Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

Enhancing nugget size and weldable current range of ultra-high-strength steel using multi-pulse resistance spot welding

ABSTRACT

This paper presents an innovative approach that uses a pulse-profile to improve the welding quality of CP1180 steel in resistance spot welding process. Three pulses with two cooling times were used in the developed multi-pulse welding (MPW) schedule. The experimental results show that the first pulse increases the contact area between the sheets to improve the current flow pattern. The second pulse was designed to extend the sheet-to-sheet contact area and corona bond for preventing rapid nugget growth. Using these designs, the nugget size was maximised through the third pulse. The maximum nugget size using the designed MPW schedule was 18.5% greater than that of the single-pulse welding schedule and the weldable current range was extended by 130%.     

分时电价下多目标绿色可重入混合流水车间调度

针对多目标绿色可重入混合流水车间调度问题（RHFSP）的特点，在机器分配和工序排序的基础上引入分时电价机制，构建了以最小化最大完工时间、总能耗成本和碳排放为目标的绿色调度优化模型，提出了一种改进的多目标文化基因算法（MOMA）来求解该问题，通过数值实验验证了所设计的MOMA算法的可行性。实验结果表明MOMA算法在非劣解的收敛性、多样性和支配性指标方面都显著优于多目标蚁狮优化算法(MOALO)、多目标粒子群优化算法（MOPSO）和带精英策略的非支配排序遗传算法（NSGA-Ⅱ），四种算法的分布性指标无显著差异。所提出的模型能够使企业有效避开高电价时段作业，合理转移用电负荷，达到降低总用电成本和碳排放的目的。     

配网工程建设时间控制

时间控制是运用科学的方法、工具,并综合运用现有资源,在规定时间内取得预期效果的一种控制手段。本文从工程时间管理的概念出发,研究了如何运用时间管理方法实现工程施工工期的管理,讨论了时间管理在工程建设中的重要性,为企业配网工程建设时间控制管理提供参考。     

多因素耦合作用下地下厂房施工进度风险分析及其应用

为深入分析地下厂房施工进度风险因素之间的耦合作用及耦合效应,采用WBS-RBS法识别了影响施工进度的人、物、环境、管理4类风险因素,基于系统动力学方法揭示了同质单因素和异质双-多因素耦合关系,在此基础上建立了同质单因素耦合度模型和异质双-多因素N-K模型,运用蒙特卡罗法模拟了各风险因素对进度影响的概率分布,分析耦合强度。工程实例应用表明,各类风险子系统中存在较大的同质单因素耦合风险,应加强针对性预防和控制;异质双-多因素耦合中,有管理因素参与耦合的风险较大,实际施工中应尽可能减少多因素参与耦合。     

Risk of slaughterhouse workers developing work-related musculoskeletal disorders in different organizational working conditions

Job rotation is defined as workers rotating between tasks with different exposure levels and occupational demands. The aim of the present study was to analyze the risk factors for the development of upper-limb work-related musculoskeletal disorders (UL-WMSDs) in poultry slaughterhouse workers as well as compare the score of the OCRA Checklist in different organizational working conditions. In this cross-sectional study, 118 workers were involved, 68 women (32.3 ± 10.7 years) and 50 men (29.5 ± 10.5 years). Three organizational configurations (“without job rotation”, “with job rotation – tasks >1h” and “with job rotation - tasks <1h”) were evaluated using the OCRA Checklist method, totaling 36 tasks and 28 job rotation schemes composed of 2–3 tasks. The OCRA score of the right upper limbs (17.8 ± 3.2) was significantly higher (p = 0.046) relative to the left upper limbs (16.4 ± 3.6). The median score was significantly lower in the “with job rotation - tasks <1h” condition (18.6) when compared to the “job rotation – tasks >1h” (19.4) (p < 0.001) and the “without job rotation” (19.0) (p = 0.038) conditions. However, there was no significant difference between the condition “with job rotation - tasks >1h” and “without job rotation” (p = 0.155). Thus, job rotations with intervals <1h reduced the risk of developing UL-WMSDs. Job rotations with <1 h intervals in poultry slaughterhouses are recommended along with further studies to verify the effectiveness of rotations with more than two tasks, involving mild or non-repetitive tasks.     

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

This study presents a simulation optimization approach for a hybrid flow shop scheduling problem in a real-world semiconductor back-end assembly facility. The complexity of the problem is determined based on demand and supply characteristics. Demand varies with orders characterized by different quantities, product types, and release times. Supply varies with the number of flexible manufacturing routes but is constrained in a multi-line/multi-stage production system that contains certain types and numbers of identical and unrelated parallel machines. An order is typically split into separate jobs for parallel processing and subsequently merged for completion to reduce flow time. Split jobs that apply the same qualified machine type per order are compiled for quality and traceability. The objective is to achieve the feasible minimal flow time by determining the optimal assignment of the production line and machine type at each stage for each order. A simulation optimization approach is adopted due to the complex and stochastic nature of the problem. The approach includes a simulation model for performance evaluation, an optimization strategy with application of a genetic algorithm, and an acceleration technique via an optimal computing budget allocation. Furthermore, scenario analyses of the different levels of demand, product mix, and lot sizing are performed to reveal the advantage of simulation. This study demonstrates the value of the simulation optimization approach for practical applications and provides directions for future research on the stochastic hybrid flow shop scheduling problem.     

An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms.     

A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems

Airline disruptions incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated aircraft and crew schedule recovery problem. A two stage heuristic algorithm for the integrated recovery problem is proposed. In the first stage, the integrated aircraft recovery and flight-rescheduling model with partial crew consideration is built. This model is based on the traditional multi-commodity network model for the aircraft schedule recovery problem. The objective of this model also includes minimization of the original crew connection disruption. In the second stage, the integrated crew schedule recovery and flight re-scheduling model with partial aircraft consideration is built. We proposed a new multi-commodity model for the crew schedule recovery. The main advantage of such model is that it is much more efficient to integrate the flight-scheduling and aircraft consideration. New constraints are incorporated to guarantee that the aircraft connections generated in the stage 1 are still feasible. Two stages are run iteratively until no improvement can be achieved. Experimental results show that our method can provide better recovery solutions compared with the benchmark algorithms.