Flexible job shop scheduling problem (FJSP) is an NP-hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid tackle this problem. In study, existing solution methods for FJSP recent literature are classified into exact algorithms, heuristics meta-heuristics, reviewed comprehensively. Moreover, real-world also introduced. Finally, development trends manufacturing industry analysed,...

This paper investigates an energy-efficient hybrid flowshop scheduling problem with the consideration of machines different energy usage ratios, sequence-dependent setups, and machine-to-machine transportation operations. To minimize makespan total consumption simultaneously, a mixed-integer linear programming (MILP) model is developed. solve this problem, three-stage multiobjective approach based on decomposition (TMOA/D) suggested, in which each solution bound main weight vector set its...

Purpose The purpose of this paper is to establish a new model for non‐equidistance sequence and research affine properties the model. Design/methodology/approach Generalized GM(1,1) put forward based on generalized accumulated generating operation (AGO) theory, particle swarm optimization used solve parameters model, then are researched matrix analysis. Findings results convincing: simulation prediction precisions raised greatly, it proved that transformation has same simulative accuracy...

Dynamic scheduling is one of the most important key technologies in production and flexible job shop widespread. Therefore, this paper considers a dynamic problem considering setup time random arrival. To solve problem, framework based on improved gene expression programming algorithm proposed to construct rules. In framework, variable neighborhood search using four efficient structures combined with algorithm. And, an adaptive method adjusting recombination rate transposition evolutionary...

Abstract Pharmaceutical distribution routing problem is a key for pharmaceutical enterprises, since efficient schedules can enhance resource utilization and reduce operating costs. Meanwhile, it complicated combinatorial optimization problem. Existing research mainly focused on delivery route lengths or costs minimization, while seldom considered customer priority carbon emissions simultaneously. However, considering the simultaneously will not only help to satisfaction, but also emissions....

Job shop scheduling problem (JSSP) is a typical in manufacturing. Traditional methods fail to guarantee both efficiency and quality complex changeable production environments. This paper proposes an end-to-end deep reinforcement learning (DRL) method address the JSSP. In order improve of solutions, network model based on transformer attention mechanism constructed as actor enable DRL agent search its solution space. The Proximal policy optimization (PPO) algorithm utilized train learn...

Steelmaking-refining-Continuous Casting (SCC) is a key process in iron and steel production. SCC scheduling to determine an optimal schedule for the process, which worldwide important problem. High-quality methods will help allocate production resources effectively increase productivity. However, dynamic events (e.g. machine breakdown) may happen realistic make inexecutable or not optimal. In this case, rescheduling essential order obtain new suitable current environments. The can be modeled...

Permutation flow shop scheduling problem (PFSP) exists widely in the manufacturing system. The traditional researches often considered setup time as one part of processing time. However, this assumption does not meet requirement “more varieties, small batches” production model. Therefore, PFSP with becomes an urgent to be solved. This paper proposes effective hybrid algorithm combining genetic and variable neighborhood search together. Several structures are employed. A set benchmarks has...

Public transport driver scheduling is a process of selecting set duties for the drivers vehicles to form number legal shifts. The problem usually has two objectives which are minimising both total shifts and shift cost, while taking into account some constraints related labour company rules. A commonly used approach firstly generate large feasible by domain-specific heuristics, then select subset final schedule an integer programming method. This paper presents estimation distribution...

Public transport crew scheduling is a worldwide problem, which NP-hard. This paper presents new approach, called GRAVIG, integrates grey relational analysis (GRA) into Variable Iterated Greedy (VIG) algorithm. The GRA served as solver for the shift selection during schedule construction process, can be considered multiple attribute decision making (MADM) since there are static and dynamic criteria governing efficiency of to selected schedule. Moreover, in biased probability destruction...

In this paper, the steelmaking-continuous casting problem with variable processing times is addressed. We present a modified migrating birds optimization (MMBO) to deal within reasonable time. For addressed problem, we use job permutation represent solution and give detailed decoding process. employed algorithm, introduce various neighborhood strategy explore space more widely. And benefit mechanism take full advantage of promising solutions. To evaluate performance our proposed other three...

Public transport driver scheduling is a process of selecting set duties for the drivers vehicles to form number legal shifts. The problem usually has two objectives which are minimising both total shifts and shift cost, while taking into account some constraints related labour company rules. A commonly used approach firstly generate large feasible by domain-specific heuristics, then select subset final schedule an integer programming method. This paper presents estimation distribution...

The iron and steel industry is energy-intensive due to the large volume of produced its high-temperature high-weight characteristics, sensors such as application can be utilized collect production data support process control optimization. Steelmaking-refining-continuous casting (SRCC) a bottleneck in process. SRCC scheduling problems are worldwide NP-hard. not only important for enterprises enhance efficiency, but also play significant role saving energy reducing resource consumption....

In view of nonlinear characteristics between ultimate bearing capacity single pile and its influencing factors, in this paper, GMC(1,N) power model is established by introducing parameters into model, the are solved particle swarm optimization. To improve precision further, based on cluster analysis corresponding algorithm put forward. Finally used to predict pile, result indicates that prediction accuracy better than neutral network GM(1,N) model. Therefore, new efficient effective.