Marcela C. González-Araya

Operational research models applied to the fresh fruit supply chain

The fresh fruit supply chain is characterized by long supply lead times combined with significant supply and demand uncertainties, and relatively thin margins. These challenges generate a need for management efficiency and the use of modern decision technology tools. We review some of the literature on operational research models applied to the fresh fruit supply chain. It is an attempt to gain a better understanding of the OR methods used in the revised papers apparently independent and oriented towards problem solving rather than theory developing. We conclude by outlining what we see as some of the significant new problems facing the industry like the lack of holistic approaches for the design and management of fresh FSC. Finally, some future research directions are indicated.

Joint carbon footprint assessment and data envelopment analysis for the reduction of greenhouse gas emissions in agriculture production

Operations management tools are critical in the process of evaluating and implementing action towards a low carbon production. Currently, a sustainable production implies both an efficient resource use and the obligation to meet targets for reducing greenhouse gas (GHG) emissions. The carbon footprint (CF) tool allows estimating the overall amount of GHG emissions associated with a product or activity throughout its life cycle. In this paper, we propose a four-step method for a joint use of CF assessment and Data Envelopment Analysis (DEA). Following the eco-efficiency definition, which is the delivery of goods using fewer resources and with decreasing environmental impact, we use an output oriented DEA model to maximize production and reduce CF, taking into account simultaneously the economic and ecological perspectives. In another step, we stablish targets for the contributing CF factors in order to achieve CF reduction. The proposed method was applied to assess the eco-efficiency of five organic blueberry orchards throughout three growing seasons. The results show that this method is a practical tool for determining eco-efficiency and reducing GHG emissions.

Harvest Planning in Apple Orchards Using an Optimization Model

One of the main factors affecting the quality of the apple is the state of maturity at which it is harvested. To help planning agricultural work in apple orchards, this study proposes an optimization model, working through harvest time windows, and that incorporates the fruit ripeness. The model seeks to minimize labor costs, equipment use, and loss of fruit quality, in order to meet the demand of packing plants, considering their processing capacities as well as the production in orchards. This model on three apple orchards in the Maule Region, Chile, led to a 16 % decrease in both labor costs and loss of income for harvesting fruits with poor quality.

A Dwell Time-based Container Positioning Decision Support System at a Port Terminal.

In this article, a methodology as well as a decision support system for the container storage assignment at a yard of a container terminal is proposed. The motivation of the proposed methodology are the cases of container terminals where inland flows present high levels of uncertainty and variability. This situation is typical of ports in developing countries such as is the case in Latin America where due to lack of automation, there are many paper-based procedures and little coordination with the hinterland. The proposed methodology is based on a dwell time segregated storage policy, considering only import containers (due to the difficulty to determine segregation criteria for this type of containers). Dwell times are discretized in order to determine dwell time classes or segregations, so that containers of the same segregation are assigned to close locations at the yard. As a case study, the port of Arica in Chile is considered. A discrete-event simulation model is also proposed to estimate potential benefits of the proposed methodology. Numerical results for the case study show a good performance, with potential reduction of the rehandles incurred.

Supporting Harvest Planning Decisions in the Tomato Industry.

Tomato is a raw material that easily deteriorates once harvested and loaded on trucks, losing juice and flesh. Therefore, the reduction of trucks’ waiting times in the receiving area of a processing plant can allow reducing tomato waste. In this article, we develop a model that aims to keep a continuous flow of fresh tomato to a paste processing plant and to decrease trucks’ waiting times in the plant receiving area. The model is used in a real case of a tomato paste company. The obtained solutions present a better allocation of the harvest shifts, allowing more uniform truck arrivals to the plant during the day. Therefore, trucks waiting times are reduced, decreasing raw material deterioration.

Applying Mathematical Programming to Planning Bin Location in Apple Orchards.

In Chile, it has been observed that there is downtime during the apple harvest season. This is largely due to the long distances that the workers must cover and the lack of bins in the orchards. Currently, the administrators do not use methods that enable them to estimate the number of bins required or where they should be located. Taking these observations into consideration, this research paper proposes a plan for bin placement in apple orchards by applying a location model with the objective of diminishing distances covered by the harvest personnel. With data from an orchard in the Maule Region of Chile, the number of bins to be used is calculated taking into consideration the particular surface characteristics of the plantation and the apple variety maturity indicators. For the spatial distribution of the bins, the capacitated p-median was used, because better results were obtained with it in terms of reducing travel distance during the harvest and the ease of implementing the solutions.

A Mixed Integer Linear Program for Operational Planning in a Meat Packing Plant

This paper reports a mixed integer linear programming model to support the planning at an operational level in a meat packing plant. The deterministic formulation considers multi-products (yielded from different cutting patterns applied to the carcasses), multi-periods, a batch quality distribution on carcasses and perishability. The perishability of the product is modeled by the inclusion of disaggregated inventory decision variables that take into account a given maximum number of days for fresh product. The main contribution of the present work is to develop an optimization model in a real tactical planning problem. Also we develop a sensitivity analysis on the quality of the carcasses, subject to large variability. We present here two different scenarios, comparing them to asses their economical impact.

An Optimization Model for Planning Operations in a Meat Packing Plant

This paper presents an optimization model for supporting planning operations in a meat packing plant. The production system considers processing raw material (carcasses) by applying cutting patterns in order to meet a given demand of different products. Major decisions in the planning problem include the number of times each cutting pattern is applied on the available carcasses, the total yield per product and its corresponding levels of inventory at each time period; as a result a Mixed Integer Linear Programming Model (MILP) is proposed. The main contribution is to give an optimal decision that maximizes the economic profit and thereby reducing the shortages for products with the highest profits and the inventory for those with low level demand, taking into account the perishability of the products, labour capacities, and the fact that it may exists different types of carcasses with different yields.