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The paper proposes a methodology for the cost assessment of production lines with unreliable machines and finite buffers. This methodology is based on the new concept of "Line Equipment Cost" (LEC), that is the actual operating cost of a certain machine which is used into a certain line configuration. For each machine, this kind of cost not only depends on a set of static parameters describing the statistical and structural properties of the machine, but also on its actual performance which, in turn, strictly depends on the specific line configuration where the machine is installed. If the line configuration is modified (e.g., more/less buffer space is available) the machine's performance is expected to improve/degrade so that its LEC value, which dynamically expresses the machine's actual operating cost, must be properly adjusted. Hence, in order to evaluate any production line, first the specific LEC values of the single machines in that line should be computed, then the "Total Line Cost" (TLC) can be determined by summing up those LEC values. Finally, the paper provides some numerical results in order to show the applicability of the proposed methodology which can be used not only to evaluate the actual operating cost of a specific production line (expressed by the TLC value), but also to compare different line configurations in order to drive strategic decision.

The paper proposes a structured decision-making approach for supporting ERP upgrade/replacement projects when new business requirements arise. The proposed framework is composed of three possible steps involving different levels of interventions with the aim of selecting the best ERP upgrade/replacement solution. In each step the Analytic Hierarchy Process (AHP) method is applied as an evaluation tool. Specifically, an illustrative application for increasing supply network coordination is described by considering "functionality factors", "vendor factors" and "software system factors".

This paper deals with the part supply process in a mixed-model assembly line served by a logistic train. The logistic train periodically follows a fixed route (tour) and, in each tour, it visits all the stations for providing full bins of parts and retrieving the empty bins (which will be re-filled in a "supermarket" at the end of the tour). The aim of this paper is to analytically model such a system in order to support the dimensioning of both the logistic train and the station buffers. The innovative contribution consists in treating the system as a "polling system", i.e., a set of queues attended by a single server. This allows us to consider the variability of the station cycle times and the tour duration of the logistic train. In particular, since the capacity of the logistic train is constrained by the number of wagons, the study generalizes approaches for the uncapacitated-server polling systems to the capacitated case.

Oggi molte aziende si trovano di fronte al problema cruciale di decidere simultaneamente dove ubicare nuovi impianti produttivi e distributivi e come servire i propri clienti. Il presente studio propone una famiglia di modelli di programmazione lineare di tipo cost-based capaci di supportare l’ottimizzazione di una rete logistica operante nel mercato mondiale. I modelli proposti sono stati applicati offrendo notevoli risparmi di costo nella logistica in Arcotronics S.p.A, azienda bolognese leader nella produzione di componenti elettronici e di macchine automatiche.

The paper deals with the design of rack storage systems in multiple product situations. Given the production/delivery patterns of the different products and shared storage policies, the aim is to identify the amount of storage area that should be devoted to single deep selective racks and the amount for non-selective racks. The non-selective rack storage systems under analysis are accessed in a LIFO (last-in first-out) manner (e.g., "drive-in" racks). In particular, the racks under analysis consist of lane levels of different heights. This makes the problem challenging when also the unit loads may have different heights (e.g., because of product load restrictions). In such a situation, small unit loads can be put in high lanes, not vice versa. Moreover, the volumetric utilization and the storage efficiency of the warehouse become key performance indicators, so that the optimal mix of racks of different heights should be investigated. Thus, the paper presents a mathematical programming model able to address the considerations outlined above, along with floor space constraints. The objective is identify the number of single deep selective racks, the mix and number of non-selective racks and the lane depths so that the volumetric storage efficiency is maximized.

Supply chain risk management is considered a topic of increasing interest worldwide and its focus has evolved over time. The recent coronavirus pandemic (known as COVID-19) has forced business to handle a new global crisis and rapidly adapt to unexpected challenges. In an attempt to help companies counteract the pandemic risk, as well as to fuel the scientific discussion about this topic, this paper proposes a systematic literature review on risk management and disruptions in the supply chain focusing on quantitative models and paying a particular attention to highlighting the potentials of the studies reviewed for being applied to counteract pandemic emergencies. An appropriate query was made on Scopus and returned, after a manual screening, a useful set of 99 papers that proposed models for supply chain risk management. The relevant aspects of pandemics risk management have been first identified and mapped; then, the studies reviewed have been analysed with the aim of evaluating their suitability of being applied to sanitary crises. In carrying out this review of the literature, the study moves from previous, more general, reviews about risk management and updates them, starting from the lines of research that have been covered in recent years and evaluating their consistency with future research directions emerging also as a consequence of the pandemic crisis. Gaps and limitations of the existing models are identified and future research directions for pandemics risk management are suggested.

