Abstract Production scheduling and maintenance planning are two of the most important tasks that managers face before implementing their decisions on the shop floor. Another issue managers have to keep in mind is the proper allocation of various resources for production. These issues create difficulties in the planning process. In this paper, we propose a bi-objective model that integrates the three aforementioned issues and determines production scheduling, maintenance planning and resource supply rate decisions in order to minimize the make span and total production costs, which include total maintenance, resource consumption and resource inventory costs. Two meta heuristic methods were employed to find approximations of the Pareto optimal front in a permutation flow shop environment: The well-known non-dominated sorting genetic algorithm (NSGA-II) and a bi-objective adaptation of the particle swarm optimization (BOPSO). Additionally, a bi-objective randomized local search (BORLS) heuristic was developed in order to generate multiple non-dominated solutions along its search path. Two sets of computational experiments were conducted. In the first set, the performances of the two meta heuristics with purely random initial populations were compared, with results showing the superiority of BOPSO over NSGA-II. In the second set, the initial populations were enhanced with heuristically generated solutions from BORLS and the performances of BOPSO, NSGA-II and BORLS used as an independent search algorithm, were compared. In this instance, the algorithms performed evenly for large problems, with the BORLS method generating better solutions when total production cost is emphasized.

中文翻译：

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全局资源约束下联合生产和维修调度的双目标优化算法：在置换流水车间问题中的应用

摘要 生产调度和维护计划是管理人员在车间实施决策之前面临的两项最重要的任务。管理人员必须牢记的另一个问题是为生产正确分配各种资源。这些问题在规划过程中造成了困难。在本文中，我们提出了一个双目标模型，它综合了上述三个问题，并确定生产调度、维护计划和资源供应率决策，以最小化制造跨度和总生产成本，包括总维护、资源消耗和资源库存成本。在置换流水车间环境中，采用了两种元启发式方法来寻找帕累托最优前沿的近似值：著名的非支配排序遗传算法 (NSGA-II) 和粒子群优化 (BOPSO) 的双目标适应。此外，还开发了双目标随机局部搜索 (BORLS) 启发式算法，以便沿其搜索路径生成多个非支配解决方案。进行了两组计算实验。在第一组中，比较了纯随机初始种群的两种元启发式算法的性能，结果表明 BOPSO 优于 NSGA-II。在第二组中，初始种群通过 BORLS 启发式生成的解决方案得到增强，并比较了 BOPSO、NSGA-II 和用作独立搜索算法的 BORLS 的性能。在这种情况下，算法在处理大问题时表现均匀，