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In this paper, we consider a single retailer who sells multiple products subject to decay, and that implements coordinated inventory replenishments among them. The overall available surface at the retailer is limited and is partitioned into two areas: the backroom facility and the display area. The demand rate of each product is a function of displayed quantity and location, and it also depends on the cross-elasticity among items. The objective is to find the product assortment, the replenishment policy of each product, the quantity to be displayed, and the surfaces assigned to the backroom and display areas that maximize the total profit per time unit. We develop the mathematical model and formulate the optimization problem. Finally, we investigate the model response by means of numerical experiments considering several problem instances. Copyright © 2022 IFAC.

To stay competitive, modern market scenarios are forcing a radical shift in the manufacturing concept, focusing companies’ attention on customer satisfaction through increased product customization and quick response strategies. Significant progress has been made in the field of Industry 4.0 technologies, but there is still an open gap in the literature regarding methodologies for efficiently managing a manufacturing system’s available productive resources. Spearman et al. proposed the CONtrolled Work-In-Progress (CONWIP) production logic, which allows controlling Work-In-Progress (WIP) in a production system while monitoring throughput. However, in order to face with the increased variability that enters into the production system, an affordable performances estimation tool is still required. Taking advantages of the recent innovation in the field of machine learning, this paper contributes to the development of a tool for estimating the performance of a production line using a deep learning neural network. The results demonstrated that the proposed estimation tool outperforms the current best-known mathematical model when estimating the throughput of a CONWIP Flow-Shop production line with a given processing time distribution and WIP value.

Successful flow-shop scheduling outlines a more rapid and efficient process of order fulfilment in warehouse activities. Indeed the way and the speed of order processing and, in particular, the operations concerning materials handling between the upper stocking area and a lower forward picking one must be optimized. The two activities, drops and pickings, have considerable impact on important performance parameters for Supply Chain wholesaler companies. In this paper, a new flow shop scheduling algorithm is formulated in order to process a greater number of orders by replacing the FIFO logic for the drops activities of a wholesaler company on a daily basis. The System Dynamics modelling and simulation have been used to simulate the actual scenario and the output solutions. Finally, a t-Student test validates the modelled algorithm, granting that it can be used for all wholesalers based on drop and picking activities.

This paper presents a simulation study to evaluate the current performance of a waste of electrical and electronic equipment (WEEE) management system in the north of Italy. The model takes into account several actors of the real WEEE supply network and allows to analyze different alternative scenarios for the functioning of the same network. The behavior of a real treatment plant has been simulated considering the incoming flow of waste material and all the outflows of recycled goods. The analysis is focused on two specific WEEE categories treated in the plant, which have been chosen as the most dangerous categories for the human health and the environment. Some key performance indicators (KPIs) have been defined and used to assess the performance of the current WEEE network and compare it with some alternative configurations. As it grounds on a real scenario, the model is expected to provide interesting proposals for future actions about the WEEE management in Italy.

An operator’s mistakes are frequently attributed to inexperience, which leads to a superficial management of these kinds of ‘accidental’ errors. The weakness of this traditional approach is that it isolates the wrong actions from the general systematic context, overlooking potential learning from error analysis. Modern approaches push organisations to prevent the occurrence of errors and, if necessary, to start recovery operations. The methodology proposed in this paper is aimed precisely at this second type of approach. There is a growing need for systems that show errors or deviations from the desired state of the system, leading operators towards the proper execution of tasks and reducing ‘conformance risk’. This paper presents a methodology and a simulation model of ‘Conformance Risk Aware Design’ in order to support decision-makers in modelling business processes.

This paper presents a Decision Support System (DSS) for Production Planning and Control (PPC) of a Job Shop System, operating in a Make To Order environment. Various Order Review and Release (ORR) techniques have been considered in order to tackle the problems typical of this context: high level of Work in Process, significant and not controllable throughput times and difficulties in meeting due dates. A System Dynamics (SD) approach allows to analyze the impact of the control mechanisms implemented on several system performance indicators (e.g. shop oriented and job oriented measures).

Healthcare is growing sectors for optimization techniques since most of the Europeans National Health Systems are facing the goal of reduce their financial efforts. This paper presents an innovative approach where Systems Dynamics and State Transition Diagrams are integrated building a flexible and general model able to reproduce complex healthcare operations in a tradeoff between complexity and flexibility. The papers shows a case study applied to real life hospital.

This paper proposes a new method for controlling a flow shop in terms of throughput and Work In Process (WIP). In order to achieve a throughput target, a Deep Q-Network (DQN) is used to define the constant WIP quantity in the system. The main contribution of this paper is the novel approach used to formulate the state, action space, and reward function. An extensive pre-experimental campaign is conducted to determine the best network structure and appropriate hyperparameter values. Finally, the system’s performance is compared to the known results of an analytical model from the literature (Practical Worst Case, PWC).

