An Automatic Scheduling System for Perishable Product’s Supply Chain

Perishable products are a kind of agricultural product that has a very short shelf-life, such as fruits and vegetables. To maintain the quality, as expected by consumers, it needs a system that could be utilized to manage production planning of those perishable products. By utilizing this system should also minimize human error hence reducing the loss. To increase the availability of fruits and vegetables for people who live in cities is by shortening the distance between supplier (farmer) and consumer (citizen). Urban Farming is one way to increase the availability of fruits and vegetables for people who live in city. To create an efective Urban Farming infrastructure, it need a system by utilizing information technology to manage its supply chain. All members of the supply chain network including producers (farmers), distribution center, administrator, retailers, and consumer can access this system. Members of the supply chains can manage every information related to him/her, and able to see the information of the other members when needed. With the integrated information, the planting schedule planning process, the selection of products that will be planted, harvest schedules, and reports related to the fulfillment of the request can be done easily and faste

Multi-objective optimization for the integrated supply and production planning in olive oil industry

Sustainable agriculture, among other things, implies encouraging a diverse and decentralized system of family farms rather than corporate concentration. The challenge is to find a way to organize coalitions improving the food system. The case study that inspired this work originates from Istria, a Croatian region with 25 olive oil producers and about 5,000 mostly small farmers growing and harvesting olives. To account for all the objectives of the agri-food supply chain participants, this work aims to set up a model for its integrated optimization, give its mathematical formulation and suggest a method for solving the problem

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Integrated Production and Distribution Problem with Single Perishable Product

This dissertation investigated the extension of the Integrated Production and Distribution Scheduling Problem (IPDSPP) using a variety of perishable products, applying the JIP principle and make-to-order strategy to integrate the production and distribution schedules. A single perishable product with a constant lifetime after production was used in the model discussed here. The objective of the problem was to find the solution that results in minimal system total transportation costs while satisfying customer demand within a fixed time horizon. In the solution, the fleet size, vehicle route and distribution schedule, plant production batch size and schedule were determined simultaneously. This research employed non-identical vehicles to fulfill the distribution; each allowed multiple trips within the time horizon. Both the single plant and multiple plant scenarios were analyzed in this research. For each scenario, the complexity analysis, mixed integer programming model, heuristic algorithms and comprehensive empirical study are provided

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Data-driven decision support for perishable goods

Retailers offering perishable consumer goods such as baked goods have to make hundreds of ordering decisions every day because they typically operate numerous stores and offer a wide range of products. Daily decisions or even intraday decisions are necessary as perishable goods deteriorate quickly and can usually only be sold on one day. Obviously, decision making concerning ordering quantities is a challenging but important task for each retailer as it affects its operational performance. Ordering too little leads to unsatisfied customers while ordering too much leads to discarded goods, which is a major cost factor. In practice, store managers are typically responsible for decisions related to perishable goods, which is not optimal for various reasons. Most importantly, the task is time consuming and some store managers may not have the necessary skills, which results in poor decisions. Hence, our goal is to develop and evaluate methods to support the decision-making process, which is made possible by advances in information technology and data analysis. In particular, we investigate how to exploit large datasets to make better decisions. For daily ordering decisions, we prose data-driven solution approaches for inventory management models that capture the trade-off of ordering too much or ordering too little such that the profits are maximized. First, we optimize the order quantity for each product independently. Second, we consider demand substitution and jointly optimize the order quantities of substitutable products. For intraday decisions, we formulate a scheduling problem for the optimization of baking plans based on hourly forecasts. Demand forecasts are an essential input for operational decisions. However, retail forecasting research is mainly devoted to weekly data using statistical time series models or linear regression models, whereas large-scale forecasting on daily data is understudied. We phrase the forecasting problem as a supervised Machine Learning task and conduct a comprehensive empirical evaluation to illustrate the suitability of Machine Learning methods. We empirically evaluate our solution approaches on real-world datasets from the bakery domain that are enriched with explanatory feature data. We find that our approaches perform competitive to state-of-the-art methods. Data-driven approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods for decision optimization. Overall, we conclude that data-driven decision support for perishable goods is feasible and superior to alternatives that are based on unreasonable assumptions or established time series models