Integrated Planning of Industrial Gas Supply Chains

In this work, we propose a Mixed Integer Linear Programming (MILP) model for optimal planning of industrial gas supply chain, which integrates supply contracts, production scheduling, truck and rail-car scheduling, as well as inventory management under the Vendor Managed Inventory (VMI) paradigm. The objective used here is minimisation of the total operating cost consisting of purchasing of raw material, production, and transportation costs by trucks/rail-cars so as to satisfy customer demands over a given time horizon. The key decisions for production sites include production schedule and purchase schedule of raw material, while the distribution decisions involve customer to plant/depot allocation, quantity transported through rail network, truck delivery amounts, and times. In addition, a relaxation approach is proposed to solve the problem efficiently. An industrial case study is evaluated to illustrate the applicability of the integrated optimisation framework

Network design decisions in supply chain planning

Structuring global supply chain networks is a complex decision-making process. The typical inputs to such a process consist of a set of customer zones to serve, a set of products to be manufactured and distributed, demand projections for the different customer zones, and information about future conditions, costs (e.g. for production and transportation) and resources (e.g. capacities, available raw materials). Given the above inputs, companies have to decide where to locate new service facilities (e.g. plants, warehouses), how to allocate procurement and production activities to the variousmanufacturing facilities, and how to manage the transportation of products through the supply chain network in order to satisfy customer demands. We propose a mathematical modelling framework capturing many practical aspects of network design problems simultaneously. For problems of reasonable size we report on computational experience with standard mathematical programming software. The discussion is extended with other decisions required by many real-life applications in strategic supply chain planning. In particular, the multi-period nature of some decisions is addressed by a more comprehensivemodel, which is solved by a specially tailored heuristic approach. The numerical results suggest that the solution procedure can identify high quality solutions within reasonable computational time

Integrated production and inventory routing planning of oxygen supply chains

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework

Hierarchical Approach to Integrated Planning of Industrial Gas Supply Chains

In this article, an optimization-based framework is proposed for integrated production and distribution planning of industrial gas supply chains. The main goal is to minimize the overall cost, which is composed of raw material, product sourced from external suppliers, production, truck, and rail-car costs, while satisfying customer demands. The overall problem is formulated as a mixed-integer linear programming (MILP) model while a two-phase hierarchical solution strategy is developed to solve the resulting optimization problem efficiently. The first phase relies on truck scheduling decisions being relaxed, whereas the second phase solves the original model at reduced space by fixing product allocation as determined by phase one. Finally, an industrial-size case study is used to illustrate the applicability and efficiency of the proposed optimization framework

An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem

We consider a stochastic single item production-inventory-routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first-stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances.publishe

Production-Distribution Model Considering Traceability and Carbon Emission: A Case Study of the Indonesian Canned Fish Food Industry

Background: Traceability systems and carbon emissions are two important factors involved in production and distribution activities. The involvement of these two factors in production and distribution activities along the supply chain will ensure the safety and quality of food through the manufacture, packaging and distribution of products with minimal costs and in an environmentally friendly way. Objective: This study aimed to develop a model of canned fish food production and distribution integration by considering traceability and carbon emissions to minimize total costs. Method: A mixed-integer linear programming (MILP) approach was used to develop mathematical models and the optimal solution of the model created was obtained using an open-source spreadsheet solver program. Results: The results show that the proposed models produce the minimum total production and distribution cost with high traceability and low carbon emissions. Conclusions: The sensitivity analysis from this study shows that there is a significant relationship between production, carbon emissions, and the total cost of production-distribution. Moreover, it was concluded that the production level, carbon emission level, and emission threshold can have a significant influence in the generation of the total carbon emissions