A scheduling policy strongly influences the performance of a manufacturing system. However, the design of an effective scheduling policy is complicated and time consuming due to the complexity of ...each scheduling decision, as well as the interactions among these decisions. This paper develops four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments. In addition to using three existing search strategies, nondominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2, and harmonic distance-based multi-objective evolutionary algorithm, to develop new MO-GPHH methods, a new approach called diversified multi-objective cooperative evolution (DMOCC) is also proposed. The novelty of these MO-GPHH methods is that they are able to handle multiple scheduling decisions simultaneously. The experimental results show that the evolved Pareto fronts represent effective scheduling policies that can dominate scheduling policies from combinations of existing dispatching rules with dynamic/regression-based due-date assignment rules. The evolved scheduling policies also show dominating performance on unseen simulation scenarios with different shop settings. In addition, the uniformity of the scheduling policies obtained from the proposed method of DMOCC is better than those evolved by other evolutionary approaches.
As environmental awareness grows, sustainable scheduling is attracting increasing attention. The purposes of this paper are obtain the lower bound of energy-efficient flexible job shops with machine ...selection, job sequencing, and machine on-off decision making via a new mathematical model and to discover more energy-efficient rules with easy implementation in real practice via an efficient Gene Expression Programming (eGEP) algorithm. This paper first formulates a novel mixed-integer linear mathematical model to achieve effective machine selection, job sequencing, and machine off-on decision making. Then for the purpose of avoiding the empirical combination, five attributes exerting direct influence on the total energy consumption are extracted and consequently involved in the evolutionary process of eGEP. Furthermore, diversified rule mining operations with multi-gene representation and self-study are designed to enhance the search space and solutions quality. And, unsupervised learning is utilized in which global best and current worst are set to guide evolution direction since the learning progress has no prior knowledge. Experimental results show that machine off-on decisions efficiently reduce the total energy consumption; and, the discovered rules reach the lower bound calculated by GAMS/CPLEX in small problems and have significant superiority over other dispatching rules in energy saving.
•Effective machine selection, job sequencing and machine on-off decision making is achieved concurrently.•A novel general nonlinear mixed integer mathematical model is proposed and linearized for energy-efficient flexible jobshops.•Multi-gene representation and self-study are appropriately designed to enhance the search space and quality of solutions.•Unsupervised learning avoids the empirical combination of simple rules and overcomes the deficiency of no prior knowledge.
In this paper, we discuss scheduling problems for flexible job shops that contain identical and unrelated parallel machines. The performance measure is the total weighted tardiness (TWT). The main ...contribution is an efficient iterative local search approach for flexible job shops with TWT measure using the disjunctive graph representation. Escaping from local optima is ensured by using the simulated annealing acceptance criterion. The assessment of moves is based on a dynamic topological ordering of the disjunctive graph. We hybridize the shifting bottleneck heuristic (SBH) with the proposed local search approach and a variable neighborhood search (VNS) approach. In addition, list scheduling techniques for a variety of due date-oriented dispatching rules are discussed. The proposed heuristics are compared by computational experiments for problem instances available in the literature and a set of new large-size problem instances. The local search scheme is able to determine high-quality solutions within a short amount of computing time. If the processing flexibility increases, i.e. the number of parallel machines, the improvement of the advanced techniques compared to list scheduling techniques decreases. In case of identical parallel machines, the SBH-type algorithms outperform the local search scheme with respect to TWT. However, the SBH requires more computing time.
•Scheduling problems for flexible job shops with TWT objective are considered.•We propose an efficient local search (LS) approach.•The shifting bottleneck heuristic is hybridized with LS and VNS schemes.•Computational experiments demonstrate that the heuristics perform well.
•QPSO is proposed to solve FJSS problem to improve global search ability.•Mutation has been introduced in QPSO to avoid premature convergence.•QPSO outperforms other algorithms in most of the ...benchmark instances.
A flexible job shop scheduling problem (FJSP) is an extension of the classical job shop problem (JSP) where operations are allowed to be processed on any among a set of available machines at a facility. For such problems, it is not always possible to find optimal solution in a reasonable time. Hence, a large variety of heuristic procedures such as dispatching rules, local search, and meta-heuristic procedures are used to solve such problems and generate approximate solutions close to the optimum with considerably less computational time. PSO is an effective algorithm which gives quality solutions in a reasonable computational time and requires less number of parameters to be tuned in comparison to other evolutionary meta-heuristics. However, PSO has an inherent drawback of getting trapped at local optimum due to large reduction in velocity values as iteration proceeds and poses difficulty in reaching at best solution. This drawback can be effectively addressed using quantum-behaved particle swarm optimization (QPSO) due to its advanced global search ability. Mutation, a commonly used operator in genetic algorithm, has been introduced in QPSO so that premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. The performance of schedules is evaluated in terms of total completion time or makespan (Cmax). The results are compared with different well-known algorithms used for the purpose from open literature. The results indicate that the proposed QPSO algorithm is quite effective in reducing makespan because small value of relative deviation is observed.
