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The purpose of this study is to propose and model an integrated production‐maintenance strategy for a failure‐prone machine in a just‐in‐time context.

The proposed integrated policy is defined and a simulation model is developed to investigate it.

The paper focuses on finding simultaneously two decision variables: the period (T) at which preventive maintenance actions have to be performed; and the sequence of jobs (S). These values minimize the maintenance costs (M_C) and the expected total earliness and tardiness costs (ET_C) away from a common due‐date D.

The paper attempts to integrate in a single model the two main aspects of any manufacturing and production systems: production and maintenance. It focuses on a stochastic scheduling problem in which n immediately available jobs are to be scheduled jointly with the preventive maintenance. The effect of the period (T) and the sequence of job (S) on the expected total cost are shown through a numerical example.

The paper proposes an integrated model that links production, preventive maintenance and corrective maintenance. It is simultaneously focusing on the period (T) at which preventive maintenance actions have to be performed and the sequence of jobs (S) to reduce production and maintenance‐related costs.

The purpose of this paper is to study the potential for improving sustainability performance in food supply networks by implementing collaborative distribution. Food supply chains generate a significant portion of CO₂ emissions, because of the large volume of food transportation and the large number and frequency of trips made to deliver food products to retail stores, making it available to the customers. Collaboration of partners in food supply chains will lead to reducing CO₂ emissions.

Such collaboration could be in the form of sharing trucks by retailers, to increase the fill rate of the vehicles and to reduce their empty running. A case study of the logistics network of a British company, distributing fruits and vegetables, is carried out. The company sends the products from a distribution centre to 27 retailers’ warehouses of 3 different companies in the UK. A simulation study is carried out to measure the reduced traveled distance for delivery of the products and the reduced amount of CO₂ emissions across two different scenarios, as a result of implementing collaborative distribution.

With this approach, the total CO₂ emissions are able to be reduced by at least 26 per cent.

The theoretical contribution of the paper is important both for showing the role of simulation and collaborative distribution for developing the green supply chain solutions management and their indicating to the applications to logistics and product delivery.

The purpose of this study is to propose and formulate an integrated hierarchical production, and maintenance‐planning model.

The proposed model is formulated mathematically and tested for some hypothetical cases. A two‐level planning is proposed to address the hierarchical planning problem, i.e. aggregate planning and detailed planning. A preventive maintenance planning is integrated into the aggregate planning, while machine breakdowns, which require corrective maintenance actions, are investigated in the detailed planning. The proposed general preventive maintenance model is tested against cyclical preventive maintenance models for some cases, and for evaluating the performance of the models, in terms of costs, and service levels.

The proposed general preventive maintenance model gives a better solution in terms of cost than the cyclical maintenance model (i.e. 6 per cent less costly), if the maintenance planning is executed separately from the production planning. In terms of service level, however, both models perform equally well with average service levels equal to 97.6 per cent. The effect of tight capacity, long maintenance duration, and small machine parameters similarly tightens the capacity. In these cases, it is shown that a stable level of capacity is more beneficial to achieve a better service level, which is gained if the preventive maintenance actions are carried out monthly.

At the aggregate level, the proposed preventive maintenance model considers a non‐cyclical planning, which means that the preventive maintenance periods do not necessarily fall at equally distant times. The inventory movement constraints at the aggregate level decouple machines to operate independently; hence the detailed level problem can be solved separately for each machine. In a rolling horizon approach, only the first period of the aggregate plan is implemented and disaggregated into the production of items at the detailed level.

The paper proposes an integrated model of hierarchical production and maintenance planning. A general preventive maintenance is integrated into the aggregate planning, while machine breakdowns are investigated in the detailed planning. To the best of one's knowledge, such a hierarchical view of production planning and maintenance has not been addressed adequately.

The purpose of this study is to gain some insights into the number of shortages resulting from two alternative demand allocation schemes between a contractor (machine M₁) and subcontractor (machine M₂), on the one hand, and from inventory accumulation, on the other hand. The shortages stem from random machine breakdowns, and each machine undergoes preventive maintenance. The motivation behind inventory accumulation is to allow demand to be fulfilled even when both machines are down.

The number of shortages stemming from all scenarios under consideration was established via computer simulation with the Arena^© language.

For demand allocation that remains unchanged for the duration of the planning horizon and constant reliability of M₁, it was found that, the less reliable M₂ is, the more biased in favour of M₁ will be the optimal demand allocation and the greater will be the number of shortages. Moreover, both dynamic demand reallocation over the planning horizon and inventory accumulation result in a substantial reduction in shortages.

The results are representative of the specific data, which were assumed in the simulation models. Nevertheless, this methodology is recommended for this type of analysis, as it is highly flexible and can take into account many practical considerations, which an analytical approach cannot.

