Short-Term scheduling of a polymer compounding plant

This paper addresses the optimal short-term scheduling of a three parallel production line plymer compounding plant, whose equipments require cleaning between product changeovers. A very effective user-friendly software tool was developed, which consists of a general scheduling model coupled with capabilities of Microsoft Excel for data handling and analysis. The scheduling model is based on a Resource Task Network discrete time formulation and leads to Mixed Integer Linear Programming problems. As outputs the user can access the optimal schedules for a number of different objectives

A goal programming approach for the retrofit of supply chain networks

In order to achieve sustainability, the design and planning of a supply chain has to fulfil economic, social and environmental objectives. Traditionally the design of supply chains has been based on economic objectives. As societal environment concerns grow, environmental aspects are also emerging, not only at the industry level, but also within the context of supply chain management. The investment towards logistics structures that consider both economic and environmental performance is nowadays an important research topic. However, much is still to be done. This paper, addresses the retrofit of supply chain networks where planning aspects are also considered. The supply chain network design and planning is modeled through a Resource-Task-Network (RTN) methodology. A mixed integer linear programming (MILP) multi-objective approach is developed, which attempts to simultaneously maximize the annual profit of the supply chain, taking into account the network retrofit, while environmental impacts are minimized. The environmental impacts are accounted for through the Eco-indicator 99 methodology. Profit and environmental impacts are balanced through the use of goal programming. The model applicability is illustrated through the solution of an example

Supply chain network optimization with environmental impacts

Traditionally the design of supply chains has been based on economic objectives. As societal environment concerns grow, environmental aspects are also emerging, at the industry level, as decisive factors within the context of supply chain management. The investment towards logistics structures that consider both economic and environmental performance is nowadays an important research topic. However, much is still to be done. This paper, addresses the planning and design of supply chain structures for annual profit maximization, while considering environmental aspects. The latter are accounted through the Eco-indicator methodology, which is used to quantify the damage to human health. Profit and environmental impacts are balanced through the use of an optimization approach adapted from symmetric fuzzy linear programming (SFLP), while the supply chain is modelled as a mixed integer linear programming (MILP) optimization problem using the Resource-Task- Network (RTN) methodology. The obtained model is validated through the solution of an example, where its applicability to supply chain problems is demonstrated

A Multi-objective optimization for the design and periodic scheduling of multipurpose facilities under uncertainty

Like most real-world problems, the design of multipurpose batch facilities involves multiple objectives. However the existing literature on the subject has been mainly centred on mono-criterion objectives (Barbosa-Povoa, 2007). Therefore, multi-objective optimisation is a modelling approach that requires further study when applied to such facilities. The best way to deal with various goals simultaneously is to define the efficient frontier which offers the optimal solutions found by multi-objective optimization. In this work, the inspection of the efficient frontier allows the decision maker to select the most satisfactory plant topology with the respective equipment design and storage policies that minimizes the total cost of the system, while maximizing production, subject to operational restrictions. The approach to the detailed design of multipurpose batch facilities with periodic mode of operation, as proposed by Pinto et al. (2005), is now extended to address the problem of uncertainty associated with demand and the incorporation of economic aspects. The uncertainty is treated through a two-stage stochastic model, leading to a MILP formulation. A scenario is set up where the demand is represented by a discrete probability function and a cyclic operation is considered. The e- constraint method is employed to handle the multi-objective optimization. An example, where different situations are evaluated is solved and a topology analysis is made

Design and planning of green supply chains: a fuzzy approach

Green SC can be seen as logistic structures that guarantee production and global distribution of products in an environmental manner. To achieve this goal companies must invest on the optimal design and planning of their logistic structures, while accounting for the trade-off between profits and environmental impacts. This is addressed using a generic and uniform mathematical framework, the RTN. For this bi-level optimization a SFLP approach is applied, where those objectives are treated as constraints and replaced by a new one, the aspiration level, which embodies a compromise between them

Scheduling of job shop, make-to-order industries with recirculation and assembly: discrete versus continuous time models

This work studies the performance of two Mixed Integer Linear Programming (MILP) models to solve scheduling problems in a flexible job shop environment with recirculation and assembly using a due-date-based objective function. The models convey different approaches both in the modelling of time (discrete and continuous approaches) as well as in the assignment of jobs to machines. The comparison is carried out for a job shop system considered closer to the industrial reality than the classical job shop problem of a single machine per operation that has been extensively studied in the literature, with the mould making industry providing the motivatin

LP-based heuristic procedure for the optimal design of water using networks with multi-contaminants

This paper proposes a new strategy for the optimal design of water-using networks in industrial systems featuring possibly more than a single water source and multiple contaminants. The model formulation is supported on a superstructure that exploits reuse opportunities and gives rise to a non-convex nonlinear which often leads to local optimal solutions. To overcome this, the new approach generates multiple initialization points, one for each possible sequence of operations, where a particular starting point is obtained by the sequential solution of a small set of related linear programs. The best solution of the several non-linear problems that are solved is then assumed to be the global optimal solution. The results obtained for a set of case studies have shown that the best initialization point is often the global optimal solution and that the procedure as a whole is efficient in escaping local optima

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems