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In this paper, we formulate joint optimization of microgrid configuration and energy consumption scheduling as a leader-follower Stackelberg game to model the coordination between two rational decision makers of microgrid configuration and energy consumption scheduling. The microgrid configuration decision, as the leader, is modeled as an upper-level optimization problem for optimal installed numbers of wind turbines, photovoltaic (PV) units, micro-turbines and batteries. The energy consumption scheduling, as the follower, is modeled as a lower-level optimization problem, which responds to decisions of the upper level in order to determine the optimal appliance scheduling. A bi-level nonlinear programming model is formulated for the Stackelberg game. To solve this optimization model, four bi-level hierarchical algorithms with the combination of different evolutionary algorithms are implemented and compared. A case study of microgrid configuration of smart building is employed to demonstrate the feasibility and advantage of the proposed game-theoretic model.

Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we present three variants of the discrete invasive weed optimization (DIWO) for the DAPFSP with total flowtime criterion. For solving such a problem, we present a two-level representation that consists of a product permutation and a number of job sequences. We introduce neighbourhood operators for both the product permutation and job sequences. We design effective local search procedures respectively for product-permutation-based neighbourhood and job-sequence-based neighbourhood. By combining the problem-specific knowledge and the idea of invasive weed optimization, we present three DIWO-based algorithms: a two-level discrete invasive weed optimization (TDIWO), a discrete invasive weed optimization with hybrid search operators (HDIWO), and a HDIWO with selection probability. The algorithms explore the two neighbourhoods in quite a different way. We calibrate the presented DIWO algorithms by means of the design of experimental method, and carry out a comprehensive computational campaign based on the 810 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithms perform significantly better than the other competing algorithms in the literature. Among the proposed algorithms, HDIWO is the best one.

To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail.

This paper proposes an energy-aware multi-objective optimization algorithm (EA-MOA) for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions. Two objectives, namely, the minimization of the makespan and the energy consumptions, are considered simultaneously. In the proposed algorithm, first, each solution is represented by two vectors: the machine assignment priority vector and the scheduling vector. Second, four types of decoding approaches are investigated to consider both objectives. Third, two efficient crossover operators, namely, Single-point Pareto-based crossover (SPBC) and Two-point Pareto-based crossover (TPBC) are developed to utilize the parent solutions from the Pareto archive set. Then, considering the problem structure, eight neighborhood structures and an adaptive neighborhood selection method are designed. In addition, a right-shifting procedure is utilized to decrease the processing duration for all machines, thereby improving the energy consumption objective of the given solution. Furthermore, several deep-exploitation and deep-exploration strategies are developed to balance the global and local search abilities. Finally, the proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of the experimental results, the highly effective proposed EA-MOA algorithm is compared with several efficient algorithms from the literature.

The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.

Production and distribution are two important sectors in a supply chain and their managements become an essential issue in industrial fields. The integrated operation of production and distribution stages are regarded as an effective approach. This work proposes an integrated production and distribution optimization problem, where jobs are processed in a distributed manufacturing system with multiple flow shops, and then they are delivered to customers locating in geographically-dispersed points. To mathematically describe this problem, a mixed integer programming model is formulated to minimize maximum completion time. In order to optimally solve the proposed problem, an enhanced black widow optimization algorithm is developed to deal with the studied problem. In this proposed approach, the solution representation, population initialization, procreation, cannibalism, and mutation along with a simulated annealing approach are specially designed. Then, a design of experiment approach is employed to analyze the influence of sensitive parameters on the proposed approach. Performance and efficiency of the designed method are validated through conducting extensive experiments on a set of benchmark test problems. Besides, comparisons with some well-known optimizers in the literature have been conducted to show the superiority of the proposed method.

