Multi-objective Scheduling Problem Research Articles

Application of chicken swarm optimization algorithm for multi objective scheduling problems in FMS

Scheduling plays an important role in Flexible Manufacturing System (FMS).Various Evolutionary Algorithms are used by different researchers to solve the multi objective scheduling problems. However, solutions obtained by some of these algorithms suffer from various issues such as struck in local optima, not support deadline constraint, poor convergence speed etc. In this paper, a new optimization technique called Chicken Swarm Optimization (CSO) algorithm is implemented for optimum scheduling of Multi objective Scheduling problems considered from the literature. The Combined Objective Function (COF) is formulated by considering two objectives such as Minimization of machine idle time and Penalty cost minimization with equal weight-ages. MATLAB code has developed to determine the COF values and also for implementing CSO to obtain the optimum solution. It is observed that, the results obtained by CSO algorithm are very competitive when compared with other well-known algorithms like Genetic Algorithms (GA), Cuckoos Search Algorithm (CSA), Modified Cuckoos Search Algorithm (MCSA) & Jaya Algorithm (JA)

Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes

The aim of this study was to solve power scheduling issues in smart homes to enable demand response in smart grids. The objective of demand response is to match demand with supply by reflecting supply expectations through consumer price signals, and especially to avoid peak demand during times of high prices and when supply is limited. Three objectives were considered: first, economic rationing by minimizing the total costs for consumers with the given hourly prices; second, to achieve better efficiency in terms of supply and greater stability in a power system by reducing peaks in usage or load, which is defined by minimizing the percentage of power rate; third, related to consumer comfort levels, by reducing variance in the schedule of appliances to actual usage periods requested. This multiobjective power scheduling problem for smart homes (PHPSH) was explored using a nondominated sorting genetic algorithm, called NSGA-II. The results showed that the Pareto-optimal solutions from NSGA-II are compatible with the weighted-sum-based model from the literature, and viable alternatives are available for end users with different weighted objectives.

Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.

Energy-efficient scheduling for an energy-intensive industry under punitive electricity price

As global warming and climate change become more and more serious, governments and scholars are paying more and more attention to these problems. Considering that consumption in manufacturing accounts for about one-half of the world's total energy consumption, the industrial sector is extremely important for preventing global warming. Facing with a multi-objective scheduling problem in the energy-intensive industries under punitive electricity price, a hybrid flow shop scheduling problem with onsite PV power generation and battery system is proposed. An improved multi-objective evolutionary algorithm based on decomposition is adopted, which integrates the low-carbon method with existing algorithms in three stages. First, an enhanced NEH heuristic algorithm is used to improve the quantity of initial solution in the initialization. Then the right-shift and speed scaling method are adapted to reduce the total energy consumption without affecting the maximum completion time. In the third stage, the distribution of renewable energy is performed, and the renewable energy is rescheduled based on the electricity range. The result of random instances and critical peak pricing verify the effectiveness of the proposed study.

Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources

In the era of aiming toward reaching a sustainable ecosystem, the primary focus is to curb the emissions generated by non-conventional resources. One way to achieve this goal is to find an alternative to traditional power plants (TPP) by integrating various distributed energy resources (DERs) via a Virtual Power Plant (VPP) in modern power systems. Apart from reducing emissions, a VPP enhances the monetary benefits to all its participants, including the DER owners, participants, and utility personnel. In this paper, the multi-objective optimal scheduling of the VPP problem considering multiple renewable energy resources has been solved using the multi-objective black widow optimization (MOBWO) algorithm. Renewable resources consist of solar PV modules, wind turbines, fuel cells, electric loads, heat-only units, and CHP units. The weighting factor method was adopted to handle the multi-objective optimal scheduling (MOOS) problem by simultaneously maximizing profit and minimizing emission while satisfying the related constraints. In this research, a peak valley power pricing strategy is introduced and the optimal scheduling of the VPP is attained by performing a multi-objective scheduling strategy (MOSS), which is day-ahead (on an hourly basis) and 15-min based (for a one-day profile), to observe the behavior of the anticipated system with a better constraint handling method. This algorithm is capable of dealing with a complex problem in a reduced computational time, ensuring the attainment of the considered objective functions. The numerical results obtained by the MOBWO algorithm after 100 independent trials were compared with the latest published work showing the effectiveness and suitability of the developed system.

