Effective dynamic dispatching rule and constructive heuristic for solving single-machine scheduling problems with a common due window

This study addresses the single-machine scheduling problem with a common due window (CDW) that has a constant size and position. The objective is to minimise the total weighted earliness–tardiness penalties for jobs completed out of the CDW. To determine a schedule as close to optimum as possible, this study develops a dynamic dispatching rule and an effective constructive heuristic. The better performance of the proposed heuristic is demonstrated by comparing the results of it with those of a state-of-the-art greedy heuristic on a well-known benchmark problem set. In addition, we incorporate the constructive heuristic into a best-so-far meta-heuristic to examine the benefit of the proposed heuristic. The results show that the best known solutions in 144 out of the 250 benchmark instances are improved.     

Dynamic single-machine scheduling under distributed decision-making

This paper presents a deterministic dynamic model for the single-machine scheduling problem. The model uses forecasts of future job arrivals with the current data to extract job interactions over time, updating the information on rolling basis. The model is implemented in a distributed structure with both the machine and the jobs involved in decision-making to create a schedule. The decision-making is modelled similar to an auction with a theoretical basis for problem decomposition, bid construction and bid evaluation. Numerical results indicate that the model outperforms other distributed implementations in both static and dynamic implementations for a wide range of single-machine scheduling problems.     

Robustness and stability measures for scheduling: single-machine environment

This paper addresses the issue of finding robust and stable schedules with respect to random disruptions. Specifically, two surrogate measures for robustness and stability are developed. The proposed surrogate measures, which consider both busy and repair time distributions, are embedded in a tabu-search-based scheduling algorithm, which generates schedules in a single-machine environment subject to machine breakdowns. The performance of the proposed scheduling algorithm and the surrogate measures are tested under a wide range of experimental conditions. The results indicate that one of the proposed surrogate measures performs better than existing methods for the total tardiness and total flowtime criteria in a periodic scheduling environment. A comprehensive bibliography is also presented.     

Integer programming models for the re-entrant shop scheduling problems

A re-entrant shop describes a manufacturing environment in which a machine can process a job more than once. A re-entrant shop can be classified into a re-entrant job-shop (RJS) and a re-entrant flow-shop (RFS). This article presents the development of the mixed binary integer programming (BIP) technique for the RJS and the RFS scheduling problems. Eight mixed BIP models are proposed for solving re-entrant shop scheduling problems to minimize makespan. The models RJS-1, RJS-2, RJS-3, and RJS-4 are for the RJS problems, while the other four models (RFS-1, RFS-2, RFS-3, and RFS-4) are for the RFS problems. Computational results show that the RJS-4 model is the best of the RJS models, and the RFS-3 model is the best of the RFS models.     

A large-scale distributed decision-making procedure for a single-machine scheduling problem

This paper presents a distributed scheduling procedure for a large-scale single-machine problem with precedence constraints, and identifies phenomena using large-scale distributed decision-making for a decomposable and distributed situation. The approach proposed exhibits efficient computational performance over a large-sized work load (23,000 jobs and 2,209,536 precedence constraints) in a distributed computing environment. We highlight three findings: (1) the communication burden originating from the large-scale problem can lead to performance loss while distributed agents collaborate to solve the problem, but after a threshold the computational gain by distribution offsets the loss; (2) when the problem size is sufficiently large, the real computation gain outperforms the expected gain by the number of agents (super-linear effect); and (3) when the number of precedence constraints of each agent differs, the slowest agent processing the largest number of precedence constraints restrains the computational performance (load imbalance). We believe that our research is the first distributed decision-making model that meets the requirements of distributed information, distributed decision authority, and distributed computation in a large-scale single-machine scheduling situation with precedence constraints.     

β-Robust scheduling for single-machine systems with uncertain processing times

In scheduling environments with processing time uncertainty, system performance is determined by both the sequence in which jobs are ordered and the actual processing times of jobs. For these situations, the risk of achieving substandard system performance can be an important measure of scheduling effectiveness. To hedge this risk requires an explicit consideration of both the mean and the variance of system performance associated with alternative schedules, and motivates a β-robustness objective to capture the likelihood that a schedule yields actual performance no worse than a given target level. In this paper we focus on β-robust scheduling issues in single-stage production environments with uncertain processing times. We define a general β-robust scheduling objective, formulate the β-robust scheduling problem that results when job processing times are independent random variables and the performance measure of interest is the total flow time across all jobs, establish problem complexity, and develop exact and heuristic solution approaches. We then extend the 0-robust scheduling model to consider situations where the uncertainty associated with individual job processing times can be selectively controlled through resource allocation. Computational results are reported to demonstrate the efficiency and effectiveness of the solution procedures.     

Solution algorithms for single-machine group scheduling with ready times and deteriorating jobs

This article considers the single-machine group scheduling problem with deterioration effect and ready times. The objective of this problem is to determine the sequence of groups and the sequence of jobs to minimize the makespan. To solve the problem, an algorithm based on enumeration, an heuristic algorithm and a branch-and-bound algorithm are developed and exhaustively tested. The computational results show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions and the branch-and-bound algorithm is very effective in obtaining optimal solutions.     

