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<p>In the face of increasing complexity, dynamism, and competition, businesses must enhance operational efficiency, with production schedules being a critical factor affecting costs and service levels. The Job Shop Scheduling Problem (JSSP), which involves allocating ‘<i>N</i>’ jobs to ‘<i>M</i>’ machines, poses significant challenges due to its NP-hard nature and exponential growth in complexity as the problem size increases. Practitioners, particularly in micro, small, and medium enterprises (MSMEs), often rely on manual or ad hoc scheduling to avoid the high costs of advanced software solutions, which leads to suboptimal resource utilization. This study proposes a hybrid Python–Excel–PSO framework for solving deterministic JSSP. This framework embeds PSO in python with an MS Excel data interface that allows users to easily enter job details, machine assignments, start times, and due dates. The proposed approach is compared to traditional heuristics based on metrics such as makespan, flowtime, maximum tardiness, average tardiness, and the number of tardy jobs. Results based on the specific deterministic instances tested indicate that the PSO implementation consistently reduces makespan compared to the selected heuristic dispatching rules. Integrating PSO within an affordable and user-friendly Excel interface expedites optimal scheduling processes, facilitating quicker, productivity-based decisions. This approach enhances resource utilisation and reduces production times, offering a practical solution for MSMEs constrained by limited resources.</p>

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A hybrid python–excel framework with particle swarm optimization for job shop scheduling

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Raghuram Potluru,
  • Narassima Madhavarao Seshadri,
  • Thenarasu Mohanavelu,
  • Yaoxin Wu,
  • Venkata Roshan Mannepu

摘要

In the face of increasing complexity, dynamism, and competition, businesses must enhance operational efficiency, with production schedules being a critical factor affecting costs and service levels. The Job Shop Scheduling Problem (JSSP), which involves allocating ‘N’ jobs to ‘M’ machines, poses significant challenges due to its NP-hard nature and exponential growth in complexity as the problem size increases. Practitioners, particularly in micro, small, and medium enterprises (MSMEs), often rely on manual or ad hoc scheduling to avoid the high costs of advanced software solutions, which leads to suboptimal resource utilization. This study proposes a hybrid Python–Excel–PSO framework for solving deterministic JSSP. This framework embeds PSO in python with an MS Excel data interface that allows users to easily enter job details, machine assignments, start times, and due dates. The proposed approach is compared to traditional heuristics based on metrics such as makespan, flowtime, maximum tardiness, average tardiness, and the number of tardy jobs. Results based on the specific deterministic instances tested indicate that the PSO implementation consistently reduces makespan compared to the selected heuristic dispatching rules. Integrating PSO within an affordable and user-friendly Excel interface expedites optimal scheduling processes, facilitating quicker, productivity-based decisions. This approach enhances resource utilisation and reduces production times, offering a practical solution for MSMEs constrained by limited resources.