Optimization of Worker Scheduling at Logistics Depots Using Genetic Algorithms and Simulated Annealing

TL;DR

This paper compares genetic algorithms and simulated annealing for optimizing worker schedules at logistics depots, finding genetic algorithms produce better solutions for minimizing labor days.

Contribution

It introduces a combined approach using genetic algorithms and simulated annealing to optimize worker scheduling with a new integer linear programming model.

Findings

Genetic algorithms outperform simulated annealing in solution quality.

Optimal scheduling minimizes 29,857 person-days.

The model effectively balances labor requirements and worker constraints.

Abstract

This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm. The efficient scheduling of permanent and temporary workers is crucial for optimizing the efficiency of the logistics depot while minimizing labor usage. The study begins by establishing a 0-1 integer linear programming model, with decision variables determining the scheduling of permanent and temporary workers for each time slot on a given day. The objective function aims to minimize person-days, while constraints ensure fulfillment of hourly labor requirements, limit workers to one time slot per day, cap consecutive working days for permanent workers, and maintain non-negativity and integer constraints. The model is then solved using genetic algorithms and simulated annealing. Results indicate that, for this problem, genetic algorithms outperform simulated annealing in terms of solution quality. The optimal solution reveals a minimum of 29857 person-days.