Jian-Ya Ding

An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

Graphical abstractDisplay Omitted HighlightsWe propose an improved IG algorithm for the no-wait flowshop scheduling problem.The proposed algorithm is incorporated with a Tabu-based reconstruction strategy.Simulation results confirm the advantages of utilizing the new reconstruction scheme.Our algorithm is more effective than other competitive algorithms in the literature.43 new upper bound solutions for the problem have been made available. This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms.

Carbon-efficient scheduling of flow shops by multi-objective optimization

Recently, there has been an increasing concern on the carbon efficiency of the manufacturing industry. Since the carbon emissions in the manufacturing sector are directly related to the energy consumption, an effective way to improve carbon efficiency in an industrial plant is to design scheduling strategies aiming at reducing the energy cost of production processes. In this paper, we consider a permutation flow shop (PFS) scheduling problem with the objectives of minimizing the total carbon emissions and the makespan. To solve this multi-objective optimization problem, we first investigate the structural properties of non-dominated solutions. Inspired by these properties, we develop an extended NEH-Insertion Procedure with an energy-saving capability. The accelerating technique in Taillard’s method, which is commonly used for the ordinary flowshop problem, is incorporated into the procedure to improve the computational efficiency. Based on the extended NEH-Insertion Procedure, a multi-objective NEH algorithm (MONEH) and a modified multi-objective iterated greedy (MMOIG) algorithm are designed for solving the problem. Numerical computations show that the energy-saving module of the extended NEH-Insertion Procedure in MONEH and MMOIG significantly helps to improve the discovered front. In addition, systematic comparisons show that the proposed algorithms perform more effectively than other tested high-performing meta-heurisitics in searching for non-dominated solutions.

Parallel Machine Scheduling Under Time-of-Use Electricity Prices: New Models and Optimization Approaches

The industrial sector is one of the largest energy consumers in the world. To alleviate the grids burden during peak hours, time-of-use (TOU) electricity pricing has been implemented in many countries around the globe to encourage manufacturers to shift their electricity usage from peak periods to off-peak periods. In this paper, we study the unrelated parallel machine scheduling problem under a TOU pricing scheme. The objective is to minimize the total electricity cost by appropriately scheduling the jobs such that the overall completion time does not exceed a predetermined production deadline. To solve this problem, two solution approaches are presented. The first approach models the problem with a new time-interval-based mixed integer linear programming formulation. In the second approach, we reformulate the problem using Dantzig-Wolfe decomposition and propose a column generation heuristic to solve it. Computational experiments are conducted under different TOU settings and the results confirm the effectiveness of the proposed methods. Based on the numerical results, we provide some practical suggestions for decision makers to help them in achieving a good balance between the productivity objective and the energy cost objective.

Accelerated methods for total tardiness minimisation in no-wait flowshops

For the minimisation of total tardiness in no-wait flowshops, objective incremental properties are investigated in this paper to speed up the evaluation of candidate solutions. To explore the properties, we introduce a new concept of sensitive jobs and identify through experiments that the proportion of such jobs is very small. Instead of evaluating the tardiness of each job, we focus on the evaluation of sensitive jobs which will help to reduce the computational efforts. With these properties, the time complexity of the NEH-insertion procedure is reduced from to approximately in average. Then, an accelerated NEH algorithm and an accelerated iterated greedy algorithm are designed for the problem. Since the NEH-insertion procedure constitutes the main computational burden for both algorithms, these algorithms will benefit directly from the speedup. Numerical computations show that the accelerated algorithms perform 10–40 times faster than the original algorithms on the middle- and large-sized instances. In addition, comparisons show that the proposed algorithms perform more efficiently and effectively than the existing heuristics and meta-heuristics.

Distributionally robust single machine scheduling with risk aversion

This paper presents a distributionally robust (DR) optimization model for the single machine scheduling problem (SMSP) with random job processing time (JPT). To the best of our knowledge, it is the first time a DR optimization approach is applied to production scheduling problems in the literature. Unlike traditional stochastic programming models, which require an exact distribution, the presented DR-SMSP model needs only the mean-covariance information of JPT. Its aim is to find an optimal job sequence by minimizing the worst-case Conditional Value-at-Risk (Robust CVaR) of the job sequence’s total flow time. We give an explicit expression of Robust CVaR, and decompose the DR-SMSP into an assignment problem and an integer second-order cone programming (I-SOCP) problem. To efficiently solve the I-SOCP problem with uncorrelated JPT, we propose three novel Cauchy-relaxation algorithms. The effectiveness and efficiency of these algorithms are evaluated by comparing them to a CPLEX solver, and robustness of the optimal job sequence is verified via comprehensive simulation experiments. In addition, the impact of confidence levels of CVaR on the tradeoff between optimality and robustness is investigated from both theoretical and practical perspectives. Our results convincingly show that the DR-SMSP model is able to enhance the robustness of the optimal job sequence and achieve risk reduction with a small sacrifice on the optimality of the mean value. Through the simulation experiments, we have also been able to identify the strength of each of the proposed algorithms.