In this work, we develop a novel mathematical formulation for the energy-efficient flexible job-shop scheduling problem using the improved unit-specific event-based time representation. The flexible ...job-shop is represented using the state-task network. It is shown that the proposed model is superior to the existing models with the same or better solutions by up to 13.5 % energy savings in less computational time. Furthermore, it can generate feasible solutions for large-scale instances that the existing models fail to solve. To efficiently solve large-scale problems, a grouping-based decomposition approach is proposed to divide the entire problem into smaller subproblems. It is demonstrated that the proposed decomposition approach can generate good feasible solutions with reduced energy consumption for large-scale examples in significantly less computational time (within 10 min). It can achieve up to 43.1 % less energy consumption in comparison to the existing gene-expression programming-based algorithm.
•A novel unit-specific event-based mathematical formulation is developed.•A grouping-based decomposition method is proposed to solve industrial scale problems.•The proposed formulation is more robust and efficient than the existing models.•A significant reduction in energy consumption by up to 43.1 % is achieved.
•Coffee shops have the potential to play various role in urban spaces.•Coffee shops can facilitate connections, foster community development and enrichment.•Community connections can be facilitated ...through businesses and consumer activity.•Contemporary notions of community need elaboration.
Coffee shops have been described as ‘third places’ in urban lives separate from the work and home, providing places for people to meet, relax and develop connections. However, the growing presence of coffee shops in the urban landscape has meant that they increasingly take on a wider range of roles, becoming spaces of both leisure and work but also providing spaces of sociality in which people can develop connections, and potentially communities. The roles of coffee shops in five cities in England are explored in order to consider how they can be understood not only as spaces of consumption, but spaces which facilitate connection in increasingly isolated urban lives, and generate the potential for communities to develop. By understanding the varied ways in which businesses and consumers co-create these spaces, it may be possible to increase their potential as ‘spaces of community’.
Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing ...system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
Most shops in practice are constrained by more than one resource. Consequently, a large body of literature on dual resource constrained shops has emerged. This research typically focuses on worker ...assignment rules, with attention being on when and where to move workers. In contrast, the decision concerning who to reallocate to a station has received limited attention. The limited prior work assumes workers are assigned to a new station as soon as they become available or seeks to minimise the risk of worker idleness. Using simulation, we question this assumption and show that it can be beneficial to introduce additional worker idleness to ensure workers only work at their most efficient station(s). In general, it is less likely that there are several workers available for one station than it is for there to be multiple stations available for one worker. Consequently, the Who Rule is used less frequently than the Where rule and has less of an impact on performance. Finally, considering the criticality of work orders as part of the Where Rule is important in assembly shops; but if labour is heterogeneous then the focus should be on efficiency. The findings have important implications for research and practice.
Workload control (WLC) is a lean oriented system that reduces queues and waiting times, by imposing a cap to the workload released to the shop floor. Unfortunately, WLC performance does not ...systematically outperform that of push operating systems, with undersaturated utilizations levels and optimized dispatching rules. To address this issue, many scientific works made use of complex job-release mechanisms and sophisticated dispatching rules, but this makes WLC too complicated for industrial applications. So, in this study, we propose a complementary approach. At first, to reduce queuing time variability, we introduce a simple WLC system; next we integrate it with a predictive tool that, based on the system state, can accurately forecast the total time needed to manufacture and deliver a job. Due to the non-linearity among dependent and independent variables, forecasts are made using a multi-layer-perceptron; yet, to have a comparison, the effectiveness of both linear and non-linear multi regression model has been tested too. Anyhow, if due dates are endogenous (i.e. set by the manufacturer), they can be directly bound to this internal estimate. Conversely, if they are exogenous (i.e. set by the customer), this approach may not be enough to minimize the percentage of tardy jobs. So, we also propose a negotiation scheme, which can be used to extend exogenous due dates considered too tight, with respect to the internal estimate. This is the main contribution of the paper, as it makes the forecasting approach truly useful in many industrial applications. To test our approach, we simulated a 6-machines job-shop controlled with WLC and equipped with the proposed forecasting system. Obtained performances, namely WIP levels, percentage of tardy jobs and negotiated due dates, were compared with those of a set classical benchmark, and demonstrated the robustness and the quality of our approach, which ensures minimal delays.
Different from the classical job shop scheduling, the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) should deal with job sequence, machine assignment and worker assignment ...all together. In this paper, a knowledge-guided fruit fly optimisation algorithm (KGFOA) with a new encoding scheme is proposed to solve the DRCFJSP with makespan minimisation criterion. In the KGFOA, two types of permutation-based search operators are used to perform the smell-based search for job sequence and resource (machine and worker) assignment, respectively. To enhance the search capability, a knowledge-guided search stage is incorporated into the KGFOA with two new search operators particularly designed for adjusting the operation sequence and the resource assignment, respectively. Due to the combination of the knowledge-guided search and the smell-based search, global exploration and local exploitation can be balanced. Besides, the effect of parameter setting of the KGFOA is investigated and numerical tests are carried out using two sets of instances. The comparative results show that the KGFOA is more effective than the existing algorithms in solving the DRCFJSP.
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