or structurally by leagues. These heuristics quickly produce high-quality solutions when referee availability is sufficient. Notably, shorter batches of weeks in temporal decomposition perform better, as longer batches create restrictive commitments. Sensitivity analyses further demonstrate the practical value of optimization-based decision support.
We address the retail shelf space allocation problem, which incorporates product simultaneous categorization across horizontal and vertical shelves. The problem also includes products with varying storage conditions and limited compatibility to be sold nearby. Our mathematical model focuses on optimizing the visibility of the products on the shelves in the retail store. We introduce a novel flower-cutting heuristic, which employs two variations of internal sorting rules. Seventeen parameters were used to narrow the solution space and generate efficient solutions without relying on randomization or exhaustive searches. When compared to optimal solutions obtained from a commercial solver, our approach yields solutions with 98.58%-100% accuracy. Optimal solutions were achieved for 17 out of 25 instances using both heuristics. The developed heuristics offer an approach that focuses on optimizing resource allocation problems.
Railway transportation plays a critical role in supporting sustainable mobility in Indonesia, yet significant fluctuations in passenger demand often lead to congestion and operational challenges. This study presents a systematic evaluation of decomposition-based forecasting frameworks for railway passenger demand prediction by integrating Seasonal-Trend Decomposition using Loess, EMD applied to residual components, and Fuzzy C-Means clustering. Using the Argo Muria train service as a case study, multiple deep learning models, including LSTM, GRU, RNN, CNN, and BiLSTM, are trained on decomposed components, and their forecasts are combined linearly. Model performance is evaluated using a rolling-origin strategy across multiple stations. At the primary destination station, Semarang-Gambir, the best configuration achieves an MAE of 19.88, RMSE of 26.79, sMAPE of 8.97, and R2 of 0.84. Consistent results across stations demonstrate the framework's robustness and generalization capability.