Assembly line rebalancing is a problem companies are frequently confronted with as continuous changes in product features and volume demand caused by the volatility of modern markets result in re-definition of assembly tasks and line cycle time fluctuations. Consequently, managers are forced to adjust the balancing of their lines in order to adapt to the new conditions while trying to minimise both increases in completion costs and costs related to changes in task assignment. In particular, when modifications are made to line balancing, costs are incurred for operator training, equipment switching and moving, and quality assurance. The stochastic assembly line rebalancing problem is essentially composed of a multi-objective problem in which two joint objectives, total expected completion cost of the new line and similarity between the new and the existing line, must be optimised. Consequently, this paper presents a multiple single-pass heuristic algorithm developed for the purpose of finding the most complete set of dominant solutions representing the Pareto front of the problem. The operative parameters of the heuristic are set as a result of a great deal of experimentation. Moreover, a multi-objective genetic algorithm is developed and then compared with the proposed heuristic in order to demonstrate its effectiveness. Finally, an illustrative case study is presented.

Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors.

The design and management of a multi-stage production–distribution system is one of the most critical problems in logistics and in facility management. Companies need to be able to evaluate and design different configurations for their logistic networks as quickly as possible. This means coordinating the entire supply chain effectively in order to minimize costs and simultaneously optimize facilities location, the allocation of customer demand to production/distribution centers, the inbound and outbound transportation activities, the product flows between production and/or warehousing facilities, the reverse logistics activities, etc. Full optimization of supply chain is achieved by integrating strategic, tactical, and operational decision-making in terms of the design, management, and control of activities. The cost-based and mixed-integer programming model presented in this study has been developed to support management in making the following decisions: the number of facilities (e.g. warehousing systems, distribution centers), the choice of their locations and the assignment of customer demand to them, and also incorporate tactical decisions regarding inventory control, production rates, and service-level determination in a stochastic environment. This paper presents an original model for the dynamic location–allocation problem with control of customer service level and safety stock optimization. An experimental analysis identifies the most critical factors affecting the logistics cost, and to finish, an industrial application is illustrated demonstrating the effectiveness of the proposed optimization approach.

International audience; The design of logistic distribution systems is one of the most critical and strategic issues in industrial facility management. The aim of this study is to develop and apply innovative mixed integer programming optimization models to design and manage dynamic (i.e. multi-period) multi-stage and multi-commodity location-allocation problems (LAP). LAP belong to the NP-hard complexity class of decision problems, and the generic occurrence requires simultaneous determination of the number of logistic facilities (e.g. production plants, warehousing systems, distribution centers), their locations, and assignment of customer demand to them. The proposed models use a mixed integer linear programming solver to find solutions in complex industrial applications even when several entities are involved (production plants, distribution centers, customers, etc.). Lastly, an application of the proposed models to a significant case study is presented and discussed.

In the modern competitive business environment the design and management of supply chains is one of the most important and critical problems facing managers of multinational companies operating worldwide. There are three levels of decisions in a supply chain: strategic, tactical, and operational levels. The literature generally treats these decisions separately. This study defines a conceptual framework for the development of new modeling approaches to the Production Distribution Logistic System Design (PDSD) problem. In particular, the authors introduce the initial basis for the development of an innovative decision support systems platform capable of integrating the design, management, control, and optimization activities for a supply chain system.

The design and management of a multi-stage production/distribution system is one of the most critical problem in logistics and in facility management. This manuscript deals with the so called facility location-allocation problem, i.e. with the simultaneous decisions regarding the design, management, and control of a distribution network. In particular the logistic problem object of this study deals with the determination of the number of facilities (e.g. production plants, warehousing systems, distribution centers, etc.), the choice of their locations and the assignment of customers demand to them, incorporating also tactical decisions regarding inventory control, production rates and service level determination. The purpose is to design, test, and compare innovative cost-based models and solutions for the dynamic (i.e. multi-period) location allocation problem (LAP) with safety stock levels determination and customer service level optimization. An experimental analysis conducted on an industrial application is presented and discussed.