Within the context of unreliable production machines, the most important practical implication of this study is that the dynamic reallocation of demand between a contractor and subcontractor, along with inventory accumulation, both have the potential to yield important reductions in the number of shortages.

The subject‐matter of this paper was not previously reported in the literature. Furthermore, the insights gained as a result of this study can yield substantial benefits to companies in terms of improving their service levels as measured by reduced shortages.

The purpose of this paper is to propose a sustainable dashboard for evaluating the sustainable performance of urban delivery systems from the perspective of operational logistics managers, one of the categories of stakeholders given less consideration by public authorities in their quest for consensus.

First, a synthesis of the main works on the subject is proposed to provide a common grid of economic, environmental and social/societal indicators for sustainable supply chain management (SSCM), after which the method for defining the dashboard is presented. This method is derived from a collaborative decision-support approach and applied to a panel of operational logistics managers. Using a co-constructive method, a group of experts is consulted first separately, then by small groups and then a group restitution and consensus search process is made to find an agreed-upon set of indicators.

The results show a difference between the indicators chosen in the individual phase and those defined in small groups. They also show a gap between classical expert-obtained indicators (mainly made by one or a small group of non-operational experts) and the proposed dashboard, made by and for operational managers.

The originality of the paper is that it addresses two issues (urban logistics evaluation and consensus search) by using methods of natural and active pedagogy and shows by an experimental method the interests and opportunities of collaboration in defining sets of indicators for urban logistics evaluation.

This purpose of this paper is concerned with the issue of designing and operating a sustainable waste management system, in rapidly growing urban areas. Some existing models for waste management and the extent to which they could be applied in a specific environment – Cairo for example – are discussed. An integrated framework which can serve as a basis for the development of a sustainable waste management system is proposed. Sustainability of the system, opportunities and challenges, material re-use and recycling, composting, incineration and energy production and land-filling are examined. Relevant waste management and cost sub-models inherent to the system are discussed. In addition, some other environmental and social aspects are also debated. The main focus of this paper is to provide some insights into the design of a more effective and integrated sustainable waste management system applicable for developing economies.

An in-depth analysis is carried out for the existing models and approaches for waste management. This has led to the development of a framework which can be implemented.

A comprehensive conceptual framework for a waste management system is developed. The major inputs and expected outputs are clearly identified.

A comprehensive and complete system for waste management is proposed. This system can be operationalized and implemented with a view of sustainability.

This paper seeks to address the production control problem of a failure‐prone manufacturing system producing a random fraction of defective items.

A fluid model with perfectly mixed good and defective parts has been proposed. This approach combines the descriptive capacities of continuous/discrete event simulation models with analytical models, experimental design, and regression analysis. The main objective of the paper is to extend the Bielecki and Kumar theory, appearing under the title “Optimality of zero‐inventory policies for unreliable manufacturing systems”, under which the machine considered produced only good quality items, to the case where the items produced are systematically a mixture of good as well as defective items.

The paper first shows that for constant demand rates and exponential failure and repair time distributions of the machine, the Bielecki‐Kumar theory, adequately revisited, provides new and coherent results. For the more complex situation where the machine exhibits non‐exponential failure and repair time distributions, a simulation‐based approach is then considered. The usefulness of the proposed models is illustrated through numerical examples and sensitivity analysis.

Although the decisions taken in response to demands for productivity have a direct impact on product quality, management quality and production management have been traditionally treated as independent research fields. In response to this need, this paper is considered as a preliminary work in the intersection between quality control and production control issues.

This paper seeks to develop an optimal stochastic control model where interactive feedback consists of the quantity of flawless and defective products. The main objective of this study is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs.

The model differs from similar research projects in that, instead of age‐dependent machine failure, it considers only defective products as feedback into the optimal model for maintenance and production planning. In this paper a near optimal control policy of the system through numerical techniques is obtained.

In this paper, a new model in which the system's retroaction is the quantity of defective products is presented, considering that defective products are a consequence of global manufacturing system deterioration. Instead of taking into account machine failure and human error separately, it considers a defect in product as being the consequence of a combined failure; this consideration allows one to be more realistic by merging all failure parameters into a single one. A new stochastic control model, which focuses on defective products, inventory, and backlog, has been developed.

This approach extended the concept of hedging point policy to the quantity of defective products combined with preventive and corrective maintenance strategies. The control policy obtained has a bang bang structure and is completely known for given parameters.

The integration of maintenance and production strategies has been mainly focused on the machine. Many research projects have been focusing on the age when dealing with machine failure. It is considered as the main target of the cost reduction in maintenance engineering departments. The originality of this paper is the taking into account of all operational failures into the same optimization model. It brings a value added to high level of maintenance and for operation managers who need to consider all failure parameters before taking decisions related to cost.