New block properties for flowshop scheduling with blocking and their application in an iterated greedy algorithm

This paper proposes new block properties for the flowshop scheduling problem with blocking to minimise makespan. A pruning procedure based on these proposed properties is used in the construction phase of an iterated greedy algorithm to decrease the total number of solutions to be examined to find an optimal schedule. Computational results using Taillard’s benchmark problem instances show that the new block properties help to eliminate more ‘unpromising’ solutions than the classic properties. In addition, the effectiveness of the proposed algorithm is verified by comparison with some high-performing algorithms for the considered problem.

A novel Block-shifting simulated annealing algorithm for the no-wait flowshop scheduling problem

This paper proposes a Block-shifting Simulated Annealing (BSA) algorithm for the no-wait flowshop scheduling problem (NWFSP) to minimize makespan. The proposed algorithm makes use of the objective incremental properties of NWFSP and embeds a block-shifting operator based on k-insertion moves into the algorithm framework of simulated annealing. A major advantage of the BSA algorithm lies in its easy implementation since it does not involve sophisticated evolutionary strategy and parameter tuning process. In addition to its simplicity, BSA is shown to be very effective. Through experimental comparisons, the effectiveness of the block-shifting operator is clearly revealed. In addition, the BSA algorithm is proved to be more effective and robust than the state-of-the-art algorithms for solving the NWFSP.

Minimizing makespan for a no-wait flowshop using tabu mechanism improved iterated greedy algorithm

This paper proposes a tabu mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flow-shop scheduling problem with makespan criterion. The motivation of seeking for further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may lead to repeated search when applying the insertion neighborhood search. To overcome the drawback, we modified the IG algorithm by a tabu-based reconstruction strategy to enhance its exploitation ability. A powerful neighborhood search method which involves insert, swap, and double-insert moves is then applied to obtain better soluions from the reconstructed solution in the previous step. Numerical computations verified the advantages of utilizing the new reconstruction scheme. In addition, comparisons with other high-performing algorithms demonstrated the effectiveness and robustness of the proposed algorithm.

Likelihood ratio based communication for distributed detection

This paper is concerned with a detection framework under scheduled communication for a binary hypothesis testing problem. A scheduler is designed to smartly select useful sensor measurements for transmission and leave non-useful ones, which results in that only a subset of measurements is sent to the testing agency. To this purpose, a likelihood ratio based scheduler is implemented to decide the transmission of measurements from sensor to the tester. For comparison, a random scheduler which randomly selects measurements for transmission is also included. The Neyman-Pearson tests under the above two schedulers is provided. Given a moderate communication cost constraint, it is shown that the likelihood ratio based scheduler achieves a comparable asymptotic testing performance to the optimal test using the full set of measurements, and is strictly better than the random scheduler. The theoretical results are verified by simulations.

Distributionally robust scheduling on parallel machines under moment uncertainty

Abstract This paper investigates a distributionally robust scheduling problem on identical parallel machines, where job processing times are stochastic without any exact distributional form. Based on a distributional set specified by the support and estimated moments information, we present a min-max distributionally robust model, which minimizes the worst-case expected total flow time out of all probability distributions in this set. Our model doesn’t require exact probability distributions which are the basis for many stochastic programming models, and utilizes more information compared to the interval-based robust optimization models. Although this problem originates from the manufacturing environment, it can be applied to many other fields when the machines and jobs are endowed with different meanings. By optimizing the inner maximization subproblem, the min-max formulation is reduced to an integer second-order cone program. We propose an exact algorithm to solve this problem via exploring all the solutions that satisfy the necessary optimality conditions. Computational experiments demonstrate the high efficiency of this algorithm since problem instances with 100 jobs are optimized in a few seconds. In addition, simulation results convincingly show that the proposed distributionally robust model can hedge against the bias of estimated moments and enhance the robustness of production systems.