Solving the Integrated Bin Allocation and Collection Routing Problem for Municipal Solid Waste: a Benders Decomposition Approach

The municipal solid waste system is a complex reverse logistic chain which comprises several optimisation problems. Although these problems are interdependent, i.e., the solution to one of the problems restricts the solution to the other, they are usually solved sequentially in the related literature because each is usually a computationally complex problem. We address two of the tactical planning problems in this chain by means of a Benders decomposition approach: determining the location and/or capacity of garbage accumulation points, and the design and schedule of collection routes for vehicles. Our approach manages to solve medium-sized real-world instances in the city of Bah\'{i}a Blanca, Argentina, showing smaller computing times than solving a full MIP model.Comment: 29 pages, 6 figure

Circular economy implementation in waste management network design problem: a case study

The paper presents a new approach to support the strategic decision-making in the area of municipal solid waste management applying modern circular economy principles. A robust two-stage integer non-linear program is developed. The primary goal tends to reduce the waste production. The generated waste should be preferably recycled as much as possible and the resultant residual waste might be used for energy recovery. Only some waste residues are appropriate for landfilling. The aim is to propose the near-optimal waste allocation for its suitable processing as well as waste transportation plan at an operational level. In addition, the key strategical decisions on waste treatment facilities location must be made. Since waste production is very often hard to predict, it is modeled as an uncertain decision-dependent quantity. To support the circular economy ideas, advertising and pricing principles are introduced and applied. Due to the size of available real-world data and complexity of the designed program, the presented model is linearized and uncertainty is handled by a robust optimization methodology. The model, data, and algorithm are implemented in MATLAB and Julia, using the state-of-the-art solvers. The computational result is a set of decisions providing a trade-off between the average performance and the immunization against the worst-case conditions. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

Integrating bio-hubs in biomass supply chains: Insights from a systematic literature review

Biomass sources are geographically scattered, and seasonal changes influence their availability. Variations in location, type, and feedstock quality impose logistical and storage challenges. Such a dispersion and variety of biomass sources, as well as the dispersion of demand points, may undermine the economies of scale and increase the risk of supply shortage. By consolidating biomass preprocessing and distribution activities in bio-hub facilities, they can contribute to the overall resilience of biomass supply chains (BSCs) and ensure a more sustainable and cost-efficient approach to bioenergy production. As such, investigating the advantages and challenges associated with bio-hub implementation can offer invaluable insights on the efficiency and sustainability of BSCs. Despite its critical role, a major part of the literature on BSCs is confined to the decision-making processes related to biomass suppliers and bioconversion facilities. To bridge this research gap, the current study conducts a systematic literature review on bio-hub implementation within BSCs in the period of the last ten years. Shortlisted papers are classified and analyzed meticulously to extract possible improvements from BSC and modeling perspectives. From the BSC viewpoint, one notable gap is the little attention to mid-term and short-term decisions of bio-hub operations such as inventory control, resource management and production planning. Furthermore, the results revealed that environmental and social aspects of bio-hub implementation require considerable attention. From the modeling perspective, findings illustrate the underutilization of integrated approaches to incorporate micro-level and macro-level information in decision-making. In this regard, a number of areas are suggested for further exploration

Mathematical optimization and learning models to address uncertainties and sustainability of supply chain management

As concerns about climate change, biodiversity loss, and pollution have become more widespread, new worldwide challenges deal with the protection of the environment and the conservation of natural resources. Thus, in order to empower sustainability and circular economy ambitions, the world has shifted to embrace sustainable practices and policies. This is carried out, primarily, through the implementation of sustainable business practices and increased investments in green technology. Advanced information systems, digital technologies and mathematical models are required to respond to the demanding targets of the sustainability paradigm. This trend is expanding with the growing interest in production and services sustainability in order to achieve economic growth and development while preventing their negative impact on the environment. A significant step forward in this direction is enabled by Supply Chain Management (SCM) practices that exploit mathematical and statistical modeling to better support decisions affecting both profitability and sustainability targets. Indeed, these targets should not be approached as competing goals, but rather addressed simultaneously within a comprehensive vision that responds adequately to both of them. Accordingly, Green Supply Chain Management (GSCM) can achieve its goals through innovative management approaches that consider sustainable efficiency and profitability to be clearly linked by the savings that result from applying optimization techniques. To confirm the above, there is a growing trend of applying mathematical optimization models for enhancing decision-making in pursuit of both environmental and profit performance. Indeed, GSCM takes into account many decision problems, such as facility location, capacity allocation, production planning and vehicle routing. Besides sustainability, uncertainty is another critical issue in Supply Chain Management (SCM). Considering a deterministic approach would definitely fail to provide concrete decision support when modeling those kinds of scenarios. According to various hypothesis and strategies, uncertainties can be addressed by exploiting several modeling approaches arising from statistics, statistical learning and mathematical programming. While statistical and learning models accounts variability by definition, Robust Optimization (RO) is a particular modeling approach that is commonly applied in solving mathematical programming problems where a certain set of parameters are subject to uncertainty. In this dissertation, mathematical and learning models are exploited according to different approaches and models combinations, providing new formulations and frameworks to address strategic and operational problems of GSCM under uncertainty. All models and frameworks presented in this dissertation are tested and validated on real-case instances

