Abstract in English:

ABSTRACT This paper presents na integrated surgery scheduling and post-surgical bed planning problem for a standard hospital setting. The setting gives rise to a general healthcare modelling problem with a number of innovations with respect to the literature. The model includes multiple post-surgical recovery trajectories involving possible stays at the intensive (ICU) or semi-intensive care unit (SICU) and allows the decision maker to assign a bed allocation plan that considers the maximum length of stays at both SICU and ICU. The approach is designed to ensure a seamless patient flow, avoiding surgery cancellations due to insufficient downstream resources, and enables tactical planning that considers the long-term balance between demand and surgery provision across all specialities. To validate the model and investigate the sensitivity with respect to model parameters and the availability of resources, we use a series of experiments that were based on the actual operation of a military hospital’s orthopaedic department. The results illustrate the demand pressures, as na optimised allocation with the current demand and resources results in na occupation of 96.5%. We also show that increases in demand should be matched by a similar percentage increase in operating theatre capacity in order to keep the occupation below 100%.

Abstract in English:

ABSTRACT This paper focuses on a scheduling problem that arises when job processing times are variable and subject to change based on due dates. The objective is to minimize the makespan on a single machine, while ensuring that jobs are completed independently and without interruption. This problem is challenging due to the possibility of a job’s processing time deteriorating over time. We have developed two mixed integer programming models to obtain optimal solutions, but due to the NP-hardness of this problem, we recommend using heuristics and metaheuristics (genetic algorithm and simulated annealing) as well. To validate our approach, we conducted computational experiments using randomly generated test instances. Our findings indicate that the improved second model can produce better solutions for up to 40 jobs within a one hour time frame. Furthermore, the genetic algorithm performs comparably better than other heuristics and simulated annealing for larger instances.

Abstract in English:

ABSTRACT In response to the increasing uncertainties in agricultural decision-making, driven by market fluctuations and changes in environmental conditions, this research implemented the Balanced DecisionMaking Method (BDMM) to improve crop selection on a farm in western Bahia. The BDMM integrates multiple decision-making methods-TOPSIS, PROMETHEE II, and the Borda method-to provide a comprehensive and balanced evaluation. Both qualitative and quantitative data were gathered from literature, insights from two decision-makers, and the authors of the article. Qualitative factors, such as cost estimates, market prices, and labor requirements, were sourced from various references, while quantitative data focused on customer demand and expert consensus regarding pest risk. The combination of these data sources and the application of the proposed method enabled a detailed analysis that reflected the combined preferences and needs of each producer. The BDMM facilitated consensus among the producers, supporting decisions that optimize both economic performance and environmental sustainability.

Abstract in English:

ABSTRACT Export market selection (EMS) is a critical strategic decision that significantly impacts the success or failure of exporting companies. This study presents an innovative multi-criteria decision-making framework that integrates Fuzzy Delphi, Best-Worst Method (BWM), and Fuzzy TOPSIS to tackle complex decision-making challenges in the context of export market selection for liquefied natural gas (LNG) and naphtha. Through a comprehensive literature review, the most important criteria for EMS are identified and ranked, culminating in an evaluation of five potential export markets. The findings reveal that ”market potential/elasticity” is the foremost criterion for EMS, with China emerging as the optimal export market for LNG and naphtha. This research not only offers a systematic methodology for export market selection but also highlights practical implications for businesses and policymakers striving to enhance export performance while aligning with broader sustainability goals, including the United Nations Sustainable Development Goals (SDGs). By providing valuable insights into market prioritization and decision-making frameworks, this study contributes to the fields of international business and petrochemical exports.

Abstract in English:

ABSTRACT In this paper, we focus on the multi-objective stochastic multiple knapsack problem, in which the object weights are random. We propose a new approach called the multi-objective memetic algorithm with either Martello and Toth’s heuristic method (MTHM) or the method denoted MTHMn, recently developed and inspired by MTHM. In each iteration of this approach, crossover, mutation, and local search techniques are applied to the current feasible population (parents), initially generated by the greedy algorithm. These operations generate new solutions that form a population of descendants. Next, a selection operator is employed to choose the best elements from the combined population comprising parents and offspring, using the non-domination sorting and the crowding distance calculation algorithms. A comparative analysis of the memetic algorithm using the MTHM and MTHMn heuristics with an exact method is performed, and the experimental results demonstrate the significant efficiency achieved by the memetic algorithm with the MTHMn heuristic.