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Motivated by a practical problem, this paper investigates the integrated planning of maintenance operations and workload allocation on a set of machines in a workshop. Given quantities of products to be produced per period on a planning horizon must be processed on unrelated flexible machines. Moreover, each machine has to undergo one or more maintenance operations that must be planned within a given time window and impact products differently. The main goal is to find a feasible plan that satisfies the machine capacity by allocating the production quantities to machines and assigning maintenance operations as late as possible in their time windows. Various original mathematical models are presented. In particular, we propose models that allow maintenance operations and some production quantities to overlap two consecutive periods. Computational experiments based on industrial data show that allowing this overlapping helps the earliness of maintenance operations to be significantly reduced in the most difficult instances, going for example from a total of 14 periods to only 1 period, and by more than 35% on average. [ABSTRACT FROM AUTHOR]

The complexity of decision-making for companies buying transportation services has increased due to the presence of more options and pricing schedules for transportation. Many companies make transportation and inventory decisions in an uncoordinated way and select only one transportation mode, missing opportunities for logistics cost savings. The experimental study in this paper is based on a real-world decision problem faced by a Scandinavian company that distributes fast-moving consumer goods and wants to determine its transportation strategy. We propose a novel multi-mode lot-sizing model with dynamic deterministic demand to illustrate the cost impact of accurately modelling piecewise-linear transportation costs and allowing a more flexible usage of transportation modes when planning order replenishments. We compare three transportation strategies with increasing degrees of flexibility: two single mode strategies, where one strategy is more flexible than the other, and a multi-mode strategy. We conclude that managers can significantly reduce costs by increasing the flexibility of mode selection in transportation strategies. [ABSTRACT FROM AUTHOR]

In this paper, a practical relevant operational production planning problem in complex manufacturing systems is addressed. In this problem, lots are planned individually to provide a more detailed plan than approaches that only consider production quantities. A three-step approach, which is currently fully integrated and used in a Decision Support System, is then introduced. This work follows the one of Mhiri et al. [2018. "Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing." IEEE Transactions on Semiconductor Manufacturing 31 (1): 62–75] who addressed this problem. We push the approach a step further by introducing new optimisation possibilities through new smoothing rules, whose performance is studied according to different indicators. Furthermore, we present the production planning process in which the decision support tool is embedded and how it bridges the gap between the upper and lower planning levels. [ABSTRACT FROM AUTHOR]

This paper proposes a novel global scheduling approach for cycle time control strategy in large complex manufacturing systems with multiple workcentres, such as semiconductor manufacturing systems. The interaction between workcentres is taken into account by using global information such as release quantities, the Work-In-Process (WIP), cycle time targets and machine capacities. Local scheduling decisions in workcentres are steered by production targets, i.e. quantities of products to complete in each operation and each period on a scheduling horizon. These global production targets are determined by a mathematical model (global scheduling model), which optimises the satisfaction of cycle time targets. One of the major innovations of the proposed model is that it relies on the temporal trace of the WIP. The mathematical model is coupled with a generic multi-method simulation model for evaluation purpose. Computational experiments conducted on industrial data show that our global scheduling approach efficiently controls the cycle times of products. [ABSTRACT FROM AUTHOR]

This paper addresses a multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with different types of resources and complex industrial constraints, which originates from SNCF heavy maintenance factories. Two objective functions, that have been rarely addressed in the literature, are independently considered: (i) Minimization of the sum of the weighted tardiness of the projects and (ii) Minimization of the sum of the weighted duration of the projects. A time-indexed mixed-integer linear programming model is presented with both resource assignment and capacity constraints. To solve large instances with several thousand activities, a new memetic algorithm combining a novel hybrid simulated genetic algorithm with a simulated annealing is implemented. The memetic algorithm is compared with popular solution approaches. Computational experiments conducted on real instances and benchmark instances validate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Motivated by real challenges on energy management faced by industrial firms, we propose a novel way to reduce production costs by including the pricing of electricity in a multi-product lot-sizing problem. In incentive-based programs, when electric utilities face power consumption peaks, they request electricity-consuming firms to curtail their electric load, rewarding the industrial firms with incentives if they comply with the curtailment requests. Otherwise, industrial firms must pay financial penalties for an excessive electricity consumption. A two-stage stochastic formulation is presented to cover the case where a manufacturer wants to satisfy any curtailment request. A chance-constrained formulation is also proposed, and its relevance in practice is discussed. Finally, computational studies are conducted to compare mathematical models and highlight critical parameters and show potential savings when subscribing incentive-based programs. We show that the setup cost ratio, the capacity utilisation rate, the number of products and the timing of curtailment requests are critical parameters for manufacturers. [ABSTRACT FROM AUTHOR]

