Tactical and operational planning of sustainable supply chains : a study in the frozen food industry
Bibliographic record
Abstract
Nowadays, the management in the food sector is facing dynamics and complexity in supply chains more than ever. Managers need to figure out new sustainability issues in their company in order to gain a competitive advantage. Planning and design of a food supply chain are associated with an integrated and complicated decision-making process. To design and evaluate the performance of a food supply chain, different criteria and conflicting objectives must be integrated. Besides, the decisions need to be taken at different levels (strategic, tactical and operation) and stages (supplier, manufacturing, distribution, and transportation). \n \nFurthermore, the sector has been considered as the second biggest emitter of greenhouse gases after energy, and it requires cutting the emissions from its growth. Not to mention that food supply chains are heavily associated with social structures since many players and agents are involved in this system. However, there have been few attempts to optimize economic, environmental and social concerns simultaneously, especially in food supply chains. Incorporating sustainability dimensions into decision making and finding a trade-off between objectives are challenging. This is even more challenging when a supply chain deals with issues related to perishability and seasonality. Therefore, a decision support tool that can consider all these aspects is required. \n \nIn this thesis, the aim is to propose a novel and more realistic approach to design sustainable supply chains. The primary objective of this thesis is to develop an integrated tactical-operational planning model for sustainable supply chains. First, we provide a supply chain model to support the tactical planning that integrates the three dimensions of sustainability: total cost, GHG emissions, and social responsibilities. Secondly, we extend our model in order to ensure a more realistic representation of the supply chain considered in this research by proposing a multi-objective optimization model. A solution methodology is developed to cope with multiple conflicting objectives in reasonable solution time. In addition, the operation of a supply chain network is simulated using a discrete-event simulation model to analyze the supply chain network configuration obtained from the tactical planning model. The tactical optimization model can get insights on the best network configuration which combined with the operational simulation model helps realize the practicability of a given configuration and sustainable strategy. Eventually, this study propose an integrated approach to validate the decisions made at the tactical planning level and ensure the feasibility of sustainability goals in both planning levels. This work gives researchers and practitioners insights on how to design/redesign a sustainable supply chain and evaluate supply chain performance in order to achieve sustainability goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".