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We study scheduling problems on parallel dedicated machines. Thus, each job can be processed on one specific machine only. The option of job-rejection is considered, and the total permitted rejection cost of all the jobs is bounded. Six scheduling problems are solved: ( $ i $ i) minimising makespan, ( $ ii $ ii) minimising makespan with release-dates, ( $ iii $ iii) minimising total completion time, ( $ iv $ iv) minimising total weighted completion time, ( $ v $ v) minimising total load, and ( $ vi $ vi) minimising maximum tardiness. Pseudo-polynomial dynamic programming algorithms are introduced for all these NP-hard problems. [ABSTRACT FROM AUTHOR]

This paper explores the multitasking scheduling problem via alternate periods, which is common in manufacturing and service industries. The shift pattern comprises regular shifts interspersed with breaks, allowing job switching. We focus on two alternate periods consisting of odd periods and even periods. Each job is not only processed completely within odd periods or completely within even periods, but job switching is also allowed between alternate periods with additional overhead. For this problem, we propose two job switching models. The first model is alternate periods multitasking scheduling with switching time, where a fixed switching time is incurred whenever a job is switched from one period to its adjacent period. The second model is alternate periods multitasking scheduling with diverse speeds, where an urgent switching cost is produced if a job is processed through the alternate periods with different speeds. For the two models, we study three scheduling objectives including minimising the total completion time, minimising the maximum lateness, and minimising the number of tardy jobs, respectively. For each of the problems under consideration, we investigate the structural properties of the optimal schedule and develop the corresponding pseudo-polynomial time dynamic programming algorithm. Furthermore, we give examples to effectively illustrate the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study uses an agent-based approach with a combinatorial auction mechanism to solve the joint order acceptance and assembly job shop scheduling problem. A set of jobs is offered. Each job has a revenue, ready time, due date, deadline, and consists of a set of operations with precedence relationships. Jobs that deviate from their due dates incur earliness/tardiness penalties. An operation may require several units of capacity per time unit and a resource could have multiple units of capacity. The manufacturer can reject any job to satisfy the capacity constraints and maximise the overall profit. We develop a mathematical model for the problem, then use an agent-based approach to solve it. First, the relaxed problem is decomposed into a set of job-level subproblems. Each job is optimised individually without considering the capacity constraints. Profitable jobs at the individual level submit their optimal schedules as combinatorial bids to an auctioneer to acquire combinations of resource capacity-time units. Then, the auctioneer records the profit upper bound, resolves capacity conflicts to reach a feasible solution, records the profit lower bound, and updates the dual variables. Experimental results show that the proposed methodology can solve large-sized problems in reasonable CPU times. [ABSTRACT FROM AUTHOR]

Distributed manufacturing is gradually becoming the future trend. The fierce market competition makes manufacturing companies focus on productivity and product delivery. The hybrid flow shop scheduling problem (HFSP) is common in manufacturing. Considering the difference of machines at the same stage, the multi-objective distributed hybrid flow shop scheduling problem with unrelated parallel machines (MODHFSP-UPM) is studied with minimum makespan and total tardiness. An improved multi-population genetic algorithm (IMPGA) is proposed for MODHFSP-UPM. The neighbourhood structure is essential for meta-heuristic-based solving algorithms. The greedy job insertion inter-factory neighbourhoods and corresponding move evaluation method are designed to ensure the efficiency of local search. To enhance the optimisation ability and stability of IMPGA, sub-regional coevolution among multiple populations and re-initialisation procedure based on probability sampling are designed, respectively. In computational experiments, 120 instances (including the same proportion of medium and large-scale problems) are randomly generated. The IMPGA performs best in all indicators (spread, generational distance, and inverted generational distance), significantly outperforming existing efficient algorithms for MODHFSP-UPM. Finally, the proposed method effectively solves a polyester film manufacturing case, reducing the makespan and total tardiness by 40% and 60%, respectively. [ABSTRACT FROM AUTHOR]

We study a common due-date assignment problem on two parallel uniform machines. The jobs are assumed to have identical processing times, and job-dependent and asymmetric earliness and tardiness unit costs. The scheduler may process only a subset of the jobs, i.e. the option of job-rejection is allowed. The objective function consists of three cost components: the total earliness-tardiness cost of all scheduled jobs, the cost of the common due-date, and total rejection cost. For a given number of rejected jobs and a given due-date, the problem is reduced to a non-standard linear assignment problem. Consequently, the optimal solution is shown to be obtained in polynomial time in the number of jobs. The case of a given (possibly restrictive) due-date, the extension to a setting of more than two machines, and a number of special cases, are also discussed. [ABSTRACT FROM AUTHOR]

Motivated by the need of quick job (re-)scheduling, we examine an elaborate scheduling environment under the objective of total weighted tardiness minimisation. The examined problem variant moves well beyond existing literature, as it considers unrelated machines, sequence-dependent and machine-dependent setup times, and a renewable resource constraint on the number of simultaneous setups. For this variant, we provide a relaxed MILP to calculate lower bounds, thus estimating a worst-case optimality gap. As a fast exact approach appears not plausible for instances of practical importance, we extend known (meta-)heuristics to deal with the problem at hand, coupling them with a Constraint Programming (CP) component – vital to guarantee the non-violation of the problem's constraints – which optimally allocates resources with respect to tardiness minimisation. The validity and versatility of employing different (meta-)heuristics exploiting a relaxed MILP as a quality measure are revealed by our extensive experimental study, which shows that the methods deployed have complementary strengths depending on the instance parameters. Since the problem description has been obtained from a textile manufacturer where jobs of diverse size arrive continuously under tight due dates, we also discuss the practical impact of our approach in terms of both tardiness decrease and broader managerial insights. [ABSTRACT FROM AUTHOR]