Optimal scheduling of residential houses with optimal photovoltaic energy utilization strategy using improved multi-objective equilibrium optimizer algorithm

In the background of strongly practicing the “double carbon” strategy, the use of solar photovoltaic (PV) energy in residential houses is an effective way to achieve energy saving and emission reduction. In this paper, a multi-objective optimal scheduling model for residential houses is established, and it takes electricity cost, demand response (DR) curtailment value and carbon emission as the optimization objectives. Then, a novel operation strategy, which is called optimal PV energy utilization strategy, is proposed to further reduce electricity cost and carbon emission, by properly managing the power flow. For the multi-objective optimal scheduling problem with multi-decision variables and nonlinearity, an improved multi-objective equilibrium optimizer (IMOEO) algorithm is proposed to obtain the Pareto front solutions. The algorithm uses constrained non-dominant mechanism to deal with the constraint problems of the model, as well as the hybrid opposite learning strategy and the spiral operator are used to improve the overall performance of the algorithm. Finally, the optimal compromise solution is obtained by using the technique for order preference by similarity to ideal solution (TOPSIS). The experimental results indicate that IMOEO algorithm has better performance in terms of convergence and diversity. What's more, compared with the original strategy, the optimal compromise solution obtained by the proposed strategy reduces 28.76%, 9.78% and 15.36% for these optimization objectives, respectively. Therefore, the proposed method is beneficial to achieve low-carbon electricity consumption in residential houses.

Improved NSGA-II for energy-efficient distributed no-wait flow-shop with sequence-dependent setup time

This paper addresses a multi-objective energy-efficient scheduling problem of the distributed permutation flowshop with sequence-dependent setup time and no-wait constraints (EEDNWFSP), which have important practical applications. Two objectives minimization of both makespan and total energy consumption (TEC) are considered simultaneously. To address this problem, a new mixed-integer linear programming (MILP) model is formulated. Considering the issues faced in solving large-scale instances, an improved non-dominated sorting genetic algorithm (INSGA-II) is further proposed that uses two variants of the Nawaz-Enscore-Ham heuristic (NEH) to generate high-quality initial population. Moreover, two problem-specific speed adjustment heuristics are presented, which can enhance the qualities of the obtained non-dominated solutions. In addition, four local and two global search operators are designed to improve the exploration and exploitation abilities of the proposed algorithm. The effectiveness of the proposed algorithm was verified using extensive computational tests and comparisons. The experimental results show that the proposed INSGA-II is more effective compared to other efficient multi-objective algorithms.

Surrogate-assisted automatic evolving of dispatching rules for multi-objective dynamic job shop scheduling using genetic programming

Dispatching rules are simple but efficient heuristics to solve multi-objective job shop scheduling problems, particularly useful to face the challenges of dynamic shop environments. A promising method to automatically evolve non-dominated rules represents multi-objective genetic programming based hyper-heuristic (MO-GP-HH). The aim of such methods is to approximate the Pareto front of non-dominated dispatching rules as good as possible in order to provide a sufficient set of efficient solutions from which the decision maker can select the most preferred one. However, one of the main drawbacks of existing approaches is the computational demanding simulation-based fitness evaluation of the evolving rules. To efficiently allocate the computational budget, surrogate models can be employed to approximate the fitness. Two possible ways, that estimate the fitness either based on a simplified problem or based on samples of fully evaluated individuals making use of machine learning techniques are investigated in this paper. Several representatives of both categories are first examined with regard to their selection accuracy and execution time. Furthermore, we developed a surrogate-assisted MO-GP-HH framework, incorporating a pre-selection task in the NSGA-II algorithm. The most promising candidates are consequently implemented in the framework. Using a dynamic job shop scenario, the two proposed algorithms are compared to the original one without using surrogates. With the aim to minimize the mean flowtime and maximum tardiness, experimental results demonstrate that the proposed algorithms outperform the former. Making use of surrogates leads to a reduction in computational costs of up to 70%. Another interesting finding shows that the enhanced ability to identify duplicates based on the phenotypic characterization of individuals is particularly helpful in increasing diversity within a population. This study illustrates the positive effect of this mechanism on the exploration of the entire Pareto front.

