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

Balancing labor requirements in a manufacturing environment

“This research examines construction environments within manufacturing facilities, specifically semiconductor manufacturing facilities, and develops a new optimization method that is scalable for large construction projects with multiple execution modes and resource constraints. The model is developed to represent real-world conditions in which project activities do not have a fixed, prespecified duration but rather a total amount of work that is directly impacted by the level of resources assigned. To expand on the concept of resource driven project durations, this research aims to mimic manufacturing construction environments by allowing a non-continuous resource allocation to project tasks. This concept allows for resources to shift between projects in order to achieve the optimal result for the project manager. Our model generates a novel multi-objective resource constrained project scheduling problem. Specifically, two objectives are studied; the minimization of the total direct labor cost and the minimization of the resource leveling. This research will utilize multiple techniques to achieve resource leveling and discuss the advantage each one provides to the project team, as well as a comparison of the Pareto Fronts between the given resource leveling and cost minimization objective functions. Finally, a heuristic is developed utilizing partial linear relaxation to scale the optimization model for large scale projects. The computation results from multiple randomly generated case studies show that the new heuristic method is capable of generating high quality solutions at significantly less computational time”--Abstract, page iv

Economic and environmental comparison of grouping strategies in coordinated multi-item inventory systems

The increasing concerns for sustainability throughout supply chains are enforcing managers to plan their operations considering not only economic but also environmental performance. Inventory management is one of the main determinants of the costs incurred and emissions generated throughout supply chains as it defines the level of logistical operations, freight transportation, and warehousing activities. In this study, we analyze a multi-item inventory control system with coordinated shipments. In particular, we revisit the well-known deterministic joint replenishment problem (JRP) with economic and environmental objectives under indirect and direct grouping strategies. We formulate and develop solution methods for each bi-objective JRP and compare direct and indirect grouping strategies with respect to their economic as well as environmental performance. A set of numerical studies is presented to examine the settings where a specific grouping strategy can be economically and environmentally better than the other

Economic and Environmental Comparison of Grouping Strategies in Coordinated Multi-Item Inventory Systems

The increasing concerns for sustainability throughout supply chains are enforcing managers to plan their operations considering not only economic but also environmental performance. Inventory management is one of the main determinants of the costs incurred and emissions generated throughout supply chains as it defines the level of logistical operations, freight transportation, and warehousing activities. In this study, we analyze a multi-item inventory control system with coordinated shipments. In particular, we revisit the well-known deterministic joint replenishment problem (JRP) with economic and environmental objectives under indirect and direct grouping strategies. We formulate and develop solution methods for each bi-objective JRP and compare direct and indirect grouping strategies with respect to their economic as well as environmental performance. A set of numerical studies is presented to examine the settings where a specific grouping strategy can be economically and environmentally better than the other