Chair: Songsong Liu, University College London, United Kingdom
Evaluating supply chain resilience under different types of disruption
Sonia Cardoso (firstname.lastname@example.org), Ana Barbosa-Póvoa (email@example.com), Susana Relvas (firstname.lastname@example.org), Augusto Novais (email@example.com)
In the present work a design and planning model with uncertainty in products’ demand is applied to two supply chains (SCs): a traditional forward network and a closed-loop supply chain (CLSC). The objective is to maximize the ENPV and evaluate their resilience towards different types of disruption. Four disruptions that affect different SC echelons are implemented, with uncertainty in their occurrence being also considered simultaneously with products’ demand uncertainty. The networks’ resilience is measured using seven indicators, four concerning the network design and three being operational. A case study is solved.
Keywords: Resilience, Uncertainty, Supply chain management
Solution Methods for MIP Production Planning and Scheduling Models
Sara Velez (firstname.lastname@example.org), Christos Maravelias (email@example.com)
The modeling of time plays a key role in the formulation of mixed-integer programming (MIP) models for scheduling, production planning, and operational supply chain planning problems. It affects the size of the model, the computational requirements, and the quality of the solution. While many types of time grids have been proposed in the literature for continuous-time models, discrete-time models have always been thought of as having a single grid with equally spaced time points. However, the ability to incorporate different grids into a single model is useful in many situations. For example, processes in different facilities that interact with each other may use different grids. We present methods to formulate discrete-time multi-grid models that allow different tasks, units, or materials to have their own time grid. Next, we present a simple reformulation that introduces an integer variable representing the total number of batches in each task and unit. Tasks in units with idle time can be shifted earlier or later to create equivalent schedules that have the same number of batches and the same objective, but different variable values. Adding this variable allows the branch-and-bound algorithm to branch directly on the number of batches and to quickly find solutions with different objective values. Together, the multi-grid formulation and the new integer variable reduce solution times by several orders of magnitude.
Keywords: Scheduling, MIP Models, Solution Methods
Fair Transfer Prices of Global Supply Chains in the Process Industry
Songsong Liu (firstname.lastname@example.org), Roberto Fucarino (email@example.com), Lazaros Papageorgiou (firstname.lastname@example.org)
A supply chain involves all activities transforming raw materials to final products and delivering them to the customers. During the past decade with rapid globalisation, many companies’ production plants and delivery centres are located in multiple countries, maybe also in different continents. In a supply chain, its total profit aims to be maximised to enhance its performance. Meanwhile, how to distribute the profit of the whole supply chain fairly to ensure adequate rewards for each member involved is still a key issue. Transfer prices, consisting of procurement, manufacturing, and selling prices within a supply chain, affect the costs and revenues of the members at different echelons in a supply chain, and then influence the supply chain strategies, including production, inventory, and distribution decisions. Transfer prices are considered as a mechanism for profit distribution. This work aims to develop an optimisation-based framework for the fair profit distribution among the members involved in a global supply chain in the process industry. We propose a mixed integer linear programming (MILP) model for the optimal production and distribution planning of the global supply chain, where the fair transfer prices of products between plants and markets are also to be optimised. The proposed model is solved by two solution approaches, Nash approach and lexicographic maximin approach, to find the fair profit distribution to the supply chain’s members. By examining an illustrative example, the obtained results are studied and discussed. The obtained results demonstrate the applicability of the proposed models and approaches.
Keywords: transfer price, fair profit distribution, mixed integer programming