In this paper, we study a single vendor-multiple buyer integrated inventory model with controllable lead time and backorders-lost sales mixture. Each buyer adopts a periodic review policy in which the review period is an integer fraction of the production cycle time of the vendor. To reflect the practical circumstance characterized by the lack of complete information about the demand distribution, we assume that only the first two moments of the demand during the protection interval are known. The long-run expected total cost per time unit is derived, which includes stockout costs. The problem is to determine the length of the inventory cycle of the vendor, the produce-up-to level for the vendor, the replenishment policy of each buyer, and the length of lead times that minimize the cost function under the minimax distribution-free approach. Two alternative heuristics are proposed. Numerical experiments have been carried out to investigate the performance of the heuristics and to study the sensitivity of the model.

Machine Learning (ML) techniques and algorithms, which are emerging technologies in Industry 4.0, present new possibilities for complex scheduling methods. Since different rules can be applied to different circumstances, it can be difficult for the decision-maker to choose the right rule at any given time. The purpose of the paper is to build an “intelligent” tool that adapts its choices in response to changes in the state of the production line. A Deep Q-Network (DQN), a typical Deep Reinforcement Learning (DRL) method, is proposed for creating a self-optimizing scheduling policy. The system has a set of known dispatching rules for each machine’s queue, from which the best one is dynamically chosen, according to the system state. The novelty of the paper is how the reward function, state, and action space are modelled. A series of experiments were conducted to determine the best DQN network size and the most influential hyperparameters for training.

The increasing globalisation process has led to a radical change in the production concept, moving from a mass production paradigm towards one of mass customisation (MC), and focusing on value creation by pursuing customers’ needs and increasing responsiveness. The rapid development of information technologies has also made it possible to create new manufacturing paradigms, such as Industry 4.0 and cloud manufacturing, in which the increased level of autonomy is one of the key concepts for tackling new market challenges. This paper proposes a decentralised scheduling approach that improves the performance of production systems while minimising the usually high work-in-progress (WIP) requirements of the classic centralised scheduling and inventory production control system. Using a semi-heterarchical Manufacturing Planning and Control (MPC) architecture and integrating the Industry 4.0 innovation in a cloud manufacturing environment, this work contributes to the design of the lower level of the MPC architecture. The resulting production controller can allocate jobs following different dispatching rules dynamically. The performances of the proposed approach were assessed for different production scenarios and control parameter settings through an exhaustive experimental campaign based on hybrid simulation tools. The results showed that the proposed low-level controller led to a productivity increase while delivering increased responsiveness.

The paper presents a novel architecture for production planning and control characterized by a semi-heterarchical structure in which different management levels are identified by both their physical identity and functional scope. The architecture is developed in three management levels: the Knowledge-based Enterprise Resource Planning (the business level, also accountable for cloud interaction); the High-Level Controller (the general performance level); the Low-Level Controller (the operative level). The proposed architecture represents an enhanced implementation of the Industry 4.0 decentralized decision concept, enabling a better knowledge and control of system performances to deliver increased profit and response time predictability in highly customised scenarios.

The work deals with the evolution of Manufacturing Planning and Control (MPC) system, from the approaches commonly adopted in the industry so far, to the new implementation schemes involved by Industry 4.0 and Cloud Manufacturing paradigms. Firstly, an introductory part presenting the different classical approaches and their scope of application is reported, together with some insights about the positioning of Industry 4.0 in future market scenarios. Then, a second section focuses the attention on the new technologies at the base of the Industry 4.0 concept, and how they can act as enablers for bridging the gap between current and future production control approaches. Finally, the last section explores the state of the art of Industry 4.0 and Cloud Manufacturing architectural implementations, also outlining further development possibilities and strategies.

In spite of an increasing development of applied mathematics to the theory of location, just a few applications have been done about support to decision regarding the location of production and/or selling sites. This paper aims at localization of a firm, that will produce controlled leavening baking products, and its kiosks (selling points), displaced on a municipality area. To make the choice, it’s been considered the double function of the kiosk: it is a restocking point and it will be necessary to study the optimal path from the production point, that is considered known, to the kiosk itself; it is also a selling point, so it has to be located where it’s mostly perceived by the customers. The two choice dimensions, regarding an unique location, need the creation of a model able to represent the scenarios dynamism of reality. As the location of production points has been supposed known, it’s been studied the problem of kiosks location, by the optimization of attracting capacity and by minimizing the restocking cost from the production point. So, it’ been simulated a quantitative model to represent the choice behaviour of different categories customers to move in the urban area. The simulation model output gives the traffic flows on the network, where the production system (manufacturing and selling points) has been located. These results give also the inputs for a second model used to define the optimal paths on the same network. In this way, the choice model for the best path from production to selling point is able to consider the network loads. The interaction between the two models will also be useful to define the localization of the whole production system, estimating the dimension of each production and selling point.

Vico Equense, Napoli