Some dominance properties for single-machine tardiness problems with sequence-dependent setup

Some dominance properties are proposed for the NP-hard problems of scheduling N jobs on a single machine with due dates, and sequence-dependent setup times. The algorithms based on Ragatz's branch and bound scheme with the dominance properties are developed to minimize the maximum tardiness or the total tardiness. Computational experiments demonstrate the effectiveness of the dominance rules.     

Makespan minimisation for a parallel machine scheduling problem with preemption and job incompatibility

In this paper, an extension of the graph colouring problem is introduced to model a parallel machine scheduling problem with job incompatibility. To get closer to real-world applications, where the number of machines is limited and jobs have different processing times, each vertex of the graph requires multiple colours and the number of vertices with the same colour is bounded. In addition, several objectives related to scheduling are considered: makespan, number of pre-emptions and summation over the jobs’ throughput times. Different solution methods are proposed, namely, two greedy heuristics, two tabu search methods and an adaptive memory algorithm. The latter uses multiple recombination operators, each one being designed for optimising a subset of objectives. The most appropriate operator is selected dynamically at each iteration, depending on its past performance. Experiments show that the proposed algorithm is effective and robust, while providing high-quality solutions on benchmark instances for the graph multi-colouring problem, a simplification of the considered problem.     

More MILP models for hybrid flow shop scheduling problem and its extended problems

With the rapid development of computer technology and related softwares for mathematical models, mathematical modelling of scheduling problems is receiving growing attention from researchers. In this work, the hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the objective aimed to minimise the makespan is studied. According to the characteristics of the HFSP-UPM, eight mixed integer linear programming (MILP) models are formulated in order to obtain optimal solutions based on different modelling ideas. Then, these models are extended to solve HFSP-UPM with sequence-dependent setup times (HFSP-UPM-SDST), no-wait HFSP-UPM (HFSP-UPM-NW) and HFSP-UPM with blocking (HFSP-UPM-B). All the proposed models and the existing model are detailedly compared and evaluated under three aspects namely modelling process, size complexity and computational complexity. Numerical experiments show that MILP models dependent on diverse modelling ideas perform very differently. The model developed based on stage precedence is the best one and should be given preference in future applications. In addition, the proposed models of HFSP-UPM-NW and HFSP-UPM-B improve several best known solutions for the test instances in the existing literature.     

Multicriteria models for just-in-time scheduling

Just-in-time manufacturing consists of organising the production of elements in order to meet a certain number of objectives or requirements according to the so-called ‘Just-in-Time philosophy’. Just-in-time has been studied extensively in the literature for many years due to the large number of real-life situations where it can be applied. This paper aims at revisiting Just-in-Time principles and detailing how they can be applied to the scheduling stage of a manufacturing process. Therefore, new models that are multicriteria models by their very nature are presented and discussed. The conclusions highlight the fact that most of the existing models presented in the scheduling literature happen to be incomplete regarding Just-in-Time principles.     

A single-machine distributed scheduling methodology using cooperative interaction via coupling agents

This paper considers a single-machine scheduling problem where the decision authorities and information are distributed in multiple subproduction systems. Subproduction systems share the single machine and must cooperate with one another to achieve a global goal of minimizing a linear function of the completion times of the jobs; e.g., total weighted completion times. It is assumed that neither the subproduction systems nor the shared machine have complete information about the entire system. The associated scheduling problems are formulated as zero-one integer programs. The solution approach is based on Lagrangian relaxation techniques modified to require less global information. Specifically, there is no need for a global upper bound, or a single master problem that has a complete view of all the coupling constraints. The proposed methodology exhibits a promising performance when experimentally compared to the Lagrangian relaxation with a subgradient method with the added benefit that can be applied to situations with more restrictive information sharing.     

Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming

In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first; however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems.     

Semi-Markov decision models for real-time scheduling

We first present a class of real-time scheduling problems and show that these can be formulated as semi-Markov decision problems. Then we discuss two practical difficulties in solving such problems. The first is that the resulting model requires a large amount of data that is difficult to obtain; the second is that the resulting model usually has a state space that is too large for analytic consideration. Finally, we present a non-intrusive ‘knowledge acquisition’ method that identifies the states and transition probabilities that an expert uses in solving these problems. This information is then used in the semi-Markov optimization problem. A circuit board production line is used to demonstrate the feasibility of this method. The size of the state space is reduced from 2035 states to 308 by an empirical procedure. An ‘optimal’ solution is derived based on the model with the reduced state space and estimated transition probabilities. The resulting schedule is significantly better than the one used by the observed expert.     

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.     

Generalized hull properties for location problems

This paper considers a general form of the single facility minisum location model, also known as the Fermat-Weber problem, in which the cost components are increasing differentiable functions of a norm. In particular, attention is restricted to a broad class of norms referred to as round norms, a formal definition of which is included. It is shown that all locally optimal locations of the new facility in the two-dimensional problem (N-dimensional for the Euclidean norm) must be within the convex hull of the destinations. The results are extended to a general form of the multifacility minisum location problem.     

Generalized additive models for functional data

The aim of this paper is to extend the ideas of generalized additive models for multivariate data (with known or unknown link function) to functional data covariates. The proposed algorithm is a modified version of the local scoring and backfitting algorithms that allows for the nonparametric estimation of the link function. This algorithm would be applied to predict a binary response example.