Production and distribution are two essential activities in supply chain management. Currently, integrated production and distribution problems receive much attention because decision-makers devote to improving the operation efficiency of both stages and try to achieve an optimal solution. This work proposes an integrated distributed production and distribution problem with consideration of time windows, in which a set of jobs (i.e., customer orders) needs to be assigned among factories and the jobs are processed on flow shop environments at their associated factories. Then, the completed jobs are delivered by capacitated vehicles to customers in different regions while satisfying given time windows as much as possible. Accordingly, to optimally solve the proposed problem, a mixed integer programming model with minimizing total weighted earliness and tardiness has been established. For the optimization task, an enhanced brain storm optimization algorithm with some particular strategies is designed to handle the considered problem. To assess the performance of the proposed optimization method, several experiments by adopting a set of benchmark test problems are performed, and state-of-the-art optimizers are chosen for comparisons. The obtained optimization results exhibit that the designed algorithm significantly outperforms its rivals and can be considered as an excellent optimizer for solving the studied problem. Besides, compared with the CPLEX solver, the designed optimizer also performs much better for solving large-size problems.

In wafer manufacturing, with the shrinking down of wafer lot size, cluster tools are frequently required to switch from handling one lot of wafers to another, resulting in more transient processes, including start-up and close-down processes. In the existing work, optimal scheduling of start-up process for time-constrained single-arm cluster tools has been addressed under the assumption that each processing step consists of just one process chamber. This work relaxes this strict restriction by treating that multiple process chambers could be configured for processing steps. By building Petri net model for the start-up process, a linear program is derived to search a feasible schedule with minimal makespan for time-constrained single-arm cluster tools with parallel chambers for the first time. One industrial example is given to demonstrate the effectiveness of the obtained results. NRF (Natl Research Foundation, S’pore) Published version

This paper studies a large-scale urban traffic light scheduling problem (LUTLSP). A centralized model is developed to describe the LUTLSP, where each outgoing flow rate is described as a nonlinear mixed logical switching function over the source link’s density, the destination link’s density and capacity, and the driver’s potential psychological response to the past traffic light signals. The objective is to minimize the total network-wise delay time of all vehicles in a time window. Three metaheuristic optimization algorithms, named as Jaya algorithm, harmony search (HS) and water cycle algorithm (WCA) are implemented to solve the LUTLSP. Since we adopt a discrete-time formulation of LUTLSP, we firstly develop a discrete version of Jaya and WCA. Secondly, some improvement strategies are proposed to speed up the convergence of applied optimizers. Thirdly, a feature based search operator is utilized to improve the search performance of reported optimization methods. Finally, experiments are carried out based on the real traffic data in Singapore. The HS, WCA, Jaya, and their variants are evaluated by solving 11 cases of traffic networks. The comparisons and discussions verify that the considered metaheuristic optimization methods can effectively solve the LUTLSP considerably surpassing the existing traffic light control strategy.

This paper proposes a total of nine algorithms to minimize the makespan for the hybrid flowshop scheduling problem with sequence-dependent setup times. The first six algorithms are trajectory-based metaheuristics, including three variants of iterated local search and three variants of iterated greedy. The remaining three algorithms are population-based metaheuristics, namely, the improved fruit fly optimization, the improved migrating birds optimization, and the discrete artificial bee colony optimization. We present some advanced and effective technologies, including three mixed neighborhood structures, an enhanced perturbation method, and an enhanced destruction and construction procedure for the trajectory-based metaheuristics. We propose a path-relinking-based cooperative search, a diversity control scheme, and a diversified initialization approach for the improved fruit fly optimization. We calibrate the parameters and operators for the proposed algorithms by means of a design of experiments approach. To evaluate the proposed algorithms, we present several adaptations of other recent well-known meta-heuristics for the problem and conduct a comprehensive set of computational and statistical experiments to demonstrate the effectiveness of the presented algorithms. Among them, the discrete artificial bee colony optimization is the best-performing algorithm and it is able to improve 126 out of the 240 best known solutions for the benchmarks in the literature.

In recent years, sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile, affected by the intensification of market competition and economic globalization, distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity, an improved NSGAII algorithm (INSGAII) is proposed. First, we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second, four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third, inspired by the artificial bee colony algorithm, we propose a new colony generation method using the operators designed. Fourth, a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally, a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP.