We investigate a stochastic distributed appointment assignment and scheduling problem, which consists of assigning appointments to distributed service units and determining service sequences at each service unit. In particular, the service time duration and release time uncertainties are well-considered. The solution to this generic problem finds interesting applications in distributed production systems, healthcare systems, and post-disaster operations. We formulate the problem as a two-stage stochastic program to minimise the total transportation cost and expected makespan, idle time or overtime, and apply the sample average approximation method to make the problem tractable. We then develop a stochastic logic-based Benders decomposition method, decomposing the problem into a master problem and a subproblem. The master problem determines the appointment assignment variables, and the subproblem handles the sequence and service start time variables. Benders optimality cuts are generated from the subproblem's solution and added to the master problem. The developed stochastic logic-based method is advantageous since it can manage many scenarios in parallel. We further consider each appointment's due date, minimise the weighted earliness and tardiness, and adjust the developed method to solve this variant. Experiments on random instances demonstrate the excellent performance of the proposed model and methods. [ABSTRACT FROM AUTHOR]

Punctuality, defined as the ability to arrive or complete tasks at the designated time, it is a fundamental aspect of academic success and personal development. Punctuality remains a challenge for many students, with tardiness and absenteeism posing significant barriers to their academic progress. To assess the effectiveness of integrating time management, increased engagement, and technology-based solutions as a strategy to improve students' punctuality, quantitative research design was adopted by the researchers for the study. The researchers used a quantitative research design. It is a descriptive analysis research method since the objectives of the study is to determine the differences of the previous data gathered before the implementation of the proposed intervention and innovation and the present data after the implementation. The research participants comprised the Grade 10 students from sections Gold and Radium. The interventions, which included structured schedules, technology integration, and interactive learning activities, significantly improved the punctuality rates of students in the Grade 10 sections examined. Before the implementation of these interventions, the average tardiness rates were notably high, indicating a prevalent issue with punctuality. However, after the interventions were applied, there was a marked improvement in punctuality, highlighting the effectiveness of these strategies in addressing the problem. [ABSTRACT FROM AUTHOR]

Two different ways of naming and narrating historical periods, including the present time, involve using the terms 'capitalism' and 'modernity'. Each terminological set opens certain intellectual vistas, while foreclosing others. This article argues that while 'late capitalism' is, despite its many problems, a meaningful concept to use today, 'late modernity' is not. Offering a genealogy of each term, the article considers how ascriptions of lateness to modernity result in some absurdity, while ascribing lateness to capitalism does not involve such marked risks. Nonetheless, more comprehensive and all-encompassing definitions of capitalism may be incompatible with ascriptions of lateness to capitalism. These various issues are considered in light of the need to name the current world-condition in a period of accelerating environmental crisis. [ABSTRACT FROM AUTHOR]

A single machine scheduling and due-window assignment problem is studied. The objective function is of a minmax type, i.e., the goal is to minimize the maximum cost among all processed jobs. In the classical setting of due-window assignment problems, there are four cost components: for job-earliness, job-tardiness, due-window starting-time and due-window size. We consider in this note a general cost structure which contains in addition to the standard linear earliness and tardiness cost components, a fixed penalty. Thus, early and tardy jobs are penalized also by a fixed cost which is independent of their actual earliness and tardiness values. We show that there are six candidates for the optimal starting time and size of the due-window. Consequently, an efficient, polynomial-time solution algorithm is introduced. [ABSTRACT FROM AUTHOR]

The geographical dispersion of collaborative factories can provide cost-saving potential for manufacturers and lead them to easier fit the global markets. On the other hand, in such networks of factories, coordination is especially important for the just-in-time delivery of orders. This study investigates a new configuration of factories in a network of collaborative manufacturing. In the first stage, some independent suppliers produce and deliver the different components of the final products to the assembly factory. Each supplier can produce a particular component of a final product. In the assembly factory, the final products are assembled. The objective is to determine the optimal schedule of the jobs in each factory to minimize the sum of earliness and tardiness. A mixed-integer formulation for this problem is proposed, which can find the optimal solution for the small-size instances. The iterated local search methods are also developed to cope with larger instances. Computational experiments show that the iterated local search methods outperform the well-known iterated greedy method in literature. [ABSTRACT FROM AUTHOR]

Lot scheduling is a promising manufacturing mode in green logistics that can efficiently save energy and reduce production costs. It has been widely applied to integrate circuit tests in semiconductor factories, textile processing in garment workshops, etc. Each processing lot is of a fixed capacity and identical processing time, and completes more than one job simultaneously. Jobs with sizes and due dates are allowed to be arbitrarily split and processed in consecutive lots. They are delivered immediately upon completion. To the best of our knowledge, in the domain of lot scheduling, there exist no mathematical programming models that describe the above features simultaneously. In this work, we focus on the single machine environment and mainly consider two lot scheduling problems with the objectives of minimizing the maximum lateness and the total tardiness, respectively. For the problems, we first propose new mixed integer linear programming models (solved by commercial solvers), which enable a systematic understanding of the studied problems and serve as a mathematical programming basis for more complicated problems. We then prove that the Earliest Due-Date (EDD) first rule and the Shortest Processing Time (SPT) first rule can optimally solve the two problems, respectively, provided that the due dates and job sizes are agreeable, i.e., a later due date indicates a larger size of job. Experimental results show the efficiency of our methods and managerial insights are drawn. [ABSTRACT FROM AUTHOR]

