Coping with production time variability via dynamic lead-time quotation

In this paper, we propose two dynamic lead-time quotation policies in an M/GI/1 type make-to-stock queueing system serving lead-time sensitive customers with a single type of product. Incorporating non-exponential service times in an exact method for make-to-stock queues is usually deemed difficult. Our analysis of the proposed policies is exact and requires the numerical inversion of the Laplace transform of the sojourn time of an order to be placed. The first policy assures that the long-run probability of delivering the product within the quoted lead-time is the same for all backlogged customers. The second policy is a refinement of the first which improves the profitability if customers are oversensitive to even short delays in delivery. Numerical results show that both policies perform close to the optimal policy that was characterized only for exponential service times. The new insight gained is that the worsening impact of the production time variability, which is felt significantly in systems accepting all customers by quoting zero lead times, decreases when dynamic lead-time quotation policies are employed

On multi-class multi-server queueing and spare parts management

Multi-class multi-server queuing problems are a generalization of the wellknown M/M/k situation to arrival processes with clients of N types that require exponentially distributed service with different averaged service time. Problems of this sort arise naturally in various applications, such as spare parts management, for example. In this paper we give a procedure to construct exact solutions of the stationary state equations. Essential in this procedure is the reduction of the problem for n = the number of clients in the system > k to a backwards second order difference equation with constant coefficients for a vector in a linear space with dimension depending on Nand k, denoted by d(N,k). Precisely d(N,k) of its solutions have exponential decay for n 00. Next, using this as input, the equations for n ::; k can be solved by backwards recursion. It follows that the exact solution does not have a simple product structure as one might expect intuitively. Further, using the exact solution several interesting performance measures related to spare parts management can be computed and compared with heuristic approximations. This is illustrated with numerical results

Control of multiclass queueing systems with abandonments and adversarial customers

This thesis considers the defensive surveillance of multiple public areas which are the open, exposed targets of adversarial attacks. We address the operational problem of identifying a real time decision-making rule for a security team in order to minimise the damage an adversary can inflict within the public areas. We model the surveillance scenario as a multiclass queueing system with customer abandonments, wherein the operational problem translates into developing service policies for a server in order to minimise the expected damage an adversarial customer can inflict on the system. We consider three different surveillance scenarios which may occur in realworld security operations. In each scenario it is only possible to calculate optimal policies in small systems or in special cases, hence we focus on developing heuristic policies which can be computed and demonstrate their effectiveness in numerical experiments. In the random adversary scenario, the adversary attacks the system according to a probability distribution known to the server. This problem is a special case of a more general stochastic scheduling problem. We develop new results which complement the existing literature based on priority policies and an effective approximate policy improvement algorithm. We also consider the scenario of a strategic adversary who chooses where to attack. We model the interaction of the server and adversary as a two-person zero-sum game. We develop an effective heuristic based on an iterative algorithm which populates a small set of service policies to be randomised over. Finally, we consider the scenario of a strategic adversary who chooses both where and when to attack and formulate it as a robust optimisation problem. In this case, we demonstrate the optimality of the last-come first-served policy in single queue systems. In systems with multiple queues, we develop effective heuristic policies based on the last-come first-served policy which incorporates randomisation both within service policies and across service policies

Controlling the order pool in make-to-order production systems

Voor ‘Make-To-Order’ (MTO, oftewel klantordergestuurde) productiesystemen is de tijd die orders moeten wachten op beschikbare productiecapaciteit cruciaal. Het beheersen van die wachttijd is van groot belang om zowel korte als betrouwbare doorlooptijden te realiseren. Daarom analyseerde en ontwierp Remco Germs regels voor orderacceptatie en ordervrijgave, om daarmee de wachttijden te beheersen. Orderacceptatie en -vrijgave zijn de twee belangrijkste mechanismen om de lengte van wachttijden te beïnvloeden en zodoende de productie te sturen. De logistieke prestatie hangt in grote mate af van specifieke kenmerken van MTO-systemen, zoals routing variabiliteit, beperkte productiecapaciteit, omsteltijden, strikte leveringsvoorwaarden en onzekerheid in het aankomstpatroon van orders. Om een beter begrip te krijgen van de afwegingen die MTO-bedrijven in dit opzicht moeten maken richt het proefschrift zich op de modellering van de belangrijkste kenmerken van MTO-systemen. De inzichten die dat oplevert worden vervolgens gebruikt om orderacceptatie- en ordervrijgaveregels te ontwikkelen die eenvoudig te begrijpen en daarom makkelijk in praktijksituaties te implementeren zijn. Deze relatief eenvoudige beslissingsregels kunnen al leiden tot significante verbeteringen in de logistieke prestaties van MTO-bedrijven. The thesis of Remco Germs analyses and develops order acceptance and order release policies to control queues in make-to-order (MTO) production systems. Controlling the time orders spend waiting in queues is crucial for realizing short and reliable delivery times, two performance measures which are of strategic importance for many MTO com-panies. Order acceptance and order release are the two most important production con-trol mechanisms to influence the length of these queues. Their performance depends on typical characteristics of MTO systems, such as random (batch) order arrival, routing variability, fixed capacities, setup times and (strict) due-dates. To better understand the underlying mechanisms of good order acceptance and order release policies the models in this thesis focus on the main characteristics of MTO systems. The insights obtained from these models are then used to develop order acceptance and order release policies that are easy to understand and thereby easy to implement in practice. The results show that these relatively simple policies may already lead to significant performance improvements for MTO companies.

MODELING AND SIMULATION OF A SEMICONDUCTOR MANUFACTURING FAB FOR CYCLE TIME ANALYSIS

The goal of the thesis is to conduct a study of the effects of scheduling policies and machine failures on the manufacturing cycle time of the Integrated Circuit (IC) manufacturing process for two processor chips, namely Skylake and Kabylake, manufactured by Intel. The fab simulation model was developed as First in First Out (FIFO), Shortest Processing Time (SPT), Priority based (PB), and Failure FIFO (machine failures) model, and the average cycle times and queue waiting times under the four scheduling policy models were compared for both the Skylake and Kabylake wafers. The study revealed that scheduling policies SPT and PB increased the average cycle time for Skylake wafers while decreasing the average cycle time for the Kabylake wafers, when compared to the base FIFO model. Machine failures increased the average cycle time for both types of wafers

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Stability Problems for Stochastic Models: Theory and Applications II

Most papers published in this Special Issue of Mathematics are written by the participants of the XXXVI International Seminar on Stability Problems for Stochastic Models, 21­25 June, 2021, Petrozavodsk, Russia. The scope of the seminar embraces the following topics: Limit theorems and stability problems; Asymptotic theory of stochastic processes; Stable distributions and processes; Asymptotic statistics; Discrete probability models; Characterization of probability distributions; Insurance and financial mathematics; Applied statistics; Queueing theory; and other fields. This Special Issue contains 12 papers by specialists who represent 6 countries: Belarus, France, Hungary, India, Italy, and Russia