A Multi -Objective Optimization Model to Support Freshly Cut Vegetable Processing Decisions

This study presents a multi -objective optimization approach for decision making in fresh-cut vegetable processing, optimizing processing times and costs through the selection of alternative processes at various stages of the production. Despite the limited attention given to the fresh-cut vegetable industry, particularly in applying multi -objective optimization methods to support processing decisions, this study addresses the research need. The stages of fresh- cut vegetable processing, including peeling, cutting, washing, and packing, offer multi plealternative methods with varying costs and processing times. The problem is formulated as an integer bi-objective combinatorial optimization model aimed at optimizing total processing time and cost. Two algorithms, the discrete non-dominating sorting genetic algorithm- II (NSGA II) and the discrete non- dominated sorting particle swarm algorithm (NPSO), were applied to explore their complementary algorithmic performance. The local search behaviour of NSGA- II was enhanced through several innovative local search operators including crossover, and mutation operators, while various position and velocity update operators were used in NPSO. Both primary and secondary data were utilised in estimating the process parameters of each alternative processing methods. The results showed that NPSO exhibited more robust convergence, while NSGA-II produced a greater number of solutions in the Pareto front. Keywords: Evolutionary meta-heuristics techniques; multi -objective optimization; NSGA-II; Particle swarm optimization; Process selection decision