The characterization of external or mental occurrence " is a famous criterion for dividing rational things into "primary, secondary logical, and secondary philosophical." This criterion, in addition to being semantically ambiguous, implies a two-sided meaning, i.e. relying on "accidentoccurrence" or "quality-qualified," while such duality is not hidden in the concept of "intelligible" and especially "first intelligibles". The division of intelligible things into primary and secondary has another more precedent criterion, which is the precedence and lateness in rationality. In this criterion, primary intelligible things refer to essential concepts and external concomitants, and secondary intelligible things refer to logical concepts and internal concomitants, but philosophical concepts do not have a clear place in this classification. Shihab al-Din Suhrawardi placed philosophical concepts under secondary intelligibles. After him, some others opposed the exclusive enumeration of philosophical intelligible things, but the thinking of Asalat alMahwiyat ( fundamentality of whatness ) led to placing these intelligible things in a limbo of external and mental attributes and deviating from the old criterion. Mulla Sadra considered mental attributes and external concomitants impossible and proved the externality of philosophical concepts. Of course, considering the various meanings of "existence," its position in the first and second rationality differ. Existence in the sense of "truth of existence" is not secondary rational. However, since externality is associated with existence and it is not possible to consider the "general and abstract concept of existence" as the same as external existence, and this meaning is derived from the nature of rationality, it is secondary rational. Nevertheless, since this meaning implies externality, the concurrence of secondary rationality and mentality must be denied. [ABSTRACT FROM AUTHOR]

Multiple production lines can work together to efficiently manufacture certain products. Thus, when capacity is insufficient, it is necessary to decide whether to develop new production lines to ensure the timely completion of all orders. For example, running a new production line for a small number of orders is not cost-effective. Therefore, decision-making involves choosing between paying tardiness costs for a few orders, abandoning some orders, or developing new production lines to maximize efficiency. Additionally, the timely transportation of completed orders is crucial and depends on vehicle usage efficiency. From a transportation perspective, fully loading vehicles is the most efficient, but this may impact the timeliness of orders, leading to potential tardiness costs. By comprehensively considering these aspects, a multi-machine production model is constructed that incorporates transportation batch sequences and uses heuristic algorithms to solve the problem. Finally, designed case examples validate the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

The timely graduation of students is a critical indicator of academic quality assessment. Therefore, universities should use effective predictive systems to identify earlier potential lateness of graduation. This study aimed to improve the K-nearest neighbor (K-NN) algorithm’s ability to predict student on-time graduation. It evaluated K-NN algorithm performance with and without the permutation feature importance (PFI) technique, using a dataset of 460 student graduation records from 2014 to 2017. The training data was oversampled, adjusting the ratio of minority class samples from 13% to 100% of the majority class samples. The result shows that integrating PFI into the K-NN model improved K-NN performance by 10 iterations of the PFI process, N-shuffle varying from 10 to 100 for each iteration, and a minority class sample ratio of 25%. The accuracy score improved from 90.22% to 92.39%, precision from 50.00% to 62.50%, F1-score from 52.63% to 58.82%, while recall remained consistent at 55.56%. The PFI analysis showed that achievement index for the 1st semester or IPS 1 had the least impact on the model. The study suggested using a comprehensive approach to determine the n-shuffle of PFI based on the number of test data for a more accurate feature contribution pattern. [ABSTRACT FROM AUTHOR]

This paper considers an unrelated parallel machine scheduling problem with a common due date where the objective is to minimize the sum of completion times, earliness, and tardiness durations of the jobs. This paper discovers some properties of the considered problem for restricted and unrestricted common due dates. By using these properties, the paper shows the existence of some optimal policies for the problem. Furthermore, optimal policies for the problem are found. According to our main findings, optimal policies for the problem are to increase the number of early jobs, to use the shortest processing time dispatching rule for tardy jobs, and to distribute tardy jobs on machines with tardy workloads that differ from each other as much as possible. These strategies for the problem are new to the literature and the problem has the potential to be extended. Using these strategies, the paper presents a novel heuristic algorithm for the problem that is rarely studied in the literature. Furthermore, the proposed heuristic is compared with two existing algorithms by generating some test instances for experimental study. The experimental study shows that the proposed heuristic outperforms other algorithms. Furthermore, the paper discusses which components of our proposed solution approach have impacts on the solution quality. [ABSTRACT FROM AUTHOR]

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The competition and the volatility of today's production context require that enterprises are able to rapidly adapt to changes, reducing total costs in production. The main relationship between producer and consumer can be conducted through Make-To-Order (MTO) or Make-To-Stock (MTS) systems. This study proposes a hybrid MTS and MTO production system with a dynamic decoupling point. The proposed model concerns the monitoring of earliness and tardiness of customer orders to move the decoupling point towards MTS or MTO. Simulation models are proposed to test the proposed model following periodic or continuous review. The numerical results highlight that the best dynamic approach is the periodic review, which is more effective when the customer demand rate is higher. [ABSTRACT FROM AUTHOR]

The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties. [ABSTRACT FROM AUTHOR]

