Ergonomics-aware operator-task allocation and scheduling in flexible job shop systems

Enhanced NSGA-II for multi-objective energy-saving flexible job shop scheduling

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, we consider time to be a triangular fuzzy number and apply an expert system to infer cost. The objective is to find a path minimising both the time and cost criteria, aggregately. Since time and cost have different scales, a normalisation procedure is proposed to remove the scales. The model being bi-objective, the analytical hierarchy process weighing method is applied to construct a single objective. Finally, a dynamic programming approach is presented for computing a shortest path in the network. The efficiency of the proposed approach is illustrated by a numerical example.

Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize job sequencing and machine selection while ignoring the impact of AGV transportation, resulting in suboptimal scheduling solutions and even difficulties in implementation. To address this issue, this paper formulates a cooperative scheduling model by introducing the AGV scheduling problem into the classical FJS scheduling problem, abbreviated as the FJS-AGV problem, with the objective of minimizing the makespan. With respect to the FJS-AGV problem, a heuristic-assisted deep Q-network (HA-DQN) algorithm is proposed, which leverages heuristic rules to enable the decision agent to perform multiple actions at each decision point, which includes determining the responses to the following questions: Which operation should be processed next? On which machine? By which AGV? This decision mechanism enables the agent to make more informed decisions, leading to improved performance and resource allocation in the FJS-AGV system. The practicability of the proposed FJS-AGV model and the efficiency of the HA-DQN algorithm in solving the FJS-AGV problem are verified through various international benchmarks. Specifically, when solving instances in a large benchmark, the HA-DQN algorithm yields a significant 12.63% reduction in makespan compared with that when traditional heuristics are employed.

Allocation problem has always been one of the fundamental issues of building the applications in real-time computing systems. For real-time applications, the allocation problem should directly address the issues of task and communication scheduling. In this context, the allocation of tasks has to fully utilize the available processors and the scheduling of tasks has to meet the specified timing constraints. Clearly, the execution of tasks under the allocation and schedule has to satisfy the precedence, resources, and other synchronization constraints among them. Recently, the timing requirements of the real-time systems emerge that the relative timing constraints are imposed on the consecutive executions of each task and the inter-task temporal relationships are specified across task periods. In this paper we consider the allocation and scheduling problem of the periodic tasks with such timing requirements. Given a set of periodic tasks, we consider the least common multiple (LCM) of the task periods. Each task is extended to several instances within the LCM. The scheduling window for each task instance is derived to satisfy the timing constraints. We develop a simulated annealing algorithm as the overall control algorithm. An example problem of the sanitized version of the Boeing 777 Aircraft Information Management System is solved by the algorithm. Experimental results show that the algorithm solves the problem in a reasonable time complexity.

Platform labor, mediated through digital platforms and governed by algorithmic management, has transformed contemporary work arrangements, offering both flexibility and precarity. This paper examines the multifacetednature of platform work, highlighting the diverse tasks, work structures, and algorithmic oversight shaping worker experiences. Empirical evidence suggests ambivalent effects on worker well-being, influenced by platform governance, regulation, task allocation, and individual skill levels. Collective representation, including unions and cooperative platforms, remains challenged by uncertain employment status and dispersed labor structures. Regulatory approaches at national, supranational, and transnational levels attempt to address algorithmic management risks, with policy implications for worker protection, equitable remuneration, and digital labor rights. Methodological considerations and case studies across transportation, cleaning, and generalist task platforms underscore both the social and economic significance of platform labor. Finally, gaps in knowledge highlight the need for interdisciplinary research on algorithmic management, worker well-being, and effective regulatory frameworks to guide the evolving platform economy. Keywords: Platform Labor, Algorithmic Management, Worker Well-Being, Labor Regulation, and Collective Representation.