This document is a special issue of the International Journal of Production Research focused on collaborative production and maintenance in the context of big data and Industry 4.0. It discusses the impact of the fourth industrial revolution on manufacturing, highlighting the importance of interconnectedness, automation, and data-driven decision-making. The document emphasizes the need for proper analysis of the vast amount of data collected through smart sensors and the potential for collaboration between humans, machines, and data to improve production and maintenance processes. The special issue includes seven selected papers that address various aspects of collaborative production and maintenance, such as fault diagnosis, maintenance scheduling, and maintenance policy evaluation. The papers offer valuable insights, innovative approaches, and practical applications in the field. The editors hope that this special issue will inspire further research and collaboration in the era of big data and Industry 4.0. [Extracted from the article]

For the sake of product yield and quality considerations, time constraints (TCs) are imposed between process operations in various multi-product manufacturing systems. Often spanning a number of operations, time constraints tend to follow each other in close succession and overlap, forming thus time constraint tunnels (TCTs). The regulation problem of releasing lots in these time constraint tunnels is particularly challenging in semiconductor manufacturing systems, because of re-entrant flows, machine heterogeneity and high mix low volume (HM-LV) production configurations, which are typical in many wafer fabrication facilities. In such an evolving and time-varying context, this paper proposes a sequential sampling-based approach to estimate the probability that, prior to its release, a lot leaves a given time constraint tunnel on time. The proposed approach proves to be competitive in various respects by (i) taking into account industry specific features, (ii) being industrially tractable, and (iii) being sensitive and responsive to the current manufacturing system. Based on real-life instances, numerical experiments highlight the computational effectiveness and the industrial soundness of the proposed problem modelling together with the solution approach. [ABSTRACT FROM AUTHOR]

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications. [ABSTRACT FROM AUTHOR]

Motivated by the high investment and operational metrology cost, and subsequently the limited metrology capacity, in modern semiconductor manufacturing facilities, we model and solve the problem of optimally assigning the capacity of several imperfect metrology tools to minimise the risk in terms of expected product loss on heterogeneous production machines. In this paper, metrology tools can differ in terms of reliability and speed. The resulting problem can be reduced to a variant of the Generalized Assignment Problem (GAP), the Multiple Choice, Multiple Knapsack Problem (MCMKP). A Lagrangian heuristic, including multiple feasibility heuristics, is proposed to solve the problem that are tested on randomly generated instances. [ABSTRACT FROM AUTHOR]

This paper aims at linking the work presented in Dauzère‐Pérès et al. (1998) and more recently in Kasapidis et al. (2021) on the multiresource flexible job‐shop scheduling problem with nonlinear routes or equivalently with arbitrary precedence graphs. In particular, we present a mixed integer linear programming (MIP) model and a constraint programming (CP) model to formulate the problem. We also compare the theorems introduced in Dauzère‐Pérès et al. (1998) and Kasapidis et al. (2021) and propose a new theorem extension. Computational experiments were conducted to assess the efficiency and effectiveness of all propositions. Lastly, the proposed MIP and CP models are tested on benchmark problems of the literature and comparisons are made with state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

