Conference Agenda

The prevalence of work-related musculoskeletal disorders (WMSDs) poses a significant challenge for construction companies. These disorders not only cause considerable physical pain and suffering for affected workers but also lead to decreased productivity, increased absenteeism, and escalating healthcare costs for employers. The situation is exacerbated by ongoing labor shortages and shifting workforce demographics. To address this issue, we present a novel multi-objective model designed to optimize construction projects both ergonomically and economically.

The proposed Bi-MRCPSP model expands upon the multi-mode resource-constrained project scheduling problem (MRCPSP) by incorporating equipment modes for differently skilled workers in addition to execution modes for jobs. We consider three objectives: (1) project makespan, (2) project cost, and (3) workers' occupational metabolic energy expenditure (OMEE). In the context of these objectives, three strategies to improve ergonomics are integrated: (1) additional workforce, (2) planned recovery breaks, and (3) the use of exoskeletons for mechanical support.

Applying the model to the installation of photovoltaic (PV) systems on houses demonstrates its validity and provides practical insights. Results from a Pareto front analysis reveal the potential of exoskeletons as a supportive technology in construction projects, providing valuable insights for decision-making regarding project planning and technology investments. A key finding is that exoskeletons enable more time- and cost-efficient ergonomic workplace designs, encouraging both companies and researchers to explore this technology further.

We are considering an integrated project and personnel scheduling problem with equipment transportation. It consists of one or several projects whose activities need to be completed to finish the projects. The activities are multi-modal and discretely preemptive, and there are precedence constraints that need to be respected. Furthermore, we have personnel and equipment that need to be allocated so that the activities' resource demands are satisfied.

Due to the complexities arising when scheduling the projects and resources simultaneously, a Dantzig–Wolfe reformulation is proposed. This method decomposes the problem into a master problem and one or several subproblems to find improving solutions. In our case, the allocation of personnel and equipment is taking place in the master problem, while the projects are scheduled in the subproblems by solving a preemptive project scheduling problem without resource constraints. As the projects are independent of each other, the subproblems are given one project each to find improving solutions that are then provided as columns to the master problem.

Using column generation, we are able to strengthen the linear relaxation of the integrated problem, which in turn yields good solutions more efficiently. Furthermore, this approach shows promising results regarding to find better solutions compared to a commercial solver.

This work addresses a problem inspired from scheduling problems in the food industry, with specific precedence graphs due to the workshop structure.

The problem can be modelled as a Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) with machine and sequence-dependent setup times, generalised precedence to represent waiting time (for example letting a dough rise or a baking tin cool down before the next step) and non-renewable resource may be delivered during the production period (e.g. perishable raw ingredients).

To tackle this problem we proposed a multiphase method using both linear and constraint programming derived from Unrelated Parallel Machine Scheduling dividing it in 3 sub-problems : assigning tasks to machines, ordering tasks on each machine and finally, scheduling each task.

The first step is to assign tasks to processing modes and by extension to machines while minimising the working time. This step must take into account processing time of the task and an estimated setup time.

The second step is comparable to TSP and try to minimize the working time on each machine individually by sequencing its set of assigned tasks by adding the final setup times in the model.

The last phase is meant to arbitrate resource concurrency but will also apply the final scheduling by taking into account the generalized precedences.

We will present numerical results and further improvements to the method, particularly performance comparisons between linear and constraint programming solving of each phase and bound approximation in the two first phases.