Green supply chain quantitative models for sustainable inventory management: A review

[EN] This paper provides a systematic and up-to-date review and classification of 91 studies on quantitative methods of green supply chains for sustainable inventory management. It particularly identifies the main study areas, findings and quantitative models by setting a point for future research opportunities in sustainable inventory management. It seeks to review the quantitative methods that can better contribute to deal with the environmental impact challenge. More specifically, it focuses on different supply chain designs (green supply chain, sustainable supply chain, reverse logistics, closed-loop supply chain) in a broader application context. It also identifies the most important variables and parameters in inventory modelling from a sustainable perspective. The paper also includes a comparative analysis of the different mathematical programming, simulation and statistical models, and their solution approach, with exact methods, simulation, heuristic or meta-heuristic solution algorithms, the last of which indicate the increasing attention paid by researchers in recent years. The main findings recognise mixed integer linear programming models supported by heuristic and metaheuristic algorithms as the most widely used modelling approach. Minimisation of costs and greenhouse gas emissions are the main objectives of the reviewed approaches, while social aspects are hardly addressed. The main contemplated inventory management parameters are holding costs, quantity to order, safety stock and backorders. Demand is the most frequently shared information. Finally, tactical decisions, as opposed to strategical and operational decisions, are the main ones.The research leading to these results received funding from the Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". It was also funded by the National Agency for Research and Development (ANID) / Scholarship Program/Doctorado Becas en el Extranjero/2020 72210174.Becerra, P.; Mula, J.; Sanchis, R. (2021). Green supply chain quantitative models for sustainable inventory management: A review. Journal of Cleaner Production. 328:1-16. https://doi.org/10.1016/j.jclepro.2021.129544S11632

Network design and technology management for waste to energy production:An integrated optimization framework under the principles of circular economy

The design of waste to bioenergy supply chains (W-BESC) is critically important for meeting the circular economy (CE) goals, whilst also ensuring environmental sustainability in the planning and operation of energy systems. This study develops a novel optimization methodology to aid sustainable design and planning of W-BESC that comprise multiple technologies as well as multiple product and feedstock types. The methodology identifies the optimum supply chain configuration and plans the logistics operations in a given region to meet the energy demand of specified nodes. A scenario based fuzzy multi objective modelling approach is proposed and utilized to capture the economic and environmental sustainability aspects in the same framework. We test the proposed model using the entire West Midlands (WM) region from the United Kingdom (UK) as a case study. In this scope, a comprehensive regional supply chain is designed to meet the energy and biofertilizer demand of specific nodes considering available waste and crop type biomass in the region. Further analysis is conducted to reveal the impacts of main economic and technological parameters on the supply chain performance indicators

Tools for Optimization of Biomass-to-Energy Conversion Processes

Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts

Sustainability assessment of biomass-based energy supply chain using multi-objective optimization model