Integrating robotics in construction works has the potential to address persistent challenges such as labor shortages, health risks, and low productivity. This paper proposes a systematic task planning approach for human-robot collaboration (HRC) in structure assembly, comprising a robotic potential scoring-based task classification system and a constraint programming-based task allocation and sequencing optimization model. Based on key factors such as component properties, connection methods, robotic payload capacities, material storage layouts, and workspace safety, the task classification system provides a systematic approach to classifying construction tasks. To obtain optimal task allocation and sequencing, an extended flexible job shop scheduling problem (FJSSP) based optimization model integrates human fatigue into the objective function to balance operational efficiency and worker well-being. A timber frame assembly case study with different HRC configurations and storage arrangements was conducted to evaluate the performance in enhancing makespan and reducing human fatigue. This work establishes a robust foundation for optimizing HRC in structure assembly, paving the way for more effective and human-centric practices in the industry. • Proposed a task classification system based on robotic potential evaluation • Developed a constraint programming model optimizing efficiency and human fatigue • Found high-capability robots enhance flexibility in collaboration with human worker • Guided design and application of robot deployment in structural assembly • Provided task sequencing and allocation combinations with varying emphases

Chip-multiprocessors represent a dominant new shift in the field of processor design. Better utilization of such technology in the real-time context requires coordinated approaches to task allocation, scheduling, and synchronization. In this paper, we characterize various scheduling penalties arising from multiprocessor task synchronization, including (i) blocking delays on global critical sections, (ii) back-to-back execution due to jitter from blocking, and (iii) multiple priority inversions due to remote resource sharing. We analyze the impact of these scheduling penalties under different execution control policies (ECPs) which compensate for the scheduling penalties incurred by tasks due to remote blocking. Subsequently, we develop a synchronization-aware task allocation algorithm for explicitly accommodating these global task synchronization penalties. The key idea of our algorithm is to bundle tasks that access a common shared resource and co-locate them, thereby transforming global resource sharing into local sharing. This approach reduces the above-mentioned penalties associated with remote task synchronization. Experimental results indicate that such a coordinated approach to scheduling, allocation, and synchronization yields significant benefits (as much as 50% savings in terms of required number of processing cores). An implementation of this approach is available as a part of our RT-MAP library, which uses the pthreads implementation of Linux-2.6.22.

This paper proposes control methods in a flexible job shop in the aim of completing the jobs at predetermined times. The predetermined times can be the times when the jobs can start undergoing processing by the next production process on the production line or the times when the jobs should be delivered to the customer or the next party on the supply chain. By completing the jobs at such times, the flow of jobs or goods between production processes or supply-chain parties can be smoothed and the intermediate inventory of finished jobs or goods reduced. The proposed control methods are performed online, featuring high speed, requirement of little beforehand information, and the ability to adapt to disturbances. The methods include a dispatching rule which sorts the jobs based on the forecast waiting time of each job and a decision making process which gives priority to the jobs which are waiting for the processing of their final operations. Computer simulations are conducted using the data of a real factory and the results show that the methods are higher in performance than the existing methods for the objective.

Understanding risk and protective factors to UK railway workers' mental wellbeing during the COVID-19 pandemic: a cross-sectional survey

Motivated by Boeing 777 fully autonomous upright build project, orchestration of human and robotic agents are studied. Tasks must be precisely allocated, sequenced, and coordinated among agents subject to temporal and spatial constraints. The problem is formulated as a flexible job shop with sequence-dependent setup to capture heterogeneous agents and travel time. Two exact central approaches are proposed: a mixed integer programming and a constraint programming, and tested for real-time perspective. The computational study demonstrates the proposed method can generate optimal schedules up to 100 agents with 1000 subtasks that requires 10 subtasks per agent on average, within 183 seconds, a substantial improvement over all other benchmark approaches in the literature.

