Journal of Industrial Engineering and Management Studies

Abstract Perishable food supply chains (PFSCs), particularly in the dairy sector, face significant challenges due to product deterioration, quality degradation, and financial risks associated with distribution delays. Despite extensive research on supply chain optimization, a critical gap remains in accurately modeling the dynamic relationship between product shelf-life and selling price—a factor that significantly impacts revenue and risk assessment in real-world operations. This study addresses this gap by developing a multi-objective optimization model for the dairy supply chain in Iran that incorporates: (1) a novel stepwise pricing mechanism based on remaining shelf-life, capturing revenue loss due to spoilage; (2) financial risk assessment of purchase and distribution operations; (3) transportation planning with vehicle routing; and (4) discount sales policies aligned with product freshness. Given the NP-Hard nature of the problem, NSGA-II and MOPSO algorithms with a modified priority-based encoding-decoding method were employed. Algorithm parameters were systematically tuned using the Taguchi method. The model was validated through a numerical example solved via the LP-metric method, followed by 15 larger test problems to evaluate algorithm performance. Comparative analysis using multiple evaluation metrics—including the number of Pareto-efficient solutions (NPF), maximum spread index (MSI), spacing metric (SM), and computational time—was conducted. The TOPSIS technique was applied to rank algorithm performance, revealing that NSGA-II (weight = 0.6945) significantly outperforms MOPSO (weight = 0.3055) across all problem sizes. The key contributions of this research include: (i) introducing a realistic stepwise pricing function linked to perishability, (ii) integrating financial risk into PFSC optimization, and (iii) providing a robust algorithmic framework for large-scale dairy supply chain problems. These findings offer practical guidance for managers seeking cost-effective, risk-aware, and quality-conscious management of perishable food supply chains.

Abstract This paper aims to confront the uncertainties in the flexible job shop scheduling (FJSS) problem by considering the tax regulations of energy consumption and timely delivery. Uncertainties include all unexpected disruptions such as machine breakdowns, modifications or cancellation of the orders, and receiving new orders that lead to failure in initial scheduling. Two strategies with the energy-saving approach have been proposed based on scheduling repair. Two considered objective functions are to minimize the tax cost on surplus energy consumption and to minimize total cost of jobs tardiness. The problem is described with the parameters and decision variables clearly in the form of MIP model. Moreover, the proposed model is investigated using data of a real case study in a company based on casting processes. Since the problem is well known strongly NP-hard, a new approach is introduced based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find proper solutions for decision-makers. The computational results show that the proposed model and solution approach repairs properly the original scheduling and could improve the Pareto front comparing with the original scheduling. Due to the result, two proposed strategies could reduce total cost of jobs tardiness more than 47.56% compared with the original scheduling in eight different cases. It could also improve the second objective more than 56.91%. This approach will help the manufacturing industry managers, especially in make-to-order (MTO) systems with high-powered machines to respond rapidly to unexpected disruptions with the lowest energy consumption and tardiness penalty.

Abstract In this research, we study the multi-skill resource-constrained project scheduling problem, where there are generalized precedence relations between project activities. Workforces are able to perform one or several skills, and their efficiency improves by repeating their skills. For this problem, a mathematical formulation has been proposed that aims to optimize project completion time, reworking risks of activities, and costs of processing the activities, simultaneously. A modified version of the Pareto Archived Evolution Strategy (MV-PAES) is developed to solve the problem. Contrary to the basic PAES, this algorithm operates based on a population of solutions. For the proposed method, we devised crossover and mutation operators, which strengthen this algorithm in exploring solution space. Comprehensive numerical tests have been conducted to evaluate the performance of the MV-PAES in comparison with two other meta-heuristics. The outputs show the excellence of the MV-PAES in comparison with other methods. A real-world software development project has been studied to demonstrate the practicality of the proposed model for real-world environment. The influence of competency evolution has been investigated in this case study. The results imply that the competency evolution has a considerable impact on the objective function values.