In recent years, population growth and lifestyle changes have led to an increase in energy consumption worldwide. Providing energy from fossil fuels has negative consequences, such as energy supply constraints and overall greenhouse gas emissions. As the world continues to evolve, reducing dependence on fossil fuels and finding alternative energy sources becomes increasingly urgent. Renewable energy sources are the best way for all countries to reduce reliance on fossil fuels while reducing pollution. Biomass as a renewable energy source is an alternative energy source that can meet energy needs and contribute to global warming and climate change reduction. Among the many renewable energy options, biomass energy has found a wide range of application areas due to its resource diversity and easy availability from various sources all year round. The supply assurance of such energy sources is based on a sustainable and effective supply chain. Simultaneous improvement of the biomass-based supply chain's economic, environmental and social performance is a key factor for optimum network design. This study has suggested a multi-objective goal programming (MOGP) model to optimize a multi-stage biomass-based sustainable renewable energy supply chain network design. The proposed MOGP model represents decisions regarding the optimal number, locations, size of processing facilities and warehouses, and amounts of biomass and final products transported between the locations. The proposed model has been applied to a real-world case study in Istanbul. In addition, sensitivity analysis has been conducted to analyze the effects of biomass availability, processing capacity, storage capacity, electricity generation capacity, and the weight of the goals on the solutions. To realize sensitivity analysis related to the importance of goals, for the first time in the literature, this study employed a spherical fuzzy set-based analytic hierarchy method to determine the weights of goals

Simheuristics to support efficient and sustainable freight transportation in smart city logistics