Improved ABC algorithm is proposed for FJSP with fuzzy processing time.A heuristic, named MInEnd, is proposed to initialize population.New strategies are proposed to generate new solutions.The objectives are fuzzy maximum completion time and maximum machine workload.Benchmarks and realistic remanufacturing instances are solved by IABC. This study addresses flexible job-shop scheduling problem (FJSP) with fuzzy processing time. An improved artificial bee colony (IABC) algorithm is proposed for FJSP cases defined in existing literature and realistic instances in remanufacturing where the uncertainty of the processing time is modeled as fuzzy processing time. The objectives are to minimize the maximum fuzzy completion time and the maximum fuzzy machine workload, respectively. The goal is to make the scheduling algorithm as part of expert and intelligent scheduling system for remanufacturing decision support. A simple and effective heuristic rule is developed to initialize population. Extensive computational experiments are carried out using five benchmark cases and eight realistic instances in remanufacturing. The proposed heuristic rule is evaluated using five benchmark cases for minimizing the maximum fuzzy completion time and the maximum fuzzy machine workload objectives, respectively. IABC algorithm is compared to six meta-heuristics for maximum fuzzy completion time criterion. For maximum fuzzy machine workload, IABC algorithm is compared to six heuristics. The results and comparisons show that IABC algorithm can solve FJSP with fuzzy processing time effectively, both benchmark cases and real-life remanufacturing instances. For practical remanufacturing problem, the schedules by IABC algorithm can satisfy the requirement in real-life shop floor. The IABC algorithm can be as part of expert and intelligent scheduling system to supply decision support for remanufacturing scheduling and management.

Display Omitted A centralized model for urban traffic light scheduling problem (UTLSP).A discrete harmony search algorithm (DHS) for UTLSP.An ensemble of three local search operator to improve performance of DHS.Extensive experimental comparisons and discussion to verify DHS with ensemble. This study addresses urban traffic light scheduling problem (UTLSP). A centralized model is employed to describe the urban traffic light control problem in a scheduling framework. In the proposed model, the concepts of cycles, splits, and offsets are not adopted, making UTLSP fall in the class of model-based optimization problems, where each traffic light is assigned in a real-time manner by the network controller. The objective is to minimize the network-wise total delay time in a given finite horizon. A swarm intelligent algorithm, namely discrete harmony search (DHS), is proposed to solve the UTLSP. In the DHS, a novel new solution generation strategy is proposed to improve the algorithm's performance. Three local search operators with different structures are proposed based on the feature of UTLSP to improve the performance of DHS in local space. An ensemble of local search methods is proposed to integrate different neighbourhood structures. Extensive computational experiments are carried out using the traffic data from partial traffic network in Singapore. The DHS algorithm with and without local search operators and ensemble is evaluated and tested. The comparisons and discussions verify the effectiveness of DHS algorithms with local search operators and ensemble for solving UTLSP.

Nowadays, an increasing number of vehicle routing problem with stochastic demands (VRPSD) models have been studied to meet realistic needs in the field of logistics. In this paper, a bi-objective vehicle routing problem with stochastic demands (BO-VRPSD) was investigated, which aims to minimize total cost and customer dissatisfaction. Different from traditional vehicle routing problem (VRP) models, both the uncertainty in customer demands and the nature of multiple objectives make the problem more challenging. To cope with BO-VRPSD, a membrane-inspired multi-objective algorithm (MIMOA) was proposed, which is characterized by a parallel distributed framework with two operation subsystems and one control subsystem, respectively. In particular, the operation subsystems leverage a multi-objective evolutionary algorithm with clustering strategy to reduce the chance of inferior solutions. Meanwhile, the control subsystem exploits a guiding strategy as the communication rule to adjust the searching directions of the operation subsystems. Experimental results based on the ten 120-node instances with real geographic locations in Beijing show that, MIMOA is more superior in solving BO-VRPSD to other classical multi-objective evolutionary algorithms.