This paper outlines a mathematical model to solve a scheduling problem for a company engineering and producing propellers to order. Nonås and Olsen (Comput Oper Res 32(9):2351–2382, 2005) have previously introduced a Mixed Integer Programming model for this production setting with the objective of minimizing the total tardiness. The mathematical model could however not be used to solve realistic sized problem instances, because of the very large solution time. We propose a new time indexed formulation that can solve most industrial problem instances in less than 10 min. This work is further extended by taking into account limited storage capacity and by proposing different methods to balance between total tardiness and maximum tardiness. We illustrate how the solution time and the criteria change for different setups of the mathematical model and suggest which setup to use for different scenarios. The paper also discusses how the new model can be extended to include unexpected events such as emergency orders and unavailable production equipment. [ABSTRACT FROM AUTHOR]

Performance evaluation, and in particular cycle time estimation, is critical to optimise production plans in high-tech manufacturing industries. This paper develops a new aggregation model based on queuing network, so-called queue-based aggregation (QAG) model, to estimate the cycle time in a production system. Multiple workstations in serial and job-shop configurations are aggregated into a single-step workstation. The parameters of the aggregated workstation are approximated based on the parameters of the original workstations. Numerical experiments indicate that the proposed QAG model is computationally efficient and yields fairly accurate results when compared to other aggregation approaches in the literature. [ABSTRACT FROM AUTHOR]

The measurement of lots to check process quality is crucial but also a non-added value operation in manufacturing systems. This paper is motivated by semiconductor manufacturing, where metrology tools are expensive, thus limiting metrology capacity which must be optimally used. In a context where multiple heterogeneous machines are sharing a common metrology workshop, the problem of minimising risk while considering metrology capacity arises. An integer linear programming (ILP) model is presented, which corresponds to a multiple-choice knapsack problem. Simple rounding heuristics are proposed, whose results on randomly generated instances are compared with the optimal solutions obtained using a standard solver on the ILP. Additionally, numerical experiments on industrial data are presented and discussed. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses articles in the issue on topics including semiconductor supply chains, production planning, and demand forecasting of nonvolatile memory in semiconductor manufacturing.

Production planning and scheduling are usually performed in a sequential manner, thus generating unfeasibility conflicts. Moreover, solving these problems in complex manufacturing systems (with several products sharing different resources) is very challenging in production management. This paper addresses the solution of multi-item multi-period multi-resource single-level lot-sizing and scheduling problems in general manufacturing systems with job-shop configurations. The mathematical formulation is a generalisation of the one used for the Capacitated Lot-Sizing Problem, including detailed capacity constraints for a fixed sequence of operations. The solution method combines a Lagrangian heuristic, determining a feasible production plan for a fixed sequence of operations, with a sequence improvement method which iteratively feeds the heuristic. Numerical results demonstrate that this approach is efficient and more appropriate than a standard solver for solving complex problems, regarding solution quality and computational requirements. [ABSTRACT FROM PUBLISHER]

This paper proposes an efficient heuristic algorithm for solving a complex batching and scheduling problem in a diffusion area of a semiconductor plant. Diffusion is frequently the bottleneck in the plant and also one of the most complex areas in terms of number of machines, constraints to satisfy and the large number of lots to manage. The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on machines. The problem is modelled and solved using a disjunctive graph representation. A constructive algorithm is proposed and improvement procedures based on iterative sampling and Simulated Annealing are developed. Computational experiments, carried out on actual industrial problem instances, show the ability of the iterative sampling algorithms to significantly improve the initial solution, and that Simulated Annealing enhances the results. Furthermore, our algorithm compares favourably to an algorithm reported in the literature for a simplified version of our problem. The constructive algorithm has been embedded in software and is currently being used in a semiconductor plant. [ABSTRACT FROM AUTHOR]

We consider the single item uncapacitated lot-sizing problem with production time windows, lost sales, early productions and backlogs over a planning horizon of T periods. In this context, a demand not processed within its time window can either be lost (lost sale), satisfied from a production that is processed before the release period of the demand (early production) or satisfied from a production that occurs after the demand due period (backlog). We present several properties of the optimal solution for different variants of the problem when production time windows are non-customer specific. We propose dynamic programming algorithms to solve the examined problems in O(T2). [ABSTRACT FROM AUTHOR]

