A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems

A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion

In traditional flow shop scheduling problems (FSSPs), the processing times are assumed to be pre-known and fixed parameters while in many practical environments, the processing times can be controlled by consumption of extra resources. In this paper, we propose resource-dependent processing times (RDPT) for permutation FSSP in which the processing time of a job depends on the amount of additional resources assigned to that job. To make a trade-off between makespan and required amount of resources, two conflict objective functions are considered: the minimisation of maximum completion time and the minimisation of total cost of resources. In order to solve the problem considered in this paper, a decomposition approach is suggested that strives to deal with the original model via two sub-problems: (i) sequencing problem; and (ii) resource allocation problem. A hybrid discrete differential evolution (HDDE) algorithm with an effective coordinate directions search and a variable neighbourhood search (VNS) are combined to solve two sub-problems. Furthermore, a statistical procedure is employed to adjust the significant parameters of the proposed HDDE and VNS algorithms. This procedure is based on the stepwise regression (SR) technique. The effectiveness of the suggested hybrid algorithm is investigated through a computational study and obtained results show the good performance of our approach with regard to the other algorithms.

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP.

A two-level batch chromosome coding scheme is proposed to solve the lot splitting problem with equipment capacity constraints in flexible job shop scheduling, which includes a lot splitting chromosome and a lot scheduling chromosome. To balance global search and local exploration of the differential evolution algorithm, a hybrid discrete differential evolution algorithm (HDDE) is presented, in which the local strategy with dynamic random searching based on the critical path and a random mutation operator is developed. The performance of HDDE was experimented with 14 benchmark problems and the practical dye vat scheduling problem. The simulation results showed that the proposed algorithm has the strong global search capability and can effectively solve the practical lot splitting problems with equipment capacity constraints.

In a traveling salesman problem, if the set of nodes is divided into clusters so that a single node from each cluster can be visited, then the problem is known as the generalized traveling salesman problem where the objective is to find a tour with minimum cost passing through only a single node from each cluster. In this chapter, a discrete differential evolution algorithm is presented to solve the problem on a set of benchmark instances. The discrete differential evolution algorithm is hybridized with local search improvement heuristics to further improve the solution quality. Some speed-up methods presented by the authors previously are employed to accelerate the greedy node insertion into a tour. The performance of the hybrid discrete differential evolution algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 (11) to 1084 (217) nodes (clusters) from the literature. Computational results show its highly competitive performance in comparison to the best performing algorithms from the literature.

This paper propose a harmony search (HS) algorithm to minimize makespan for the blocking flow shop scheduling problem. In the proposed algorithm, the HS-based search is employed to evolve harmony vectors to perform exploration, whereas a local search procedure based on the insert neighborhood is used to enhance the local exploitation ability. Extensive computational experiments and comparisons are carried out. Computational results show that the proposed HS algorithm is effective in finding optimal and near-optimal solutions.

The blocking flow shop scheduling problem: A comprehensive and conceptual review

ABSTRACTA given number of jobs in an open shop scheduling environment must each be processed for given amounts of time on each of a given set of machines in an arbitrary sequence. This study aims to achieve a schedule that minimizes total weighted completion time. Owing to the strong NP-hardness of the problem, the weighted shortest processing time block (WSPTB) heuristic is presented to obtain approximate solutions for large-scale problems. Performance analysis proves the asymptotic optimality of the WSPTB heuristic in the sense of probability limits. The largest weight block rule is provided to seek optimal schedules in polynomial time for a special case. A hybrid discrete differential evolution algorithm is designed to obtain high-quality solutions for moderate-scale problems. Simulation experiments demonstrate the effectiveness of the proposed algorithms.

The globalization of production promotes the adoption of Contract Manufacturing (CM) mode, enhancing the efficient utilization of production resources. In this mode, multiple Original Brand Manufacturers, acting as agents, delegate their production tasks to a single Original Equipment Manufacturer. In this scenario, agents share production resources and compete priority for each other. To reveal the nature of the competitive agents, this article investigates a bi-agent single-processor scheduling problem. It incorporates release dates to reflect the scenario where orders arrive in actual production settings. The objective is to enhance customer satisfaction by minimizing the sum of maximum lateness of two agents. A mixed integer programming model is formulated to address this NP-hard problem. For small-scale problems, a branch and bound algorithm featuring effective lower bound and branching strategy is proposed. It is complemented by a heuristic algorithm based on Earliest Due Date List-Revising, providing an initial upper bound. For middle-scale problems, a hybrid discrete differential evolution algorithm is developed, incorporating enhanced crossover and mutation operations and a variable neighborhood search strategy to avoid local optima. Simulation experiments endorse the high efficiency of the proposed algorithms.

This paper formulates a bi-criteria fuzzy blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date. An improved multi-objective grey wolf optimization (MOGWO) algorithm is proposed to solve this combinational optimization problem. The proposed MOGWO utilizes the ranked-order-value (ROV) rule for solution representation, employs a dynamic maintenance strategy to maintain archive, and develops a thorough mechanism for leader selection. In addition, to improve the performance of the neighborhood search, a VNS structure with three randomly ranked neighborhood search operators is introduced and implemented on the members of archive that may become the selected leaders. The proposed MOGWO is tested on a fuzzy blocking flow shop scheduling problem of panel block construction, and is compared with general MOGWO and multi-objective particle swarm optimization (MOPSO). Computational results suggest that the proposed MOGWO is superior to the compared algorithms in terms of the convergence, spread and coverage of the optimal solutions. This demonstrates the feasibility and effectiveness of the proposed MOGWO.

Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Scheduling flow shops using differential evolution algorithm

An accelerated discrete artificial bee colony algorithm under the makespan constraint: Solving the distributed blocking flow shop scheduling problem with balanced energy consumption costs

Optimizing blocking flow shop scheduling problem with total completion time criterion

This paper presents an efficient hybrid algorithms (EHA) based on harmony search (HS) algorithms and genetic algorithm (GA) for solving blocking flow shop scheduling problem. An improved GA is used to get better results. The computational result shows that EHA is not only better than GA , but also better than HS algorithm.