Recyclables Collection Route Balancing Problem with Heterogeneous Fleet

Nowadays, robust and efficient solid waste collection is crucial to motivate citizens to participate in the circular economy by sorting recyclable solid waste. Vocational vehicles, including garbage trucks, contribute significantly to CO2 emissions; therefore, it is strongly recommended, and in the European Union it is mandatory, to replace conventional-fuel-based garbage trucks with electric ones. For providing sustainable and energy-efficient solid waste collection with a heterogeneous fleet, in-depth mathematical computations are needed to support solving complex decision-making problems, including crew rostering and vehicle routing, because the distance and capacity of electric garbage trucks differ from conventional-fuel-based ones. However, the literature on solid waste collection using electric garbage trucks is still relatively scarce. The main contribution of this paper is developing an optimization problem for balancing travel distance assigned to each garbage truck of a heterogeneous fleet. The problem is based on specific requirements of the Municipal Solid Waste Management in Cracow, Poland, where the working time of routes is balanced and the total time of collection service can be minimized. For the problem, an MIP program was developed to generate optimal crew schedules, so that the hitherto network of segregated solid waste pickup nodes can be served using a heterogeneous fleet in which the share of electric garbage trucks is up to 30%. We study the impact of the changed composition of the fleet on modifications in crew rostering due to the shorter range of an electric vehicle compared to a conventional-fuel-based one.

Is Equality Always Desirable? Analyzing the Trade-Off Between Fairness and Attractiveness in Crew Rostering

Millions of employees around the world work in irregular rosters. The quality of these rosters is of utmost importance. High-quality rosters should be attractive on an individual level, but also divide the work fairly over the employees. We develop novel methodology to compute the trade-off between fairness and attractiveness in crew rostering. First, we propose an intuitive fairness scheme for crew rostering and analyze its theoretical performance. To do so, we introduce the approximate resource-allocation problem. This extension of the resource-allocation problem provides a framework for analyzing decision making in contexts where one relies on approximations of the utility functions. Fairness is a typical example of such a context due to its inherently subjective nature. We show that the scheme has “optimal” properties for a large class of approximate utility functions. Furthermore, we provide a tight bound on the utility loss for this scheme. We then present a unified approach to crew rostering. This approach integrates our proposed fairness scheme with a novel mathematical formulation for crew rostering. We call the resulting problem the Fairness-Oriented Crew Rostering Problem and develop a dedicated exact Branch-Price-and-Cut solution method. We conclude by applying our solution approach to practical instances from Netherlands Railways, the largest passenger railway operator in the Netherlands. Our computational results confirm the importance of taking the fairness–attractiveness trade-off into account. This paper was accepted by Yinyu Ye, optimization.

A new mathematical model to cover crew pairing and rostering problems simultaneously

Crew scheduling problem includes two separate subproblems, namely, crew pairing and crew rostering problems. Solving these two subproblems in a sequential order may not lead to an optimal solution. This study includes two main novelties. It combines these two subproblems and presents them in a single model. On the other hand, despite previous researches that considered a pairing continuously, the proposed model benefits from the capability of considering one or more days off in a pairing assigned to a crew member. This is extremely useful as it enables the crew to participate in required courses, doing medical checks, etc. Two solution approaches, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to solve the model. Eventually, the performance of the proposed algorithms is evaluated. Both ended to satisfactory results; however, PSO relatively outperformed GA in terms of solution optimality and computational time.

Aspects of airline crew rostering

Airline crew rostering is a complicated planning-type problem, and its objective is to assemble pairings into schedules that maximise the satisfaction levels of crews. The rostering process focuses on achieving a more balanced workload distribution among the crewmembers that allows designing rosters in the interest of the crew. The main purpose of the paper was to explain the fundamental aspects of airline crew rostering and its impact on crewmembers’ fatigue. Additionally, the research identified mitigation measures that airlines should make to combat or mitigate crewmembers’ fatigue when designing their rosters. The paper also allowed readers to understand the effects of fatigue on crew’s alertness and performance. The qualitative research methods (such as literature review) was used to understand the complexity of the airline crew rostering, the flight and duty time limitations, crew’s rest requirement and crewmembers’ fatigue. The survey was used as the quantitative research method to identify crewmembers’ satisfaction with their rosters that would help to optimise the rostering process. An online survey (using Google Forms) was distributed to potential respondents (experienced crewmembers) via email addresses and online platforms. Respondents were asked 18 close ended questions divided into two parts (demographic and operational experience). One hundred eleven responses were gathered, which showed that age plays a crucial role in crewmembers’ fatigue. In addition, cargo pilots are usually more dissatisfied with their rosters due to exhausting flight duty periods at nights.

Planejamento da designação de maquinistas a trens de viagem de longa distância em ferrovias de transporte de carga

As ferrovias brasileiras transportam cargas em viagens longas. O trem é conduzido por um maquinista e, por conta da extensão da viagem com tempo total de duração, geralmente, superior à escala de trabalho do maquinista, fazendo-se necessário realizar a troca de maquinistas durante a viagem em locais pré-definidos, denominados destacamentos. Na literatura, este problema é tratado de forma separada por dois subproblemas: Crew Scheduling e Crew Rostering. Diferentemente, este artigo propõe um modelo matemático para resolver de forma integrada os dois subproblemas, inspirado no Problema de Roteamento de Veículos com Múltiplas Viagens e Múltiplos Depósitos, para planejar a designação de maquinistas para conduzir os trens que passarão pelos destacamentos, buscando minimizar o pagamento de salários e horas extras aos maquinistas. Os resultados demonstram possíveis reduções de custos quando comparados com o planejamento manual da ferrovia analisada e reduções de custo podem ser alcançadas se aumentar o tempo de escala.

Graph Coloring based Heuristic for Crew Rostering

In the last years personnel cost became a huge factor in the financial management of many companies and institutions.The firms are obligated to employ their workers in accordance with the law prescribing labour rules. The companies can save costs with minimizing the differences between the real and the expected worktimes. Crew rostering is assigning the workers to the previously determined shifts, which has been widely studied in the literature. In this paper, a mathematical model of the problem is presented and a two-phase graph coloring method for the crew rostering problem is introduced. Our method has been tested on artificially generated and real life input data. The results of the new algorithm have been compared to the solutions of the integer programming model for moderate-sized problems instances.