This paper focuses on the precast supply chain scheduling problem with time-dependent transportation time to minimise the total weighted tardiness (PSCSP_TDT |TWT). In the problem, an order sequence and several job sequences are to be determined simultaneously. At first, through in-depth analysis of problem structure and real data from a precast manufacturer, we approximate the problem into a three-stage order scheduling problem by combining the seven production stages into one differentiation stage, and then explore some useful properties of the schedules for the approximate problem. Subsequently, to solve the small instances for the PSCSP_TDT |TWT, we propose an approximate model-based hybrid dynamic programming and heuristic (AMHDPH) and obtain a lower bound as a by-product of the algorithm. For dealing with medium-or large instances, with considering the complexity of the problem, we propose four approximate model-based hybrid iterated greedy (AMHIG) algorithms by integration of constructive heuristics, structural properties of solutions, an iterated greedy, and a correction heuristic. Comprehensive computational results show that the AMHDPH generates tight lower bounds for small instances and solves the most of small instances to optimality within 60 seconds. Whereas the best AMHIG generates feasible solutions with an average optimality gap below 5 percent for around 70 percent instances. [ABSTRACT FROM AUTHOR]

The distributed flowshop has been a hot topic in research in recent years. This paper considers a distributed permutation flowshop scheduling problem with an assemble machine, so-called the distributed assembly permutation flowshop scheduling problem (DAPFSP), with total tardiness criterion. We propose an effective memetic algorithm (EMA). Firstly, a constructive heuristic combining the well-known earliest due date rule and largest processing time rule is presented for producing a seed sequence. On this basis, an effective initialisation method is used to generate an initial population with a high level of quality and diversity. The EMA uses a new structure of a small iteration nested within a large iteration. Moreover, an improved crossover and mutation expand the solution space in a good direction. According to different operation situations and objects, we design four targeted and flexible local search methods. We evaluate the strategies of the EMA, compare and analyse it with seven efficient algorithms based on the 810 benchmark instances. The experimental results confirm the effectiveness and efficiency of the proposed EMA. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine scheduling problem with truncated learning effect, time-dependent processing time, and past-sequence-dependent delivery time. The delivery time is the time that the job is delivered to the customer after processing is complete. The goal is to determine an optimal job schedule to minimize the total weighted completion time and maximum tardiness. In order to solve the general situation of the problem, we propose a branch-and-bound algorithm and other heuristic algorithms. Computational experiments also prove the effectiveness of the given algorithms. [ABSTRACT FROM AUTHOR]

This research deals with the parallel machine scheduling problem for identical machines that need additional operational resources during the changeover. The production strategy is mainly based on make-to-stock. When the current inventory is less than the quantity of the new order, the corresponding products will be scheduled for production in predetermined production batches that are larger than the quantity of the order. Because the additional resources are limited, batch splitting, which increases the number of changeovers, is not permitted. The objective is to minimize both the makespan and total tardiness. A two-phase methodology is proposed. In the first phase, a mixed-integer program is developed to minimize the makespan. The resulting minimal makespan becomes the constraint in the second phase. An extended mixed-integer program is then developed to minimize the total tardiness. A case study of a plastic pallet manufacturing company is introduced. The experimental results show that the proposed methodology can minimize the makespan and total tardiness efficiently. Moreover, it also shows the promise of the proposed methodology for solving practical applications. [ABSTRACT FROM AUTHOR]

In many real-world scheduling problems, an employee’s productivity fluctuates continuously as learning occurs in human-machine interactions. This paper addresses single-employee scheduling problems considering this continuous learning effect and various objectives. We derive a formula to calculate the makespan and show that the order of the jobs has no effect on the makespan, as opposed to cases where the learning effect is assumed to be discrete. In addition, we demonstrate the optimality of the Shortest Normal Processing Time first (SPT), Earliest Due Date first (EDD), and Shortest Weighted Normal Processing Time first (WSPT) rules for minimizing total completion time, weighted total completion time, and maximum lateness, respectively. A numerical example underscores the key role of the learning rate in task arrangement and provides insights for learning organizations or individuals to manage tasks effectively. [ABSTRACT FROM AUTHOR]

We investigate relationships between scheduling problems with the bottleneck objective functions (minimising makespan or maximal lateness) and problems of optimal colourings of the mixed graphs. The investigated scheduling problems have integer durations of the multi-processor tasks (operations), integer release dates and integer due dates of the given jobs. In the studied scheduling problems, it is required to find an optimal schedule for processing the partially ordered operations, given that operation interruptions are allowed and indicated subsets of the unit-time operations must be processed simultaneously. First, we show that the input data for any considered scheduling problem can be completely determined by the corresponding mixed graph. Second, we prove that solvable scheduling problems can be reduced to problems of finding optimal colourings of corresponding mixed graphs. Third, finding an optimal colouring of the mixed graph is equivalent to the considered scheduling problem determined by the same mixed graph. Finally, due to the proven equivalence of the considered optimisation problems, most of the results that were proven for the optimal colourings of mixed graphs generate similar results for considered scheduling problems, and vice versa. [ABSTRACT FROM AUTHOR]