This paper presents a study of a practical job-shop scheduling problem modelled and solved when helping a company to design a new production workshop. The main characteristics of the problem are that some resources are flexible, and blocking constraints have to be taken into account. The problem and the motivation for solving it are detailed. The modelling of the problem and the proposed resolution approach, a genetic algorithm, are described. Numerical experiments using real data are presented and analysed. We also show how these results were used to support choices in the design of the workshop. [ABSTRACT FROM AUTHOR]

The papers are grouped into three categories: AI methods for manufacturing systems, AI developments specifically in semiconductor manufacturing, and AI in additive manufacturing and maintenance. They combine a deep neural network model and Markov decision processes (MDP) to rapidly generate near optimal dynamic control policies for problems that are too large to be only solved by MDP, thus showing the potential of machine learning in controlling unreliable manufacturing systems. [Extracted from the article]

In this paper, we propose a metaheuristic for solving an original scheduling problem with auxiliary resources in a photolithography workshop of a semiconductor plant. The photolithography workshop is often a bottleneck, and improving scheduling decisions in this workshop can help to improve indicators of the whole plant. Two optimization criteria are separately considered: the weighted flow time (to minimize) and the number of products that are processed (to maximize). After stating the problem and giving some properties on the solution space, we show how these properties help us to efficiently solve the problem with the proposed memetic algorithm, which has been implemented and tested on large generated instances. Numerical experiments show that good solutions are obtained within a reasonable computational time. [ABSTRACT FROM AUTHOR]

This paper considers a supply chain management problem which integrates production, inventory, and distribution decisions. The supply chain is composed of one supplier production facility and several retailers located in a given geographic region. The supplier is responsible for the production and the replenishment of the inventory of retailers, in a vendor managed inventory (VMI) context. The distance between retailers is negligible compared to the distance between the supplier and the retailers' region. Thus, for each vehicle, there is a major fixed cost for traveling to the cluster of retailers and a minor fixed cost for visiting each individual retailer. The problem consists of determining quantities to be produced, quantities to be delivered to retailers, vehicles to be used, and retailers to be serviced by each vehicle. This problem is an extension of the one warehouse multi-retailer problem with the consideration of production planning and storage and vehicle capacity limitations in addition to fixed vehicle utilization costs and retailer servicing costs. The objective is to minimize a total cost composed of production, transportation, and inventory holding costs at the supplier and at the retailers. Two mixed integer linear programming formulations are proposed and six families of valid inequalities are added to strengthen these formulations. Two of these families are new and the others are adapted from the literature. The numerical results show that the valid inequalities considerably improve the quality of the formulations. Moreover, the parameters that influence the most computational times are analyzed. [ABSTRACT FROM AUTHOR]

We consider a two-level dynamic lot-sizing problem where the first level consists of N finished products competing for a single type of purchased raw material in the second level. While the procurement and production capacities are unlimited, the storage capacity of the raw material is limited and must be carefully managed. The goal is to simultaneously determine a replenishment plan for the raw material and optimal production plans for the finished products on a horizon of T periods while minimizing production, purchasing, setup and inventory holding costs. The problem is modeled using mixed-integer linear programs and solved using both a Lagrangian relaxation-based heuristic and a commercial mixed-integer linear programming solver. Learning capabilities are integrated in the Lagrangian relaxation to update step size in the subgradient algorithm. The computational results show that the Lagrangian heuristic outperforms the solver on different formulations, in particular for large problems with long time horizons. [ABSTRACT FROM AUTHOR]

