Lazaros G. Papageorgiou

Supply chain optimisation for the process industries: Advances and opportunities

Supply chain management and optimisation is a critical aspect of modern enterprises and a flourishing research area. This paper presents a critical review of methodologies for enhancing the decision-making for process industry supply chains towards the development of optimal infrastructures (assets and network) and planning. The presence of uncertainty within supply chains is discussed as an important issue for efficient capacity utilisation and robust infrastructure decisions. The incorporation of business/financial and sustainability aspects is also considered and future challenges are identified.

A combined optimization and agent-based approach to supply chain modelling and performance assessment

The main objective of this paper is to give an example of how expert systems techniques for distributed decision-making can be combined with contemporary numerical optimization techniques for the purposes of supply chain optimization and to describe the resulting software implementation. In this paper, multi-agent modelling techniques are applied to simulate and control a simple demand-driven supply chain network system, with the manufacturing component being optimized through mathematical programming. The system measures supply chain performance and the effect of different parameters in the replenishment control system, and can be used to simulate the behaviour of a system that uses optimization for part of its decision-making. The objective of this supply chain network system is to reduce operating cost, while maintaining a high level of customer order fulfilment.

A hierarchical solution approach for multi-site capacity planning under uncertainty in the pharmaceutical industry

This paper presents a systematic mathematical programming approach for long-term, multi-site capacity planning under uncertainty in the pharmaceutical industry. The proposed mathematical model constitutes an extension of the work of Papageorgiou et al. (2001) determining both the product portfolio and the multi-site capacity planning in the face of uncertain clinical trials outcomes while taking into account the trading structure of the company. The overall problem is formulated as a two-stage, multi-scenario, mixed-integer linear programming (MILP) model. A hierarchical algorithm is then proposed in order to reduce the computational effort needed for the solution of the resulting large-scale MILP problem. The applicability of the proposed solution approach is demonstrated by a number of illustrative examples

Fair transfer price and inventory holding policies in two-enterprise supply chains

Abstract A key issue in supply chain optimisation involving multiple enterprises is the determination of policies that optimise the performance of the supply chain as a whole while ensuring adequate rewards for each participant. In this paper, we present a mathematical programming formulation for fair, optimised profit distribution between echelons in a general multi-enterprise supply chain. The proposed formulation is based on an approach applying the Nash bargaining solution for finding optimal multi-partner profit levels subject to given minimum echelon profit requirements. The overall problem is first formulated as a mixed integer non-linear programming (MINLP) model. A spatial and binary variable branch-and-bound algorithm is then applied to the above problem based on exact and approximate linearisations of the bilinear terms involved in the model, while at each node of the search tree, a mixed integer linear programming (MILP) problem is solved. The solution comprises inter-firm transfer prices, production and inventory levels, flows of products between echelons, and sales profiles. The applicability of the proposed approach is demonstrated by a number of illustrative examples based on industrial processes.

An optimisation framework for a hybrid first/second generation bioethanol supply chain

Abstract Assessment of both economical and environmental performance of biofuel supply chains is crucial to have a complete view of the future implications of those systems. This paper presents a multi-objective, static modelling framework for the optimisation of hybrid first/second generation biofuel supply chains. Using the proposed modelling framework, different aspects are analysed including the potential GHG savings, the impact of carbon tax on the economic and environmental performance of a biofuel supply chain, the trade-off between the economic and environmental objectives and the maximum bioethanol throughput that can be achieved at different cap levels on the total supply chain cost. The trade-off between the conflicting objectives is analysed by solving the proposed multi-objective model using the ɛ-constraint method. In addition, the impact of technological learning on the economic and environmental performance of the supply chain throughout time is also analysed using a multi-period model developed based on the proposed static optimisation framework. Bioethanol production in the UK using hybrid first/second generation technologies is considered as the case study to highlight the model applicability.

Capacity Planning Under Uncertainty for the Pharmaceutical Industry

Over the last few years, the simultaneous optimisation of the product portfolio and manufacturing capacity has gained increased importance in the Pharmaceutical Industry. The problem of capacity planning under clinical trials uncertainty for the pharmaceutical industry has recently been addressed in the literature. However, there is a need for better solution approaches, as when the potential product portfolio increases, existing models become extremely large and very difficult to solve. Here, a scenario aggregation/disaggregation approach for this problem is presented. The results from the proposed flexible approach are compared with those obtained from a detailed stochastic, multistage, multiperiod model.

Optimal Multi - floor Process Plant Layout

Abstract This paper presents a general mathematical programming formulation for the multi-floor process plant layout problem, which considers a number of cost and management/engineering drivers within the same framework thus resolving various trade-offs at an optimal manner. The proposed model determines simultaneously the number of floors, land area, floor allocation of each equipment item and detailed layout for each floor. The overall problem is formulated as a mixed integer linear programming (MILP) model based on a continuous domain representation. The applicability of the model is demonstrated by three illustrative examples.

Optimal energy and cleaning management in heat exchanger networks under fouling

This paper addresses the problem of cyclic cleaning and energy scheduling in special classes of heat exchanger networks (HENs). A salient characteristic of this problem is that the performance of each heat exchanger decreases with time and can then be restored to its initial state by performing cleaning operations. Due to the cyclic nature of the schedule, some operations may span successive cycles (wrap-around) which is taken into account in the mathematical model. A tight mixed integer linear programming (MILP) model is presented which is solved to global optimality. A detailed objective function is used to account for cleaning cost and energy requirements. The formulations can model serial and parallel HENs, as well as network arrangements arising from the combination of these basic cases. The optimization algorithm determines simultaneously: (i) the number of cleaning operations required along with their corresponding timings and (ii) the optimal utility utilization profile over time. A complex heat exchanger network example is presented to illustrate the applicability of the proposed model.

A mixed integer optimisation approach for integrated water resources management

In areas lacking substantial freshwater resources, the utilisation of alternative water sources, such as desalinated seawater and reclaimed water, is a sustainable alternative option. This paper presents an optimisation approach for the integrated management of water resources, including desalinated seawater, wastewater and reclaimed water, for insular water deficient areas. The proposed mixed integer linear programming (MILP) model takes into account the subdivided regions on the island, the subsequent localised needs for water use (including water quality) and wastewater production, as well as geographical aspects. In addition, the integration of potable and non-potable water systems is considered. The optimal water management decisions, including the location of desalination, wastewater treatment, and reclamation plants, as well as the conveyance infrastructure for desalinated water, wastewater and reclaimed water, are obtained by minimising the annualised total capital and operating costs. Finally, the proposed approach is applied to two Greek islands: Syros and Paros-Antiparos, for case study and scenario analysis.

Supply chain design and multilevel planning : An industrial case

Abstract In this paper we address a case study, inspired by a real agrochemicals supply chain, with two main objectives, structured in two stages. In the first stage we redesign the global supply chain network and optimise the production and distribution plan considering a time horizon of 1 year, providing a decision support tool for long term investments and strategies. The output decisions from the first stage, mainly the supply chain configuration and allocation decisions, are the input parameters for the second stage where a short term operational model is used to test the accuracy of the derived design and plan. The outputs of this stage are detailed production and distribution plans and an assessment of the customer service level. At the operational level, failure to meet on time the demand fulfilment targets established at the planning stage is usually caused by allocation of too many products/customers to the same resource in the first stage, especially to those surrounding the system bottlenecks. This introduces idle periods in the planning of the bottleneck resources, preventing the whole system from operating at its maximum capacity. An analytical methodology was developed to use the information gathered in the second step to improve the supply chain design and plan by enforcing a more distributed allocation of products/customers to the available resources in each time period.