Recently, meta-heuristics have been employed and improved for solving various scheduling and combinational optimization problems. Disassembly line balancing, planning and scheduling problems (DLBPSP) are typical examples since the high complexity (NP-Hard). Since 2000s, numerous articles have represented the applications of meta-heuristics for solving DLBPSP. This paper aims to review the state-of-the-art of this topic. It can help researchers, especially for new researchers, to identify the current status of meta-heuristics for solving DLBPSP, to obtain the technologies used in various algorithms, and to follow the research trends of this topic. First, the related research articles are summarized, classified, and analyzed. Second, the special meta-heuristics for solving DLBPSP are reviewed. The encoding/decoding rules and improvement strategies are analyzed and discussed. Finally, the current research trends are summarized, and some future research directions are given.

Sustainable scheduling problems have been attracted great attention from researchers. For the flow shop scheduling problems, researches mainly focus on reducing economic costs, and the energy consumption has not yet been well studied up to date especially in the blocking flow shop scheduling problem. Thus, we construct a multi-objective optimization model of the blocking flow shop scheduling problem with makespan and energy consumption criteria. Then a discrete evolutionary multi-objective optimization (DEMO) algorithm is proposed. The three contributions of DEMO are as follows. First, a variable single-objective heuristic is proposed to initialize the population. Second, the self-adaptive exploitation evolution and self-adaptive exploration evolution operators are proposed respectively to obtain high quality solutions. Third, a penalty-based boundary interstation based on the local search, called by PBI-based-local search, is designed to further improve the exploitation capability of the algorithm. Simulation results show that DEMO outperforms the three state-of-the-art algorithms with respect to hypervolume, coverage rate and distance metrics.

A heuristic is proposed for initializing ABC population.An ensemble local search method is proposed to improve the convergence of TABC.Three re-scheduling strategies are proposed and evaluated.TABC is tested using benchmark instances and real cases from re-manufacturing.TABC compared against several state-of-the-art algorithms. This study addresses the scheduling problem in remanufacturing engineering. The purpose of this paper is to model effectively to solve remanufacturing scheduling problem. The problem is modeled as flexible job-shop scheduling problem (FJSP) and is divided into two stages: scheduling and re-scheduling when new job arrives. The uncertainty in timing of returns in remanufacturing is modeled as new job inserting constraint in FJSP. A two-stage artificial bee colony (TABC) algorithm is proposed for scheduling and re-scheduling with new job(s) inserting. The objective is to minimize makespan (maximum complete time). A new rule is proposed to initialize bee colony population. An ensemble local search is proposed to improve algorithm performance. Three re-scheduling strategies are proposed and compared. Extensive computational experiments are carried out using fifteen well-known benchmark instances with eight instances from remanufacturing. For scheduling performance, TABC is compared to five existing algorithms. For re-scheduling performance, TABC is compared to six simple heuristics and proposed hybrid heuristics. The results and comparisons show that TABC is effective in both scheduling stage and rescheduling stage.

Multi-skill resource-constrained project scheduling problem (MS-RCPSP) is one of the most investigated problems in operations research and management science. In this paper, a genetic programming hyper-heuristic (GP-HH) algorithm is proposed to address the MS-RCPSP. Firstly, a single task sequence vector is used to encode the solution, and a repair-based decoding scheme is proposed to generate feasible schedules. Secondly, ten simple heuristic rules are designed to construct a set of low-level heuristics. Thirdly, genetic programming is utilized as a high-level strategy which can manage the low-level heuristics on the heuristic domain flexibly. In addition, the design-of-experiment (DOE) method is employed to investigate the effect of parameters setting. Finally, the performance of the GP-HH is evaluated on the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset consisting of 36 instances. Computational comparisons between GP-HH and the state-of-the-art algorithms indicate the superiority of the proposed GP-HH in computing feasible solutions to the problem.