We consider the problem of a project manager of a matrix organization responsible for the timely completion of projects arriving over time and requiring the usage of a set of resources under his/her purview. A project is defined by a set of activities, precedence relations between activities, resource requirements, a customer due date, and a priority (a weight). Arriving projects are released to a flow control system that monitors the progress of activities and schedules the set of available activities, i.e., activities that are ready to be processed, as appropriate. A critical feature of such control systems is the decision process for choosing the next activity to seize a given resource. This is the focus of this paper. In the past several decades, various heuristic priority rules have been proposed in the literature to support this type of decision in differing settings such as the job shop problem and the deterministic resource-constrained project scheduling problem. A gap exists with respect to testing the various rules all together in the more realistic dynamic-stochastic multi-project environment when the objective is to minimize weighted project tardiness. The purpose of this paper is to fill this gap. Results show that the priority rule "Weighted Critical Ratio and Shortest Processing Time" (W (CR + SPT)) is the best performing rule with respect to minimizing weighted project tardiness. W (CR + SPT) is shown to be a variant of the family of "Modified Due Date" rules first introduced by Baker and Bertrand (J Oper Manage 1(3):37–42, 1982). Repeated application of Duncan's Multiple Range test demonstrates the robustness of our findings. For the environmental parameters (due date tightness, variation of expected activity durations, and utilization of resource), the W (CR + SPT) rule is dominant with respect to weighted project tardiness among the eleven priority rules tested. Only when the number of resources is very modest (either 1 or 3 resources) or under a purely parallel resource network is W (CR + SPT) not the dominant rule. In those cases, its variant W (CR + GSPT) is the best performing rule with respect to weighted project tardiness. [ABSTRACT FROM AUTHOR]

In this paper, we consider a resource overload problem and add a tardiness penalty to the objective function when a prescribed project makespan is exceeded, which enables a trade-off between a balanced resource utilization and a project delay. For the tardiness penalty, we distinguish between a constant and variable delay cost variant. Based on the structural properties of the resource overload problem, we show that the search space of the resource overload problem with tardiness penalty can also be reduced utilizing quasistable schedules. In addition, we discuss the application of these findings to further problems, which include objectives composed of a locally concave and a concave function or a reward structure for an early project completion instead of a tardiness penalty. As solution approaches, we present mixed-integer linear model formulations as well as a novel genetic algorithm with a decoding procedure, which exploits the devised structural properties. The performance of the genetic algorithm is improved by implementing learning methods and utilizing lower bounds. Finally, we present results from experiments on small to medium sized problem instances. [ABSTRACT FROM AUTHOR]

This paper addresses a stochastic job shop scheduling problem with sequence-dependent setup times, aiming to minimize the expected maximum lateness. The stochastic nature is modeled by considering uncertain times between failures (TBF) and uncertain times to repair (TTR). To tackle this problem, a simheuristic approach is proposed, which combines a tabu search (TS) algorithm with Monte Carlo simulation. A total of 320 instances were used to conduct multiple experiments. Instances were generated with two distributions to study the behavior of stochastic TTR and TBF under log-normal and exponential distributions. Firstly, the performance of the simheuristic was evaluated for small instances by comparing it with the simulation of optimal solutions obtained with a mixed-integer linear programming (MILP) model. The simheuristic approach demonstrated an average improvement of around 7% compared to the simulation of MILP model solutions. Secondly, the simheuristic performance was evaluated for medium and large-size instances by comparing it with the simulation of the solutions obtained by the earliest due date (EDD) and process time plus work in the next queue plus negative slack (PT + WINQ + SL) dispatching rules. The results showed an average improvement of around 11% compared to EDD and 14% compared to PT + WINQ + SL. Furthermore, the results highlight that even when the two distributions have the same expected value and coefficient of variation, they can yield different expected maximum lateness values. This emphasizes the importance of precise distribution fitting when solving real cases to achieve effective scheduling performance. [ABSTRACT FROM AUTHOR]

Both walk-in and online customers appear in many service systems such as restaurants. Online customers are often given a target time for pick up, and the quality of the food/beverage can degrade if it is ready before the arrival of online customers. This distinctive feature brings essential difficulties in the analysis and control of such systems. In "Technical Note: Asymptotically Optimal Control of Omnichannel Service Systems with Pick-up Guarantees," Gao, Huang, and Zhang study the optimal control problem of such service systems based on a two-class single server queueing model. The paper proposes a nearly-optimal policy that keeps the server idle when the queue of online customers drops below a threshold and there are no walk-in customers. Motivated by the recent popularity of omnichannel service systems, we analyze the joint admission and scheduling control of a queueing system with two classes of customers: online and walk-in. Unlike walk-in customers, online customers are given a target time for pick up upon placing an order. Thus, in addition to minimizing the waiting costs of walk-in customers and the rejection cost of both classes, we need to minimize the earliness and tardiness costs of online customers. Such a distinctive objective makes the control problem difficult to analyze. We develop a novel analysis by adopting the idea of proving H = λ G to establish an asymptotic relationship between the waiting time cost and the queue length cost under general control policies in the heavy-traffic regime. We also design a policy and show that it is asymptotically optimal. Funding: X. Gao's research is supported in part by the Hong Kong Research Grants Council [Grants 14201520, 14201421, and 14212522]. J. Huang's research is supported in part by the Hong Kong Research Grants Council [Grants 14500819, 14505820, and 14501621] and the National Natural Science Foundation of China [Grant 72222023]. J. Zhang's research is supported in part by the Hong Kong Research Grants Council [Grants 16208120 and 16214121]. [ABSTRACT FROM AUTHOR]

