Insect attack of produce in storage is a major challenge in postharvest handling and contributes to food waste and loss. Attempts to use synthetic chemicals to control this have generated other health and environmental problems. This study aimed to investigate the potential of pyrolyzed cocoa pod husk as a biopesticide (liquid Smoke (LS)) to protect cocoa beans against storage pests. The study was set up in a Completely Randomized Design (CRD) with five treatments (10%; 20%; 50%; 100 % concentrations of LS and 0 %as control). These were applied to the outer surface of mini jute sacks containing cocoa beans. A GCMS analysis of the LS detected 20 compounds. A repellence test of LS showed a very strong repellence effect, repelling almost 90% of the insect population. The feeding deterrence index also caused a reduction in bean damage from 22% in control to 7.65% for 100% LS-treated beans. Weight loss was reduced from 2.13% in control to 0.11 % in the sample treated with 100% LS. The LS treatment did not cause any significant change in the FFA and pH content of the beans. The organoleptic test also proved that LS treatment did not cause any substantial change in the flavor and overall taste and aroma acceptability. Therefore, the liquid smoke can be used for protecting cocoa beans by spraying on the outer part of jute sacks containing beans.

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A mathematical model in the form of a piecewise objective probabilistic optimization approach was proposed in this study as the new decision-making tool to solve supplier selection and inventory management problems. The focus was on price discount and uncertain parameters such as product demand, product defect rate, and late-delivery product rate, which were approached using random variables with some known probability distribution function. Meanwhile, the decision variables contained in the model include the product volume ordered by each supplier at each time for each product type and those stored in the inventory to minimize the total operational cost in the problem. The corresponding optimization problem was solved using a probabilistic programming algorithm via the LINGO optimization tool. The computational simulation showed the proposed model provided the optimal decision, and this means it can be used as a decision-making tool by industrial practitioners.

The decision-makers in manufacturing industries continuously optimize every supply-chain part to achieve optimal profit. In this paper, three crucial activities in the supply chain are observed as profit contributors: supplier selection, inventory management, and production planning. Decision-making support is needed to optimize those activities, especially when prices/costs involve discounts. Therefore, this study aims to develop integrated decision-making support for supplier selection, inventory management, and production planning involving discounted prices. The problem was considered with multi-supplier, multi-raw material, multi-product, and multi-observation time instant. The objective was based on maximizing the profit for the entire activity, i.e., from the raw material procurement and storage to the production. This supply chain was modeled as mixed-integer linear programming with a piecewise objective function representing the profit, which was maximized. It was also modeled with a bunch of constraint functions, including product demand satisfaction. The proposed model was tested with computational simulations using randomly generated supply chain data. The primal simplex algorithm was also employed to calculate the real value of the optimal decision, which was combined with the Branch-and-Bound approach to calculate the appropriate integer solution. The results showed that the optimal decision was achieved, namely (1) The optimal quantity of raw materials ordered to each supplier, (2) The optimal production quantity, and (3) The optimal inventory level, which provided the maximal profit for the whole optimization time horizon. This indicated that the proposed decision-making support model is implementable for industrial decision-makers.