An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups

A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization

Multi-item and small-lot-size production modes lead to frequent setup, which involves significant setup times and has a substantial impact on productivity. In this study, we investigated the optimal flexible job-shop scheduling problem with a sequence-dependent setup time. We built a mathematical model with the optimal objective of minimization of the maximum completion time (makespan). Considering the process sequence, which is influenced by setup time, processing time, and machine load limitations, first, processing machinery is chosen based on machine load and processing time, and then processing tasks are scheduled based on setup time and processing time. An improved quantum cat swarm optimization (QCSO) algorithm is proposed to solve the problem, a quantum coding method is introduced, the quantum bit (Q-bit) and cat swarm algorithm (CSO) are combined, and the cats are iteratively updated by quantum rotation angle position; then, the dynamic mixture ratio (MR) value is selected according to the number of algorithm iterations. The use of this method expands our understanding of space and increases operation efficiency and speed. Finally, the improved QCSO algorithm and parallel genetic algorithm (PGA) are compared through simulation experiments. The results show that the improved QCSO algorithm has better results, and the robustness of the algorithm is improved.

An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times

Surgical case scheduling is a key issue in the field of medician, which is a challenging work because of the difficulty in assigning resources to patients. This study regards the surgical case scheduling problem as a flexible job shop scheduling problem (FJSP). Considering the switching and preparation time of patients in different stage, an improved multi-objective imperialist competitive algorithm (IMOICA), which adopts the non-dominant sorting method, is proposed to optimize the whole scheduling. First, the social hierarchy strategy is developed to initialize the empire. Then, to enhance the global search ability of the algorithm, the concept of attraction and repulsion (AR) is introduced into the assimilation strategy. Moreover, to increase the diversity of the population, the revolution strategy is utilized. Finally, the variable neighborhood search (VNS) strategy is embedded to improve its exploitation capacity further. Experiments show that scheduling in advance saves time and cost, and IMOICA can solve the surgical case scheduling problem studied efficiently.

Energy Saving in Single-Machine Scheduling Management: An Improved Multi-Objective Model Based on Discrete Artificial Bee Colony Algorithm

Green manufacturing, which takes environmental effect and production benefit into consideration, has attracted increasing concern with the target of carbon peaking and carbon neutrality proposed. As a critical process in the manufacturing system, shop scheduling is also an important method for enterprises to achieve green manufacturing. Therefore, it is necessary to consider both production benefits and environmental objectives in shop scheduling, which are symmetrical and equally important. In addition, noise pollution has become an important environmental issue that cannot be ignored in the manufacturing processes, but which has been paid less attention in previous studies. Thus, the MODABC algorithm, with the optimization objectives of simultaneously minimizing lead-time/tardiness cost and job-shop noise pollution emission is proposed in this paper. We designed a discrete permutation-based two-layer encoding mechanism to generate the initial population. Then, three crossover methods were used to perform nectar update operations in the employed bee search phase, and three neighbourhood structures were used to improve the onlooker bee search operations. Finally, the MODABC algorithm was compared with other classical MOEAs. The results demonstrate that MODABC can provide non-dominated solution set with good convergence and distribution, and show significant superiority in solving green single-machine multi-objective scheduling problems.

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

An Improved Integrated Scheduling Algorithm with Process Sequence Time-Selective Strategy

The integrated scheduling algorithm of process sequence time-selective strategy (ISAOPSTSS) is an advanced algorithm in the field of integrated scheduling. The proposed algorithm points out the shortcomings of the process sequence time-selective strategy. Generally, there are too many “trial scheduling” times. The authors propose that there is no need to make “trial scheduling” at every “quasi-scheduling time point.” In fact, the process scheduling scheme can be obtained by trial scheduling on some “quasi-scheduling time points.” The scheduling result is the same as that of the sequence timing strategy. The proposed algorithm reduces the runtime of scheduling algorithm and improves the performance of the algorithm without reducing the optimization effect.