ABSTRACT
An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.
ABSTRACT
The personnel assignment problem in different service industries aims to minimize the staff surplus/shortage costs. However, uncertainty in the staff demand challenges the accomplishment of that objective. This research studies the personnel assignment problem considering uncertain demand and multiskilled workforce configured through a 2-chaining strategy. We develop a two-stage stochastic optimization (TSSO) approach to calculate the multiskilling requirements that minimize the training costs and the expected costs of staff surplus/shortage. Later, we evaluate and compare the performance of the TSSO approach solutions with the solutions of two alternative optimization approaches under uncertainty - robust optimization (RO) and closed-form equation (CF). These two alternative approaches were published in Henao et al. [1] and Henao et al. [2], respectively. In addition, we compare the performance of the TSSO approach solutions with the solution of the deterministic (DT) approach and the solutions of myopic multiskilling approaches. To make performance comparisons between the different approaches, we used both real and simulated data derived from a retail store operating in Chile. The results show that, for different demand variability levels, TSSO, RO, and CF always belong to the set of approaches with the lowest average total cost. That is, in this group, there are no statistical differences from one approach to another, so these approaches are the most cost-effective. We also provide insights to retail decision-makers for addressing two key aspects. First, the methodology allows to address two fundamental multiskilling issues: how much multiskilling to add and how it should be added. Second, it is provided understanding on how to select the most suitable approach according to the balance between the conservatism and the reliability associated with the solutions delivered by each approach. Finally, we identify some methodological challenges for future research, such as the evaluation of k-chaining strategies with k≥2.
Subject(s)
Benchmarking , Cost-Benefit Analysis , Humans , Reproducibility of Results , UncertaintyABSTRACT
Electricity grid operators and planners need to deal with both the rapidly increasing integration of renewables and an unprecedented level of uncertainty that originates from unknown generation outputs, changing commercial and regulatory frameworks aimed to foster low-carbon technologies, the evolving availability of market information on feasibility and costs of various technologies, etc. In this context, there is a significant risk of locking-in to inefficient investment planning solutions determined by current deterministic engineering practices that neither capture uncertainty nor represent the actual operation of the planned infrastructure under high penetration of renewables. We therefore present an alternative optimization framework to plan electricity grids that deals with uncertain scenarios and represents increased operational details. The presented framework is able to model the effects of an array of flexible, smart grid technologies that can efficiently displace the need for conventional solutions. We then argue, and demonstrate via the proposed framework and an illustrative example, that proper modelling of uncertainty and operational constraints in planning is key to valuing operationally flexible solutions leading to optimal investment in a smart grid context. Finally, we review the most used practices in power system planning under uncertainty, highlight the challenges of incorporating operational aspects and advocate the need for new and computationally effective optimization tools to properly value the benefits of flexible, smart grid solutions in planning. Such tools are essential to accelerate the development of a low-carbon energy system and investment in the most appropriate portfolio of renewable energy sources and complementary enabling smart technologies.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.
ABSTRACT
In engineering designed systems it is commonly considered that mathematical models, variables, and parameters are sufficiently reliable, i.e., there are no errors in modeling and estimation. However, the systems to be optimized can be sensitive to small changes in the designed variables causing significant changes in the objective function. Robust optimization is an approach for modeling optimization problems under uncertainty in which the modeler aims to find decisions that are optimal for the worst-case realization of the uncertainties within a given set of values. In this contribution, a self-adaptive heuristic optimization method, namely the Self-Adaptive Differential Evolution (SADE), is evaluated. Differently from the canonical Differential Evolution algorithm (DE), the SADE strategy is able to update the required parameters such as population size, crossover parameter, and perturbation rate, dynamically. This is done by considering a defined convergence rate on the evolution process of the algorithm in order to reduce the number of evaluations of the objective function. For illustration purposes, the SADE strategy is associated with the Mean Effective Concept (MEC) for insertion robustness, is applied to minimize forces applied in cables used for the rehabilitation of the human lower limbs by determining the positioning of motors. The results show that the methodology that was proposed (SADE+MEC) appears as an interesting strategy for the treatment of robust optimization problems.
No projeto de sistemas de engenharia é comum considerar que os modelos, as variáveis e os parâmetros são confiáveis, isto é, não apresentam erros de modelagem e de estimação. Entretanto, os sistemas a serem otimizados podem ser sensíveis a pequenas alterações nas variáveis de projeto causando significativas modificações no vetor de objetivos. Otimização robusta é uma abordagem para modelagem de problemas de otimização sob incerteza em que o modelador tem como objetivo encontrar decisões que são ideais para o pior caso de realização das incertezas dentro de um determinado conjunto de valores. Neste trabalho, um método de otimização heurística auto-adaptável, nomeada Self-Adaptive Differential Evolution (SADE), é avaliada. Diferentemente do algoritmo de Evolução Diferencial, a estratégia SADE é capaz de atualizar os parâmetros necessários, tais como o tamanho da população, o parâmetro de passagem e taxa de perturbação, de forma dinâmica. Isto é feito considerando uma taxa de convergência definido no processo de evolução do algoritmo, a fim de reduzir o número de avaliações da função objetivo. Para fins de ilustração, a estratégia SADE associado ao conceito de média efetiva, para inserção da robustez, é aplicada para minimizar as forças aplicadas nos cabos da estrutura robótica utilizada para a reabilitação dos membros inferiores humanos, determinando o posicionamento dos atuadores. Os resultados mostram que o método proposto neste trabalho configura-se como uma estratégia interessante para o tratamento de problemas de otimização robustos.