Problem definition: Drone delivery has recently garnered significant attention due to its potential for faster delivery at a lower cost than other delivery options. When scheduling drones from a depot for delivery to various destinations, the dispatcher must take into account the uncertain wind conditions, which affect the delivery times of drones to their destinations, leading to late deliveries. Methodology/results: To mitigate the risk of delivery delays caused by wind uncertainty, we propose a two-period drone scheduling model to robustly optimize the delivery schedule. In this framework, the scheduling decisions are made in the morning, with the provision for different delivery schedules in the afternoon that adapt to updated weather information available by midday. Our approach minimizes the essential riskiness index, which can simultaneously account for the probability of tardy delivery and the magnitude of lateness. Using wind observation data, we characterize the uncertain flight times via a cluster-wise ambiguity set, which has the benefit of tractability while avoiding overfitting the empirical distribution. A branch-and-cut (B&C) algorithm is developed for this adaptive distributionally framework to improve its scalability. Our adaptive distributionally robust model can effectively reduce lateness in out-of-sample tests compared with other classical models. The proposed B&C algorithm can solve instances to optimality within a shorter time frame than a general modeling toolbox. Managerial implications: Decision makers can use the adaptive robust model together with the cluster-wise ambiguity set to effectively reduce service lateness at customers for drone delivery systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101049 and 72232001], the Natural Science Foundation of Liaoning Province [Grant 2023-BS-091], the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045], and the Major Project of the National Social Science Foundation [Grant 22&ZD151]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0339. [ABSTRACT FROM AUTHOR]

The multi-objective integrated process planning and scheduling (MOIPPS) problem has a huge search space and complex technical constraints. Therefore, there is considerable difficulty in obtaining efficient solutions, and hence, metaheuristic-based solution algorithms have been actively introduced. In our paper, we propose a method to obtain a set of Pareto solutions using a firefly algorithm hybridized with a genetic algorithm for the MOIPPS problem. We considered a MOIPPS problem model that simultaneously optimizes the makespan, total flow time and total tardiness, maximum machine workload and total machine workload. Several different scale instances have been employed to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm has excellent performance in solving the MOIPPS problem. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-aware production schedule model for flexible manufacturing systems (FMSs) that aims to minimise energy costs and carbon tax while considering rising energy costs and environmental policies motivated by climate change concerns. The model is based on a sequence-dependent five-machine FMS scheduling problem, which is flexible in levels of parts, tools, machines, and routings. The proposed model is solved as a multi-objective problem, with tardiness and energy consumption as primary goals and carbon footprint reduction policies incorporated into the framework as control strategies. The machine ON/OFF strategy is also introduced to reduce idle energy. The study analyses the trade-off between minimising tardiness and carbon emissions to achieve both service level and environmental sustainability of the FMS. The proposed model reduced total energy consumption and carbon emissions by 20% and 21%, respectively, without compromising manufacturing deadlines. Future research may explore the integration of renewable energy sources and storage systems, considering more complex manufacturing scenarios, such as stochastic demands, machine failures, and workforce constraints. [ABSTRACT FROM AUTHOR]

Based on the replenishment strategy for the basic inventory level of parts and components, a two‑stage stochastic optimization model is constructed with multi‐period uncertain order product demands. In the first stage, the basic inventory level of components is the decision variable and needs to be determined at the condition of unknown demands, and the objective is to achieve the maximum system revenue. In the second stage, the order assignment variable is designed when the order due date constraint is required, and the objective of this stage is to maximize the expected order revenue by taking order revenue and tardiness penalty into account. Additionally, a single‑stage deterministic model by transforming the uncertain demand to the determined expected demand is presented and the objective function value of this deterministic model is compared with that of the stochastic model. The numerical experiment results show the performance of the stochastic model is worse than that of the deterministic model, which indicates some value of the unknown information. Meanwhile, a sensitive analysis is implemented for the parameters in the model and a strategy is put forward to improve the system revenue in practice. [ABSTRACT FROM AUTHOR]

In multi-project management, effectively allocating limited resources to projects is crucial for ensuring the success of the project portfolio and timely delivery. There are two approaches to managing renewable resources based on project execution environment and resource characteristics: resource sharing and resource dedication. This study explores a novel resource dedication-transfer policy, where renewable resources are dedicated to each project during execution and can be transferred to other projects once the corresponding project is completed. Additionally, real-world multi-project scheduling often faces uncertainties, with the most common issue being varying durations of activities. To address this, a hierarchical multi-objective optimization model is proposed under the resource dedication-transfer policy. This model aims to allocate dedicated resources to the project portfolio at a tactical level and schedule individual activities at an operational level. The specific objectives include minimizing the total weighted tardiness of all projects, minimizing the total fluctuating costs of resources, and maximizing solution robustness in the presence of activity duration variabilities. The proposed solution methodologies include an adjusted large neighborhood search (ALNS) and a customized NSGA-II algorithm. Both algorithms employ a hybrid coding scheme of "project-buffer-resource-activity" list to represent feasible solutions. Specially, the ALNS introduces new destroy-repair neighborhood operators and a hypervolume-based selection criterion to enhance its performance. Experimental results demonstrate that while both algorithms have their advantages for small-scale instances, the ALNS is more effective for large-scale instances. Finally, the derived Pareto solutions from the proposed method are further evaluated through a simulation of multi-project execution under different levels of activity duration variabilities. [ABSTRACT FROM AUTHOR]