Increased global container trade and strong competition are boosting the need for efficient container terminals. Using internal transport resources efficiently reduces transfer times and operating costs. This article addresses the allocation of straddle carriers at a tactical level at intermodal container terminals via optimization and simulation. Our objective is to reduce overall delays at the terminal-with special focus on delays of inland transport modes (rail, road and waterway). We introduce a notation to describe different service strategies and delay criteria and analyze the complexity of different service strategies. A generic mixed integer linear program-that can be easily adapted to different service strategies-models the allocation problem based on a network flow representation of the terminal. We conduct a case study for a container terminal at the Grand Port Maritime de Marseille to evaluate the proposed allocation: we adapt the optimization model to the specific terminal, implement a discrete event simulation model and conduct experiments on actual data. Results show that the allocation proposed by the optimization model reduces delays at the terminal and performs well in a stochastic environment. [ABSTRACT FROM AUTHOR]

This paper presents a new Lagrangian heuristic to solve the general capacitated single item lot sizing problem (CSILSP) where the total costs of production, setup, and inventory are to be minimized. We introduce a pre-smoothing procedure to transform the problem into a CSILSP with non-customer specific time windows and relax constraints that are specific to the CSILSP. The resulting uncapacitated single item problems with non-customer specific production time windows can be solved in polynomial time. We also analyze the performance of the Lagrangian heuristic for solving the CSILSP with non-customer specific time windows. Finally, the heuristic is adapted to the customer specific case. The introduction of pre-smoothing and the relaxation of CSILSP-specific constraints help to decrease the gap between lower bounds and upper bounds from 26.22 to 1.39 %, on average. The heuristic can be used to solve aggregate production planning problems, or can be integrated into a general procedure to solve more complex lot sizing problems. [ABSTRACT FROM AUTHOR]

Scheduling in semiconductor manufacturing is of vital importance due to the impact on production performance indicators such as equipment utilization, cycle time, and delivery times. With the increasing complexity of semiconductor manufacturing, ever-new products and demanding customers, scheduling plans for efficient production control become crucial. Scheduling and control are mutually dependent as control requires information from scheduling, for example, where jobs are processed, and scheduling requires control information, for example, on which equipment operations can be processed. Based on a survey of the literature, this article proposes a review and an outlook for the potential improvements by binding scheduling decisions and information coming from advanced process control systems in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

This paper studies the scheduling of lots (jobs) of different product types (job family) on parallel machines, where not all machines are able to process all job families (non-identical machines). A special time constraint, associated to each job family, should be satisfied for a machine to remain qualified for processing a job family. This constraint imposes that the time between the executions of two consecutive jobs of the same family on a qualified machine must not exceed the time threshold of the family. Otherwise, the machine becomes disqualified. This problem comes from semiconductor manufacturing, when Advanced Process Control constraints are considered in scheduling problems. To solve this problem, two Mixed Integer Linear Programming models are proposed that use different types of variables. Numerical experiments show that the second model is much more effective, and that there is a trade-off between optimizing the scheduling objective and maximizing the number of machines that remain qualified for the job families. Two heuristics are also presented and studied in the numerical experiments. [ABSTRACT FROM AUTHOR]

This paper deals with a parallel machine scheduling problem whose objective is to minimize a regular step total cost function. A real world application of the problem is presented, and Mixed Integer Linear Programming models are described for the cases with and without release dates, as well as a dedicated constraint generation procedure. Experimental results are reported and discussed to evaluate the relevance of the different approaches on well-known special cases and the general case. [ABSTRACT FROM AUTHOR]

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations. [ABSTRACT FROM AUTHOR]

This article deals with the use of discrete-event simulation as a decision support tool for estimating the impact of Radio Frequency IDentification (RFID) technologies on processes and activities of biological sample storage areas (called biobanks). We first give a detailed description of biobank flows and identify subprocesses improved using RFID technologies. Several indicators, such as inventory reliability and human resource utilization, are compared and discussed for different scenarios involving the use of different RFID technologies. A special emphasis is put on the so-called rewarehousing activity, which RFID makes possible and which consists in reassigning tubes to empty places when boxes are emptied. For this particular activity, optimization algorithms are developed and embedded in the simulator. This study shows the potential use of RFID in biobanks and the value of simulation for estimating and optimizing its introduction in such complex socio-technical systems. [ABSTRACT FROM AUTHOR]