To realize concurrent part processing and assembly process effectively in certain production scenarios, a method of flexible assembly job shop scheduling considering assembly sequence variation under dual-resource constraints (FAJSS-ASVDRC) including machine and worker is proposed. By analyzing the problems in FAJSS-ASVDRC, an integrated scheduling mathematical model that considers the interactive effects between part processing sequence and assembly sequence under dual-resource constraints is established, aiming to minimize the total production completion time, the total inventory time and the total labor cost in the production process. Based on above, a discrete barnacles mating optimizer (DBMO) algorithm is proposed to solve the FAJSS-ASVDRC problem. In DBMO, a three-layer hybrid chromosome encoding structure is designed, two reproduction methods are proposed by mimicking the reproduction way of barnacles to generate offspring chromosomes, and a mutation operator based on the assembly operations is designed to ensure the diversity of the solutions. In the case study, the Taguchi method is used to obtain the best combination of parameters for the DBMO algorithm. Then, the proposed FAJSS-ASVDRC method is verified to be able to improve the production efficiency more effectively compared to traditional methods, the three objective function values are reduced by 33.7%, 76.8% and 9.8%, respectively. Additionally, the effectiveness of DBMO algorithm is verified to outperform other algorithms in more than 75% cases with different scale in solving the FAJSS-ASVDRC problem.

With the surging energy cost and environmental impacts, strategies to achieve energy-efficient production have attracted increasing concerns of the manufacturing enterprises. For the fact that most manufacturing systems operate in a dynamic and nondeterministic environment, rescheduling strategies may be beneficial as it serves for adaption of initial schedule to dynamic events. Besides that, the development of modern information technology in manufacturing practice enables the flexibility of production toward alternative process plans. However, very little research has focused on the rescheduling problem integrated with process planning for energy saving. Hence, this work undertakes this challenge by proposing rescheduling decision mechanisms in response to two typical dynamic events with alternative process plans for energy-efficient flexible job shops. More specifically, by modeling the energy consumption of the flexible manufacturing system, the problem is first formulated as a mixed-integer programming optimization model. Rescheduling mechanisms for both new job arrivals and machine tool breakdowns are then designed, based on which a rescheduling algorithm is proposed in the form of a heuristic framework. The significance of the proposed algorithm is exemplified by a comparative case study under various scenarios. Note to Practitioners—The complex process plan selection and dynamic events in flexible job shops make the energy-aware schedule decision a challenging problem. Rescheduling addresses this issue, however, most existing rescheduling algorithms assume that only one process plan is given with a predetermined process route and machine tool allocation. This reduces the effectiveness of energy-saving for such a dynamic and flexible manufacturing system. This article, for the first time, to the best of our knowledge, proposes rescheduling decision mechanisms to generate schedules that can adapt to dynamic events and are energy-efficient with process plan flexibility. It may assist decision-makers to provide more practical and applicable energy-efficient schedules for flexible job shops, particularly when dynamic variations of the production environment occurred frequently.

Task allocation and scheduling models for distributed digital signal processing are presented. The notions of block-type and stream-type tasks in signal processing application are introduced, and models for sequential and parallel I/O are presented. By extending the traditional models, more accurate schedules can be obtained. Those models can be further enhanced by allowing additional restrictions on the number of parallel I/O ports and the amount of parallelism on memory access. The deterministic nature of digital signal processing algorithms allows for more computationally intensive and accurate task allocation techniques to be performed at compile time. By applying a branch and bound algorithm, the task allocation problem can easily be solved for a variety of scheduling models and various system restrictions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, we tackle the problem of minimizing the power consumption of multiple-processors-core system using multiple variable supply voltages. Given a set of tasks and their dependency, and a set of target processors, we propose a method which carries out task allocation on processors, task scheduling and voltage assignment for each task concurrently. The problem is formulated as a mixed integer nonlinear programming (MINLP) optimization problem and optimal solution can be obtained. The MINLP problem is solved by the branch and bound algorithm. To reduce the run time, we propose two modifications of the formulation. Experimental results show that significant power reduction is achieved using the proposed approach.

Background The creation of a toolkit providing modeling of a flexible production system (FPS) by means of management system is a complex multi-stage process and involves a number of tasks. Aims and Objectives Creation of a base for knowledge of the production type for the control system (CS) of FPS and its elements. The classification of products used in the management system is specified. For this purpose, the FPS is analyzed as a hierarchical system and the production system is structured according to the levels. To create a knowledge base, an expert classification approach was used. Results Based on the results of the studies carried out on the development of the system for CS FPS control system modeling, there were proposed: 1. General structure of the CS FPS modeling tool; 2. The general scheme of automation at the level of subsystems to ensure the process of managing the FPS.