Deterministic Integer Linear Programming Research Articles

Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints

This paper addresses an advance surgery scheduling problem in an operating theater composed of multiple operating rooms (ORs) and a downstream surgical intensive care unit (SICU). Uncertainties in surgery durations and postoperative length-of-stays are taken into consideration. The decisions are made on a weekly basis and consist of three parts: determining the surgical blocks to open, selecting the surgeries to be performed from a waiting list, and assigning the selected surgeries to available surgical blocks. The objective is to minimize the patient-related cost as well as the hospital-related cost while respecting the SICU capacity constraints. We propose a two-stage stochastic programming model with recourse to address the studied problem. Sample average approximation is employed to translate the stochastic programming model into a deterministic integer linear programming (DILP) model, which is then solved by column-generation-based heuristic (CGBH) approaches. The CGBH approaches developed in this paper reformulate the DILP model in a column-oriented way and adopt multiple column-generation strategies and heuristic rules to improve computational efficiency. The experimental results illustrate that the proposed CGBH approaches require significantly less computation time than the conventional algorithm, and that the gaps between the resulting near-optimal solutions and the exact ones are below 1%. Moreover, numerical experiments carried out with large test problems validate the capability of the CGBH approaches in solving realistically sized cases.

Air cargo simultaneous weight and volume capacity planning with revenue management approach

Revenue management (RM) is a subfield of operations research with the aim to maximise the revenues acquired by selling perishable products/services. Due to the substantial growth in air cargo industry over the past few years, sophisticated techniques are needed to maximise revenue. In this paper, airline cargo capacity allocation problem in two cases, including cancellation possibility and impossibility have been investigated. Two capacity dimensions of the problem, volume and weight, is complicated the decision making about request acceptance policy. For the formulation of two problems, dynamic programming technique has been used. Since dynamic programming suffers from much memory consumption for large size problems, three heuristics including deterministic integer linear programming (DILP), bid price (BP) and dynamic programming decomposition (DPD) has been proposed for problems solving. Results of simulation showed that BP and DILP have the better performance comparing to other approaches. In addition, comparison of two problem's optimum values indicated that considering cancellation increase total revenue more than 10%.