Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock

A comprehensive framework for sustainable closed-loop supply chain network design

Many companies face challenges in reducing their supply chain costs while increasing sustainability and customer service levels. A comprehensive framework for a sustainable closed-loop supply chain (CLSC) network is a practical solution to these challenges. Hence, for the first time, this study considers an integrated multio-bjective mixed-integer linear programming (MOMILP) model to design sustainable CLSC networks with cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup, and delivery under uncertainty. An intelligent simulation algorithm is proposed to produce CLSC network data with probabilistic distribution functions and feasible solution space. In addition, a fuzzy goal programming approach is proposed to solve the MOMILP model under uncertainty. Eight small and medium-size test problems are used to evaluate the performance of the proposed model with the simulated data in GAMS software. The results obtained from test problems and sensitivity analysis show the efficacy of the proposed model

Strategic design of environmentally and socially sustainable supply networks

The five published articles of this cumulative dissertation deal with the design of supply networks on a strategic level and with a special focus on the operationalization of environmental and social indicators − addressing 16 of the 17 Sustainable Development Goals (SDGs). Based on, inter alia, case studies on Waste Electric and Electronic Equipment (WEEE) as well as lignocellulosic, second-generation bioethanol production in the EU, this work provides best-practice approaches on how to integrate results from applied Industrial Ecology methods (LCA, S-LCA) into Operations Research models (here: multi-objective mixed-integer linear programming). Beside methodological contributions, the dissertation provides insights for policy-makers, practitioners, and academia in terms of environmental, social, and economic benefits and risks of WEEE recovery and second-generation bioethanol production in the EU

STRATEGIC PLANNING OF CIRCULAR SUPPLY CHAINS WITH MULTIPLE DOWNGRADED MARKET LEVELS: A METHODOLOGICAL PROPOSAL

Recent legislation has recognized the importance of adopting Circular Economy (CE) principles in supply chain (SC) restructuring. The primary objective is to create circular supply chains (CSCs) that effectively reintegrate end-of-life (EOL) products into production networks through processes such as reusing, remanufacturing, and recycling. This paradigm shift toward circularity aims to enhance resource efficiency, extend product lifecycle, and minimise waste, thereby aligning firms with sustainable practices while providing them with a competitive advantage. In line with the goals of the CE, this study focuses on the design and optimisation of strategic decisions within a circular supply chain (CSC). To achieve this aim, a bi-objective mixed-integer linear programming (MILP) model is developed. This model represents a significant contribution as it offers a compact and generalized formulation for dealing with CSC design problems. The proposed MILP model encompasses several key decision variables and considerations. It determines the optimal number of downgraded market levels to be activated, the location of forward and treatment facilities as well as the optimal product flow within the CSC. Furthermore, the model takes into account the cannibalisation effects associated with the demand for both new and recovered products, ensuring a comprehensive analysis of the system dynamics. To solve the complex mathematical model, the augmented epsilon-constraint (AUGMECON2) method is employed. The utilisation of this method enables decision-makers to obtain practical solutions within reasonable time frames. The computational results obtained from applying the MILP model illustrate its encouraging potential and effectiveness in dealing with strategic decision-making problems within CSCs

Assisting Sustainability Analysis of Forest Bioenergy Supply Chains using Mathematical Optimization

Changes in the global climate and forest management practices have given rise to increasing numbers and severity of wildfires. More than five million acres burned in the United States in 2017, while in Canada 7.4 million acres burned. In particular, an increasing amount of dead woody biomass is a key factor in forest fire hazards. The call for mitigating the effects of climate change, specifically focusing on reducing the risk of wildfires, has attracted considerable global attention toward renewable energy sources. The objective of this research is to provide decision makers in private industry and governmental agencies the ability to reliably assess economic, environmental, and social criteria simultaneously while optimizing bio-oil supply chains in managing the land and forests to decrease wildfire risks. An optimized biomass to bio oil supply chain is presented by using a mathematical problem considering economic, environmental, and social criteria. The focus of the application of this work is on northwest Oregon forests. The production of bio-oil is not only able to help mitigate climate change impacts such as forest fire hazards, but it can also improve energy independence, employment opportunities, and economic development. To extend prior related research, a single objective mathematical model is first presented, which relaxes a limitation of prior mathematical models for bio-oil supply chain problems by considering carbon cost as a part of the total supply chain cost. Since the model is a mixed integer linear programming problem, a metaheuristic optimization approach (genetic algorithm) is designed to obtain an optimized solution. The proposed mathematical model can be applied in the design of a biomass to bio-oil supply chain including mobile refineries, in which total cost consists of logistics cost and carbon cost. Decision makers will be able to apply the proposed genetic algorithm for large scale problems to overcome restrictions of exact methods. As the demand for sustainable supply chains continues, logistics problems must be designed to balance solutions across the three pillars of sustainability: the economy, environment, and society. Thus, a multi-objective mathematical model is next developed for a bio oil supply chain, which includes six levels: harvesting sites, collection sites, mobile refineries, fixed refineries, distribution centers, and residential areas. The branch-and-cut search in CPLEX software solves the proposed model using data from northwest Oregon forests. The model obtains optimal values for three decision variables, i.e., mass of biomass to be transported, mass of bio-oil to be transported, and the facility locations, to simultaneously optimize total cost, carbon footprint, and number of jobs created. From evaluation of the model, it is found that supplementing a traditional bio-oil supply chain with mobile refineries has the potential to significantly reduce the cost of bio-oil. Sensitivity analysis is performed to evaluate the effect of key parameters on supply chain objectives under different scenarios. It was also found that the percentage yield parameter and mobile refinery capacity have a more significant effect on the selected objectives than the other parameters tested. Based on the supply chain modeling, the behavior of the predicted cost of bio-oil, carbon footprint, and number of jobs created is intuitive with respect to the changes in the model parameters. Further, the sensitivity analysis results show that the cost of bio oil predicted by the mathematical model falls in the cost interval found in the market and research literature. In addition to reducing wildfire risks and energy dependence by collecting combustible forest biomass, the research result shows that consideration of societal aspects in bio-oil supply chains can provide a competitive cost of bio-oil. Exploration of mobile refineries is a focus here to elucidate bio-oil supply chain sustainability performance through mathematical modeling, and has not been previously reported in literature. The lack of access to the conversion processes prevented a more accurate estimation of the cost of bio-oil. To improve this limitation, modeling the parameters of bio-oil supply chains using stochastic approaches in future research would allow for a more in-depth investigation of tradeoffs between objectives