Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Liu, Mi; Yi, Shu Ping; Wen, Peihan

doi:10.1177/0954405416661006

Cited by 14 publications

(7 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the investigated problem is a constrained combination optimization problem that resembles the service composition and optimal selection (SCOS) in cloud manufacturing which is a NP-Hard problem, 31 the problem is also NP-hard that cannot be solved within polynomial-time. 32,33 For this type of optimization issue, various intelligent algorithms can be applied to get a satisfactory result because of their acceptable computational complexity. 34,35 In this section, based on an optimization algorithm – Equilibrium Optimizer (EO), 12 a new algorithm called Enhanced Equilibrium Optimizer (EEO) is presented.…”

Section: Proposed Methodsmentioning

confidence: 99%

An effective multi-part dedicated flow-line reconfiguration model considering the optimal selection of machining process path and machines

Yang

Wang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Multi-part Dedicated Flow-line (MDF) is widely used to produce discrete products for high volumes with low varieties. Since frequent changes occur in customers’ demand and rapid upgrading of machines, the line needs to reconfigure timely to remain competitive. A lot of researchers investigate how to reconfigure the line when facing these changes, and most of them adopts small adjustments such as machine resettlement or buffer size rearrangements to react. However, when great changes occur in product’ machining process or machine’s performance, it’s time to reconfigure the line in a higher level by reselecting machining process of products and/or reselecting machines composition of the line. To fill this research gap, the paper proposes an effective Multi-part Dedicated Flow-line Reconfiguration Model (MDFRM) which can effectively react to great changes occur in products and machines. By optimal selection of Machining Process Path (MPP) for every product and machines composition for the new line, MDFRM can reconfigure MDF with objective of minimizing the products’ cost and capital investment. To solve this problem, an Enhanced Equilibrium Optimizer (EEO) with three layers of code and three steps to handling boundaries and constraints based on Equilibrium Optimizer (EO) is presented. The proposed new model and the designed method are validated by numerical experiments and real case study. This paper will significantly help decision-makers make a scientific decision to optimally reconfigure MDF.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

An effective multi-part dedicated flow-line reconfiguration model considering the optimal selection of machining process path and machines

Yang

Wang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…Other meta-heuristic algorithms have also been considered, such as the particle swarm optimization (PSO) algorithm [25][26][27], honey bee mating optimization (HBMO) algorithm [12], hybrid simulated annealing (SA) and TS algorithm [11], and the ant colony algorithm [28]. Recently, Liu et al [29] proposed a quantum-inspired hybrid algorithm to minimize the makespan of IPPS instances, and outstanding outcomes have been observed. Other approaches in solving the IPPS problem concentrate mainly on agent-based approaches [14,30] and priority dispatch rule (PDR)-based approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Realizing Energy Savings in Integrated Process Planning and Scheduling

2019

View full text Add to dashboard Cite

The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility.

show abstract

“…21 Liu et al researched on a quantum-inspired hybrid algorithm with the objective of minimum makespan for integrated process planning and scheduling problems in dynamic manufacturing systems. 22…”

Section: Literature Reviewmentioning

confidence: 99%

An Internet of Things based framework to enhance just-in-time manufacturing

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Just in time (JIT) manufacturing is a main manufacturing strategy used to enhance manufacturers' competitiveness through inventory and lead time reduction. Implementing JIT manufacturing has a number of challenges, e.g. effective frequent and real-time information sharing and communication between different functional departments, responsive action for adjusting the production plan against the continually changing manufacturing situation. Internet of Things (IoT) technology has the potential to be used for capturing desired data and information from production environment in real time, and the collected data and information can be used for adjusting production schedules corresponding to the changing production environment. This paper presents an IoT based framework to support responsive production planning and scheduling in JIT manufacturing. The challenges of implementing JIT manufacturing are identified first, and then an IoT based solution is proposed to address these challenges. A framework to realise the proposed IoT solution is developed and its implementation plan is suggested based on a case study on automotive harness parts manufacturing. This research contributes knowledge to the field of JIT manufacturing by incorporating the IoT technology to improve the connectivity of production chains and responsive production scheduling capability.

show abstract

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Cited by 14 publications

References 76 publications

An effective multi-part dedicated flow-line reconfiguration model considering the optimal selection of machining process path and machines

An effective multi-part dedicated flow-line reconfiguration model considering the optimal selection of machining process path and machines

Realizing Energy Savings in Integrated Process Planning and Scheduling

An Internet of Things based framework to enhance just-in-time manufacturing

Contact Info

Product

Resources

About