A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time

Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.

A Chance Constrained Programming Approach for No-Wait Flow Shop Scheduling Problem under the Interval-Valued Fuzzy Processing Time

This work focuses on the study of robust no-wait flow shop scheduling problem (R-NWFSP) under the interval-valued fuzzy processing time, which aims to minimize the makespan within an upper bound on total completion time. As the uncertainty of actual job processing times may cause significant differences in processing costs, a R-NWFSP model whose objective function involves interval-valued fuzzy sets (IVFSs) is proposed, and an improved SAA is designed for its efficient solution. Firstly, based on the credibility measure, chance constrained programming (CCP) is utilized to make the deterministic transformation of constraints. The uncertain NWFSP is transformed into an equivalent deterministic linear programming model. Then, in order to tackle the deterministic model efficiently, a simulated annealing algorithm (SAA) is specially designed. A powerful neighborhood search method and new acceptance criterion are applied to find better solutions. Numerical computations demonstrate the high efficiency of the SAA. In addition, a sensitivity analysis convincingly shows that the applicability of the proposed model and its solution strategy under interval-valued fuzzy sets.

A Novel Recursive Heuristic Algorithm for Straight and U-Shaped Assembly Line Balancing with Fuzzy Processing Time

The paper aims to address the straight and U–type assembly line balancing problems by developing a novel recursive heuristic algorithm based on the idea of the depth of search. The dynamic fuzzy processing time (DFPT) model is employed to represent uncertainty and ambiguity related to the processing time in the actual production systems. The novel algorithm, the minimum cycle time objective is considered for a set of imposed considerers. They are arranged in an appropriate strategy in which three-stages are proposed and presented as a solution approach. Finally, the validity of the developed solution approach is evaluated through a tested numerical example conducted over a test problem taken from literature to assess its performance. This study proofs their ability and efficiency in assisting decision-making by determining the contribution proportion for significant assignment variables represented by skill level, work stability, type layout, and priority rule.

A novel imperialist competitive algorithm for fuzzy distributed assembly flow shop scheduling

Assembly flow shop scheduling problem with DPm → 1 layout has important applications in various manufacturing systems and has been extensively considered in single factory; however, this problem with fuzzy processing time is seldom studied in multiple factories. In this paper, fuzzy distributed assembly flow shop scheduling problem (FDAFSP) is considered, in which each factory has DPm → 1 layout, and an imperialist competitive algorithm with empire cooperation (ECICA) is developed to minimize fuzzy makespan. In ECICA, an adaptive empire cooperation between the strongest empire and the weakest empire is implemented by exchanging computing resources and search ability, historical evolution data are used and a new imperialist competition is adopted. Numerical experiments are conducted on various instances and ECICA is compared with the existing methods to test its performance. Computational results demonstrate that ECICA has promising advantages on solving FDAFSP.

Optimal Sequence in General Flow Shop with Job Block Criteria using Fuzzy Index Technique

Here, we are finding a novel methodology to solve a problem of scheduling of general flow shop where proceeding time of job is indeterminate. The parameters required to solve such problem was considered to be in triangular fuzzy number. The concept of job block concept has been introduced to understand relative interference of one job with other. The novelty of this method lies in the section that it will not convert the fuzzy processing time into classical numbers to figure out the near optimal sequence of jobs. The method has been made clearer by giving a numerical example to demonstrate the purposed technique.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.