An important and realistic class of scheduling problems is considered in this paper: the total earliness and tardiness minimization in the blocking flowshop, where there is no intermediate buffer between machines. Blocking occurs when a completed item or product remains on the machine until the next machine is available. We proposed a new hybrid evolutionary algorithm: the Genetic Iterated Greedy Algorithm (GIGA). In our innovative solution approach, a genetic algorithm presents a hybrid crossover based on the Iterated Greedy metaheuristic. The hybrid crossover considers the Hamming distance as an indicator of the diversity of the current population. In the first generations, the crossover will adopt larger values for the destruction parameter, and this value is gradually reduced throughout the search process. Our proposal is compared to four competitive metaheuristics reported for earliness and tardiness flowshop. Two performance indicators are considered: the Average Relative Percentage Deviation (ARPD) and the Success Rate (SR). Based on the statistical analysis of the computational experimentation, our GIGA outperformed all the implemented algorithms of the literature with statistical significance. Concerning the performance indicators, GIGA achieved ARPD = 0.02% and SR = 83.5%, pointing to the superiority of the proposed solution approach. [ABSTRACT FROM AUTHOR]

We study the single-machine primary–secondary scheduling problems in which the total tardiness is the primary criterion and the secondary criteria are the maximum lateness, the (weighted) number of tardy jobs, and the total weighted tardiness, respectively. These problems are known to be NP-hard, but their exact complexities (strongly NP-hard or pseudo-polynomially solvable) were posed by (in: Pardalos (ed) Complexity in numerical optimization, World Scientific, 1993) as open problems. In this paper, we show that these problems are solvable in pseudo-polynomial time. [ABSTRACT FROM AUTHOR]

The distributed no-idle permutation flowshop scheduling problem has gained significant attention as a prominent area of research in recent years, particularly in industries where setup operations are so expensive that reactivating the machines is not cost-effective. This study addresses an extension of the distributed permutation flowshop scheduling problem with no-idle and due window constraints. The aim is to determine the job assignments to the factories and their sequences in each factory that provide the minimum total weighted earliness and tardiness (TWET) penalties considering due windows. This study is the first to formulate this problem, offering four different mathematical models, and presents a benchmark to examine different problem cases that may arise in practical applications. Furthermore, to effectively solve the diverse problem instances, two hybrid metaheuristic algorithms based on the Iterated Greedy are proposed. These metaheuristics exhibit promising capabilities, enabling the solution of problem instances involving up to 500 jobs. To assess the effectiveness of the proposed models and algorithms, extensive numerical experiments are conducted, facilitating a thorough evaluation and comparison of their performances. [ABSTRACT FROM AUTHOR]

This paper studies a new variant of integrated production scheduling and vehicle routing problem where production of customer orders are performed under job-shop environment and order deliveries are made by a heterogeneous fleet of vehicles, each of which is allowed to take multiple trips. Two conflicting objectives are considered, namely minimisation of the total amount of CO2 emitted by the vehicles and minimisation of maximum tardiness resulting from late deliveries. To this end, we present a bi-objective mixed-integer programming formulation. Augmented ε-Constraint (Augmecon) method is implemented to find Pareto optimal solutions. Due to problem complexity, Augmecon cannot provide solutions even with small-sized problems. Thus, we adopt Pareto Local Search (PLS) and non-dominated sorting genetic algorithm-II (NSGA-II) for practical sized instances. For small-sized instances involving 5, 6, and 7 customers, experimental results indicate that CPU time of Augmecon are 11, 84, and 524 sec, respectively with an average number of Pareto efficient solution of 3.5. In terms of hypervolume index, Augmecon shows the best performance, followed by NSGA-II with 11.32% and PLS with 20.75% degradation for small-sized instances. For medium and large-sized instances, PLS shows worse performance than NSGA-II by 16.87% and 40.48%. [ABSTRACT FROM AUTHOR]

Multi-resource constrained dynamic workshop scheduling is a complex and challenging task in discrete manufacturing. In this paper, to obtain a high-performance scheduling in limited time, this problem is modelled into a Markov decision process, and solved by proximal policy optimisation algorithm, which can learn from the simulated workshop environment directly. A multi-modal hybrid neural network is used in the model to make good use of numerical state features representing workshop environment information and graphical state features representing constraint information during the learning process. Multi-label technique is used in this paper to decouple the output acts of jobs, machines, tools, and workers. Action mask technique coding the constraints is also used to prune invalid exploration. The experimental results show that compared with heuristic rules such as weighted shortest processing time, weighted modified due date, weighted cost over time, apparent tardiness cost and other reinforcement learning methods such as DeepRM and DeepRM2, the performance of the proposed method is at least 1.138 % better in scheduling penalty. [ABSTRACT FROM AUTHOR]

Scheduling jobs in a no-wait flow shop with the objective of minimising total earliness and tardiness is the problem addressed in this paper. Idle time may be needed on the first machine due to the no-wait restriction. A model is developed that shows additional idle can be inserted on the first machine to help reduce earliness. Several dispatching heuristics previously used in other environments were modified and tested. A two-phased procedure was also developed, estimating additional idle in the first phase, and applying dispatching heuristics in the second phase. Several versions of an insertion improvement procedure were also developed. The procedures are tested on instances of various sizes and due date tightness and range. The results show the two-phase heuristics are more effective than the simple rules, and the insertion search improvement procedure can provide considerable improvements. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic-order picking (DOP) framework where batches and picker routes are continuously modified in response to urgent orders arrival and disruption events. In the proposed framework, we consider conflicts between pickers in narrow aisles to ensure a smooth replanning function. For this purpose, we first formulate a static joint-order batching and sequencing model (J-OBS) that takes into account congestion time. Then, we propose a replanning model (D-J-OBS) that utilises real-time input from multiple information systems. As a solution approach and due to the NP-hardness of the problem, we use a tabu search algorithm to solve practical sizes of the static and the dynamic problems. The results indicate that the DOP framework generates significant tardiness savings compared to online-order picking, especially for systems with high degree of dynamism. [ABSTRACT FROM AUTHOR]