Integrating preferences within multiobjective flexible job shop scheduling

When faced with a multiobjective optimization problem, it is necessary to consider the decision-maker preferences in order to propose the best compromise solution. We consider the multiobjective flexible job shop scheduling problem and a decision-maker that is best represented using a non-compensatory reference level-based preference model. We show how integrating this model into a multiobjective genetic algorithm allows to obtain solutions that surpass more aspiration levels when compared to classical multiobjective optimization approaches. Furthermore, these solutions are found faster and in greater numbers which facilitates their integration within the workshop.

An enhanced two phase estimation of distribution algorithm for solving scheduling problem

ABSTRACT Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.

A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem

A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. The objectives of the problem include minimising the maximum assembly completion time (), minimising the total energy consumption (TEC) and achieving resource allocation balanced . Four operations, which are critical factory insert, critical factory swap, critical factory insert to other factories, critical factory swap with other factories, are designed to optimise the objective of maximum assembly completion time. Q-learning mechanism is utilised to guide the selection of operations to avoid blind search in the iteration process. The learning mechanism based on clustering mechanism in brain storm optimisation algorithm is utilised to assign products to factories in the objective space according to the processing time of products to balance the resources allocation. The speed of operations on non-critical path is slowed down to reduce TEC regarded with the characteristics of no-wait flow shop scheduling problem. The experimental results under 810 large-scale instances by RLBSO show that the RLBSO outperforms the comparison algorithm for addressing the problem.

Multi-objective flexible job-shop scheduling problem researching based on improved adaptive NSGA-II

To solve the scheduling problem of products manufacturing in the military electronic equipment enterprises, a multi-objective flexible job-shop scheduling model was established with the objective of minimum the make span, production costs and equipment load. According to the characteristics of flexible job-shop scheduling problem, an adaptive NSGA-II algorithm was proposed in this paper, which could change the probabilities of crossover and mutation operation at different stages of genetic processes. To improve the diversity of population, the algorithm performed independent crossover and mutation operations on the working processes and equipment. Besides, a population independent elite retention strategy was adopted to avoid the loss of optimum solutions. At last, the feasibility and validity of the improved adaptive NSGA-II are verified by a flexible job-shop example.

Multi-objective complex product assembly scheduling problem considering parallel team and worker skills

The flow shop scheduling problem (FSP) is a classic shop scheduling problem with a strong engineering background. As an extension of FSP, the hybrid flow shop scheduling problem (HFSP) involves parallel machine scheduling, and the worker assignment problem on assembly lines (WAPAL) involves worker allocation. Parallel machine scheduling and multi-skilled and multilevel worker allocation are both involved in the actual assembly lines of complex products, but few studies have investigated them simultaneously. This work studies a complex product assembly line scheduling problem considering multi-skilled worker assignment and parallel team scheduling and takes the maximum completion time and the imbalance degree of team workload as the optimization objective. An integer programming model is proposed, and a hybrid coding method considers the worker assignment and task order. Three improved strategies based on a multiple objective evolutionary algorithm (MOEA) are proposed in the local search. Finally, 20 test instances are generated based on actual enterprise labor data, and the results based on the three strategies are compared with six MOEAs. The results show that the three strategies are superior in terms of the quality and distribution of solutions. The inverted generational distance (IGD) index value is increased by 49.97%, 47.89%, 47.08% respectively and the hypervolume (HV) index value is increased by 39.76%, 38.19%, 38.15% respectively.

