15h30 - 15h55
Operating room management with limited beds in downstream units
In hospitals, effective management of operating rooms is critical since they are responsible for a significant portion of the total cost and revenue. One of the main reasons for the cancellation of surgeries is the unavailability of beds in downstream units due to the uncertain durations of patients’ length of stays. Therefore, the effective management of downstream cannot be overlooked while studying the productivity of operating rooms. In this paper, we study an operating room planning problem with a limited number of beds in multiple downstream units (i.e. ICU and ward) and evaluate the effect of sharing beds among different specialties. We formulate this problem as a two-stage stochastic programming model and embed it in a sample average approximation framework. Using extensive computational experiments, we analyze the effect of resource pooling in downstream units and provide managerial insights.
15h55 - 16h20
A multi-objective mathematical model, integrated with machine learning techniques for supplier selection & order allocation
In this talk, a novel 2-stage approach for supplier selection & order allocation planning is discussed. Stage 1 includes determining the values of the demands based on machine learning techniques. In Stage 2, a new multi-objective model is introduced to select the best supplier(s) and determine the order(s).
16h20 - 16h45
Utilizing energy transition to drive sustainability in cold supply chains
In alignment with the ever-growing interest in adopting sustainable practices, this paper devises a cold supply chain (CSC) planning model that integrates the three pillars of sustainability into the decision-making process while accounting for the shift towards clean energy sources. Decisions pertaining to production-distribution strategy, backorder and inventory levels, choice of truck type, and selection of third-party logistics (3PLs) providers are optimized. As such, a multi-objective mixed-integer non-linear programming (MINLP) model is developed and then solved via the weighted-sum method. The model seeks to operationalize sustainability goals by considering the rapidly evolving transition in energy sources across different regions when deciding on which 3PLs to engage in a contractual agreement with while adjusting the production and distribution strategy accordingly. The practical relevance of the model is illustrated using a case study drawn from the North American frozen food industry. The analysis indicates the possibility of obtaining a drastic improvement of 86% in jobs’ stability levels with a maximum cost increase of around 9% as compared to the economic measure. Furthermore, the analysis reveals that it is possible to reduce 71% of CO2 emissions at the expense of only 4.47% cost increase once compared to solely optimizing the economic objective.
16h45 - 17h10
The road train optimization problem
This talk introduces an extension of the VRP problem, called the road train optimization problem with load assignments (RTOP– LA). Road trains consist of two or more trailers or semi-trailers hauled by a tractor truck. In the RTOP– LA, we consider a set of road trains based in the main terminal, and they are routed through several intermediate facilities. Trailers are dismantled in these facilities and loaded on trucks to be distributed to final customers. Therefore, one needs to decide on the assignments of demands to terminals and their routing of trailers to final customers. The objective is to minimize the total distribution cost. To solve randomly generated instances of the problem, we developed an iterated local search algorithm and compared the results obtained with the ones from the commercial solver.