Customer satisfaction is a prevalent issue amongst manufacturing enterprises. Multi-agent scheduling models aim to optimise the given criteria for improving customer satisfaction by fulfilling the customisation requirements. An investigation is executed on a bi-agent flowshop scheduling model, where a mass of tasks maintained by two competitive agents share a group of successive processors over time. The objective is to determine a feasible schedule that minimises the weighted combination of makespans belonging to two different agents. Asymptotic and worst-case analyses are conducted on a class of dominant-agent-based heuristics proposed to find approximate solutions for large-scale instances. An effective branch and bound algorithm is presented to achieve optimal solutions for small-scale instances, where the release-date-based branching rules and the preemption-based lower bounds significantly speed up the convergence of the proposed algorithm. A discrete artificial bee colony algorithm is introduced to find high-quality solutions for medium-scale instances. Extensive computational experiments are conducted to reveal the effectiveness of the proposed algorithms.

客户满意度是制造企业普遍关注的问题。多代理调度模型旨在通过满足定制要求来优化给定标准以提高客户满意度。对双代理流水车间调度模型进行了调查，其中由两个竞争代理维护的大量任务随着时间的推移共享一组连续的处理器。目标是确定一个可行的计划，以最小化属于两个不同代理的制造期的加权组合。对一类基于主导代理的启发式算法进行渐近和最坏情况分析，以找到大规模实例的近似解。提出了一种有效的分支定界算法，以实现小规模实例的最优解，其中基于发布日期的分支规则和基于抢占的下界显着加快了所提出算法的收敛速度。引入离散人工蜂群算法以找到中等规模实例的高质量解决方案。进行了广泛的计算实验以揭示所提出算法的有效性。

客户满意度是制造企业普遍关注的问题。多代理调度模型旨在通过满足定制要求来优化给定标准以提高客户满意度。对双代理流水车间调度模型进行了调查，其中由两个竞争代理维护的大量任务随着时间的推移共享一组连续的处理器。目标是确定一个可行的计划，以最小化属于两个不同代理的制造期的加权组合。对一类基于主导代理的启发式算法进行渐近和最坏情况分析，以找到大规模实例的近似解。提出了一种有效的分支定界算法，以实现小规模实例的最优解，其中基于发布日期的分支规则和基于抢占的下界显着加快了所提出算法的收敛速度。引入离散人工蜂群算法以找到中等规模实例的高质量解决方案。进行了广泛的计算实验以揭示所提出算法的有效性。