Meta-Heuristic Algorithms for Multi-Objective Home Health Care Routing and Scheduling Problem Considering Time Windows and Workload Balance of Nurses

Meta-Heuristic Algorithms for Multi-Objective Home Health Care Routing and Scheduling Problem Considering Time Windows and Workload Balance of Nurses

Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time

With increasing environmental awareness and energy requirement, sustainable manufacturing has attracted growing attention. Meanwhile, there is a high level of uncertainty in practical processing procedure, particularly in flexible manufacturing systems. This study addresses the multi-objective flexible job shop scheduling problem with fuzzy processing time (MOFFJSP) to minimize the makespan and the total workload simultaneously. A mixed integer liner programming model is presented and a hybrid self-adaptive multi-objective evolutionary algorithm based on decomposition (HPEA) is proposed to handle this problem. HPEA has the following features: (i) two problem-specific initial rules considering triangular fuzzy number are presented for hybrid initialization to generate diverse solutions; (ii) five problem-specific local search methods are incorporated to enhance the exploitation; (iii) an effective solution selection method based on Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; and (iv) a parameter selection strategy is proposed to improve the quality of non-dominated solutions. To verify the effectiveness of HPEA, it is compared against other well-known multi-objective optimization algorithms. The results demonstrate that HPEA outperforms these five state-of-the-art multi-objective optimization algorithms in solving MOFFJSP.

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.

Improved discrete imperialist competition algorithm for order scheduling of automated warehouses

An improved discrete imperial competition algorithm (DICA) is proposed for the multi-objective order scheduling problems of automated warehouses. The aim is to minimize the completion time of all orders and the total tardiness of outbound orders. The priority value, influenced by the importance and relative cut-off time of the orders, as well as the number of tasks, is designed to sort the orders. Similar to the genetic algorithm (GA), the assimilation process of the improved DICA is constructed by crossover and mutation. Meanwhile, Hamming distance is introduced to verify the quality of assimilation. Taking a practical case as an example, the improved DICA is used to solve two scheduling strategies: outbound order priority scheduling and compound order scheduling. The main results show that the compound order scheduling can be performed effectively, especially when the number of orders is large, the completion time and total tardiness can be greatly reduced. In addition, compared with GA, the solution quality of the improved DICA is better, but its convergence speed is slower.

Multi-objective low-carbon hybrid flow shop scheduling via an improved teaching-learning-based optimization algorithm

In this article, for achieving an effective and environmental-friendly production scheduling, we investigate a multi-objective low-carbon hybrid flow shop scheduling problem (MLHFSP) with the consideration of machines with varied energy usage ratios. The problem is formulated by a multi-objective mathematical model with two optimization objectives, i.e., minimizing total carbon emission (TCE) and makespan (Cmax). We primarily analyse on the formation of TCE and derive its mathematical expression. MLHFSP is NP-hard, therefore, to tackle the model, an improved multi-objective teaching-learning-based optimization (ITLBO) algorithm is proposed. The ITLBO algorithm mainly contains global search based teaching phase and local search based learning phase. In ITLBO, a solution is represented by two vectors, i.e., job sequence vector and machine assignment vector. Sigma method is utilized to quantify each individual, and to avoid local optimum, sequential neighbourhood search (SNS) method is also adopted. Experimental results validate the feasibility and effectiveness of proposed ITLBO in addressing MLHFSP. The research findings help manufacturing engineers to seek a sophisticated balance between carbon emission reduction and makespan reduction.

A multi-strategy integration Pareto-based artificial colony algorithm for multi-objective flexible job shop scheduling problem with the earliness and tardiness criterion

This paper studies the multi-objective flexible job shop scheduling problem with the earliness and tardiness (E&T) criterion, explores the decoding and search strategies of algorithms under the coexistence of the mean E&T and makespan, and provides a makespan-constrainted three-phase decoding mechanism and local search strategies for both of them. Referencing to the flexibility of the artificial bee colony algorithm framework, multiple strategies are integrated properly in the algorithm to realise simultaneous optimisation of regular and irregular objectives. Through testing six benchmark instances of different scales with tight or loose delivery time for jobs, the distribution characteristics of the Pareto optimal solution set of the collaborative optimisation of the mean E&T and the makespan are explored. The proper integration of various search strategies can make the proposed algorithm have better performance.

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multiobjective fuzzy flow-shop scheduling problem

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multiobjective fuzzy flow-shop scheduling problem