La logística urbana intel·ligent constitueix un factor crucial en la creació de sistemes de transport urbà eficients i sostenibles. Entre altres factors, aquests sistemes es centren en la incorporació de dades en temps real i en la creació de models de negoci col·laboratius en el transport urbà de mercaderies, considerant l’augment dels habitants en les ciutats, la creixent complexitat de les demandes dels clients i els mercats altament competitius. Això permet als que planifiquen el transport minimitzar els costos monetaris i ambientals del transport de mercaderies a les àrees metropolitanes. Molts problemes de presa de decisions en aquest context es poden formular com a problemes d’optimació combinatòria. Tot i que hi ha diferents enfocaments de resolució exacta per a trobar solucions òptimes a aquests problemes, la seva complexitat i grandària, a més de la necessitat de prendre decisions instantànies pel que fa a l’encaminament de vehicles, la programació o la situació d’instal·lacions, fa que aquestes metodologies no s’apliquin a la pràctica. A causa de la seva capacitat per a trobar solucions pseudoòptimes en gairebé temps real, els algorismes metaheurístics reben una atenció creixent dels investigadors i professionals com a alternatives eficients i fiables per a resoldre nombrosos problemes d’optimació en la creació de la logística de les ciutats intel·ligents. Malgrat el seu èxit, les tècniques metaheurístiques tradicionals no representen plenament la complexitat dels sistemes més realistes. En assumir entrades (inputs) i restriccions de problemes deterministes, la incertesa i el dinamisme experimentats en els escenaris de transport urbà queden sense explicar. Els algorismes simheurístics persegueixen superar aquests inconvenients mitjançant la integració de qualsevol tipus de simulació en processos metaheurístics per a explicar la incertesa inherent a la majoria de les aplicacions de la vida real. Aquesta tesi defineix i investiga l’ús d’algorismes simheurístics com el mètode més adequat per a resoldre problemes d’optimació derivats de la logística de les ciutats. Alguns algorismes simheurístics s’apliquen a una sèrie de problemes complexos, com la recollida de residus urbans, els problemes de disseny de la cadena de subministrament integrada i els models de transport innovadors relacionats amb la col·laboració horitzontal entre els socis de la cadena de subministrament. A més de les discussions metodològiques i la comparació d’algorismes desenvolupats amb els referents de la bibliografia acadèmica, es mostra l’aplicabilitat i l’eficiència dels algorismes simheurístics en diferents casos de gran escala.Las actividades de logística en ciudades inteligentes constituyen un factor crucial en la creación de sistemas de transporte urbano eficientes y sostenibles. Entre otros factores, estos sistemas se centran en la incorporación de datos en tiempo real y la creación de modelos empresariales colaborativos en el transporte urbano de mercancías, al tiempo que consideran el aumento del número de habitantes en las ciudades, la creciente complejidad de las demandas de los clientes y los mercados altamente competitivos. Esto permite minimizar los costes monetarios y ambientales del transporte de mercancías en las áreas metropolitanas. Muchos de los problemas de toma de decisiones en este contexto se pueden formular como problemas de optimización combinatoria. Si bien existen diferentes enfoques de resolución exacta para encontrar soluciones óptimas a tales problemas, su complejidad y tamaño, además de la necesidad de tomar decisiones instantáneas con respecto al enrutamiento, la programación o la ubicación de las instalaciones, hacen que dichas metodologías sean inaplicables en la práctica. Debido a su capacidad para encontrar soluciones pseudoóptimas casi en tiempo real, los algoritmos metaheurísticos reciben cada vez más atención por parte de investigadores y profesionales como alternativas eficientes y fiables para resolver numerosos problemas de optimización en la creación de la logística de ciudades inteligentes. A pesar de su éxito, las técnicas metaheurísticas tradicionales no representan completamente la complejidad de los sistemas más realistas. Al asumir insumos y restricciones de problemas deterministas, se ignora la incertidumbre y el dinamismo experimentados en los escenarios de transporte urbano. Los algoritmos simheurísticos persiguen superar estos inconvenientes integrando cualquier tipo de simulación en procesos metaheurísticos con el fin de considerar la incertidumbre inherente en la mayoría de las aplicaciones de la vida real. Esta tesis define e investiga el uso de algoritmos simheurísticos como método adecuado para resolver problemas de optimización que surgen en la logística de ciudades inteligentes. Se aplican algoritmos simheurísticos a una variedad de problemas complejos, incluyendo la recolección de residuos urbanos, problemas de diseño de la cadena de suministro integrada y modelos de transporte innovadores relacionados con la colaboración horizontal entre los socios de la cadena de suministro. Además de las discusiones metodológicas y la comparación de los algoritmos desarrollados con los de referencia de la bibliografía académica, se muestra la aplicabilidad y la eficiencia de los algoritmos simheurísticos en diferentes estudios de casos a gran escala.Smart city logistics are a crucial factor in the creation of efficient and sustainable urban transportation systems. Among other factors, they focus on incorporating real-time data and creating collaborative business models in urban freight transportation concepts, whilst also considering rising urban population numbers, increasingly complex customer demands, and highly competitive markets. This allows transportation planners to minimize the monetary and environmental costs of freight transportation in metropolitan areas. Many decision-making problems faced in this context can be formulated as combinatorial optimization problems. While different exact solving approaches exist to find optimal solutions to such problems, their complexity and size, in addition to the need for instantaneous decision-making regarding vehicle routing, scheduling, or facility location, make such methodologies inapplicable in practice. Due to their ability to find pseudo-optimal solutions in almost real time, metaheuristic algorithms have received increasing attention from researchers and practitioners as efficient and reliable alternatives in solving numerous optimization problems in the creation of smart city logistics. Despite their success, traditional metaheuristic techniques fail to fully represent the complexity of most realistic systems. By assuming deterministic problem inputs and constraints, the uncertainty and dynamism experienced in urban transportation scenarios are left unaccounted for. Simheuristic frameworks try to overcome these drawbacks by integrating any type of simulation into metaheuristic-driven processes to account for the inherent uncertainty in most real-life applications. This thesis defines and investigates the use of simheuristics as a method of first resort for solving optimization problems arising in smart city logistics concepts. Simheuristic algorithms are applied to a range of complex problem settings including urban waste collection, integrated supply chain design, and innovative transportation models related to horizontal collaboration among supply chain partners. In addition to methodological discussions and the comparison of developed algorithms to state-of-the-art benchmarks found in the academic literature, the applicability and efficiency of simheuristic frameworks in different large-scaled case studies are shown

Waste processing facility location problem by stochastic programming: Models and solutions

The paper deals with the so-called waste processing facility location problem (FLP), which asks for establishing a set of operational waste processing units, optimal against the total expected cost. We minimize the waste management (WM) expenditure of the waste producers, which is derived from the related waste processing, transportation, and investment costs. We use a stochastic programming approach in recognition of the inherent uncertainties in this area. Two relevant models are presented and discussed in the paper. Initially, we extend the common transportation network flow model with on-and-off waste-processing capacities in selected nodes, representing the facility location. Subsequently, we model the randomly-varying production of waste by a scenario-based two-stage stochastic integer linear program. Finally, we employ selected pricing ideas from revenue management to model the behavior of the waste producers, who we assume to be environmentally friendly. The modeling ideas are illustrated on an example of limited size solved in GAMS. Computations on larger instances were realized with traditional and heuristic algorithms, implemented within MATLAB. © Springer Nature Switzerland AG 2019