The scheduling of (parallel) serial-batch processing machines is a task arising in many industrial sectors. In the metal-processing industry for instance, cutting operations are necessary to fabricate varying metal pieces out of large base slides. Here, the (cutting) jobs have individual, arbitrary base slide capacity requirements (sizes), individual processing times and due dates, and specific material requirements (i.e. each job belongs to one specific job family, whereby jobs of different families cannot be processed within the same batch and thus are incompatible). In addition, switching of base metal slides and material dependent adjustments of machine parameters cause sequence-dependent setup times. All these conditions need to be considered while minimising total weighted tardiness. For solving the scheduling problem, a mixed-integer program and several tailor-made construction heuristics (enhanced by local search mechanisms) are presented. The experimental results show that problem instances with up to five machines and 60 jobs can be tackled using the optimisation model. The experiments on small and large problem instances (with up to 400 jobs) show that a purposefully used batch capacity limitation improves the solution quality remarkably. Applying the best heuristic to the data of two real-world application cases shows its huge potential to increase delivery reliability. [ABSTRACT FROM AUTHOR]

This paper offers a systematic and doxographical reconstruction of the phenomena of delay and lateness in Martin Heidegger's thought between 1919 and 1930. In the first section, we trace the ambiguities that permeate the nominalization and formalization of delay in order to thematize immediately after the disruptive structure of four modes of delay understood as constitutive within Heidegger's early work. We account for the way in which delay carries an illuminating potential that phenomenologically ensures the evidencing of the constituent dimension. In the second section, we descend to the immanent transformations of Heidegger's philosophy in order to account for two possibilities of philosophy's self-understanding on the basis of delay and lateness, precisely from two modes of relation with the constituent dimension of the appearing: on the one hand, a mode marked by crepuscular lateness, and on the other hand, an executive mode based on auroral delay. [ABSTRACT FROM AUTHOR]

In this paper, we study the scheduling of n jobs on a single machine. Each of the n jobs will be processed without interruption and becomes available for processing at time zero. The goal is to find a processing order for the jobs, minimizing the total completion time, total late work, total earliness time, and maximum earliness maximum tardiness. The posed problems in this paper are as follows: The first problem is to minimize the multi-criteria, which includes minimizing the total completion time, total late work, total earliness time, maximum earliness, and maximum tardiness that are denoted by ΣCj, ΣVj, ΣΕj, Emax, and Tmax, respectively. The second problem is to minimize the multi-objective functions (ΣCj + ΣVj, +ΣΕj; + Emax + Tmax). The theoretical section will present the mathematical formula for the discussed problem. Because these problems are NP- hard problems. It is difficult to determine the efficient (optimal) solution set for these problems. Some special cases are shown and proven to find efficient (optimal) solutions to the discussed problem. The significance of the dominance rule can be applied to problems to improve and to get good solutions that will be highlighted. [ABSTRACT FROM AUTHOR]

We consider a multiobjective scheduling problem, with the aim of minimizing the maximum lateness and the makespan on identical machines, when the number of machines is fixed. This paper proposes an exact algorithm (based on a dynamic programming) to generate the complete Pareto Frontier in a pseudo-polynomial time. Moreover, four heuristics have been proposed in order to optimize our algorithm. Then, we present a Polynomial Time Approximation Scheme (PTAS) to generate an approximate Pareto Frontier. In this scheme, we use a simplification technique based on the merging of jobs. Furthermore, we present a Fully Polynomial Time Approximation Scheme (FPTAS) to generate an approximate Pareto Frontier, based on the conversion of the dynamic programming algorithm. The proposed FPTAS is strongly polynomial. Finally, some numerical experiments are provided in order to compare the proposed approaches. [ABSTRACT FROM AUTHOR]

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This paper studies the single-machine group scheduling problem with convex resource allocation and learning effect. The number of jobs in each group is different, and the corresponding common due dates are also different, where the processing time of jobs follows a convex function of resource allocation. Under common due date assignment, the objective is to minimize the weighted sum of earliness, tardiness, common due date, resource consumption, and makespan. To solve the problem, we present the heuristic, simulated annealing, and branch-and-bound algorithms. Computational experiments indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

We consider the well-known optimal decomposition algorithm for the tardiness problem. The algorithm presents conditions which determine the positions a job could occupy in an optimal sequence. This resulted in optimal solutions for up to 100 jobs. We analyze these conditions and present simplified conditions. We then study a more recent Rule which when combined with these conditions resulted in optimal solutions for up to 500 jobs. We present several properties under which this recent rule is satisfied. We provide mathematical proofs for our properties. We believe that our study will enable more theoretical research in this field and will eventually enable optimal solutions for very large job sets. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the minmax single-machine scheduling problem with the slack due window, where the actual processing time of a job is the deterioration function of its starting time. The goal is to obtain the optimal schedule of the job and the related decision variables of the slack due window to minimize the linear combination cost function of the maximum earliness (tardiness) of the job and the due window. We show that this problem is solvable in O(n) time, and we carry out the numerical analysis, where $ n $ is the number of jobs. [ABSTRACT FROM AUTHOR]