Assessment of joint inventory replenishment: a cooperative games approach

This research deals with the design of a logistics strategy with a collaborative approach between non-competing companies, who through joint coordination of the replenishment of their inventories reduce their costs thanks to the exploitation of economies of scale. The collaboration scope includes sharing logistic resources with limited capacities; transport units, warehouses, and management processes. These elements conform a novel extension of the Joint Replenishment Problem (JRP) named the Schochastic Collaborative Joint replenishment Problem (S-CJRP). The introduction of this model helps to increase practical elements into the inventory replenishment problem and to assess to what extent collaboration in inventory replenishment and logistics resources sharing might reduce the inventory costs. Overall, results showed that the proposed model could be a viable alternative to reduce logistics costs and demonstrated how the model can be a financially preferred alternative than individual investments to leverage resources capacity expansions. Furthermore, for a practical instance, the work shows the potential of JRP models to help decision-makers to better understand the impacts of fleet renewal and inventory replenishment decisions over the cost and CO2 emissions.DoctoradoDoctor en Ingeniería Industria

an evolutionary approach for the offsetting inventory cycle problem

AbstractIn inventory management, a fundamental issue is the rational use of required space. Among the numerous techniques adopted, an important role is played by the determination of the replenishment cycle offsetting which minimizes the warehouse space within a considered time horizon. The NP-completeness of the Offsetting Inventory Cycle Problem (OICP) has led the researchers towards the development and the comparison of specific heuristics. We propose and implement a genetic algorithm for the OICP, whose effectiveness is validated by comparing its solutions with those found by a mixed integer programming model. The algorithm, tested on realistic instances, shows a high reduction of the maximum space and a more regular warehouse saturation with negligible increase of the total cost. This paper, unlike other papers currently available in literature, provides instances data and results necessary for reproducibility, aiming to become a benchmark for future comparisons with other OICP algorithms

INVALS: An Efficient Forward Looking Inventory Allocation System

We design an Inventory Allocation System (INVALS) that, for each item-store combination, plans the quantity to be allocated from a warehouse replenishing multiple stores using trailers, while respecting the typical supply-chain constraints. We formulate a linear objective function which when maximised computes the allocation by considering not only the immediate store needs, but also its future expected demand. Such forward-looking allocation significantly improves the labour and trailer utilisation at the warehouse. To reduce overstocking, we adapt from our objective to prioritise allocating those items in excess which are sold faster at the stores, keeping the days of supply (DOS) to a minimum. For the proposed formulation, which is an instance of Mixed Integer Linear Programming (MILP), we present a scalable algorithm using the concepts of submodularity and optimal transport theory by: (i) sequentially adding trailers to stores based on maximum incremental gain, (ii) transforming the resultant linear program (LP) instance to an instance of capacity constrained optimal transport (COT), solvable using double entropic regularization and incurring the same computational complexity as the Sinkhorn algorithm. When compared against the planning engine that does the allocation only for immediate store needs, INVALS increases on an average the labour utilization by 34.70 and item occupancy in trailers by 37.08. The DOS distribution is also skewed to the left indicating that higher demand items are allocated in excess, reducing the days they are stocked. We empirically observed that for ~ 90% of replenishment cycles, the allocation results from INVALS are identical to the globally optimal MILP solution

Maintenance spare parts planning and control : a framework for control and agenda for future research

This paper presents a framework for planning and control of the spare parts supply chain in organizations that use and maintain high-value capital assets. Decisions in the framework are decomposed hierarchically and interfaces are described. We provide relevant literature to aid decision making and identify open research topics. The framework can be used to increase the e??ciency, consistency and sustainability of decisions on how to plan and control a spare parts supply chain. Applicability of the framework in di??erent environments is investigated

Inventory routing for dynamic waste collection

We consider the problem of collecting waste from sensor equipped underground containers. These sensors enable the use of a dynamic collection policy. The problem, which is known as a reverse inventory routing problem, involves decisions regarding routing and container selection. In more dense networks, the latter becomes more important. To cope with uncertainty in deposit volumes and with fluctuations due to daily and seasonal e ects, we need an anticipatory policy that balances the workload over time. We propose a relatively simple heuristic consisting of several tunable parameters depending on the day of the week. We tune the parameters of this policy using optimal learning techniques combined with simulation. We illustrate our approach using a real life problem instance of a waste collection company, located in The Netherlands, and perform experiments on several other instances. For our case study, we show that costs savings up to 40% are possible by optimizing the parameters

Effective prepositioning of relief inventory for humanitarian operations in the Central African Region

Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2023.Inventory management is a crucial aspect of humanitarian operations. Various inventory models and policies have been developed over the years to improve the efficiency of humanitarian inventory management. These models consider various elements, including sourcing, storage, prepositioning, distribution, and transportation. While the existence of literature and models supplied guidance and breakthroughs towards more informed decision-making, the complex setting of disasters has continued to preclude their application. Over-simplification, impracticality, and particularity of decision variables pose a challenge in using specific models in exceptionally distinct disasters owing to their complexity and ever-changing nature. This implies that the ability to manage inventory efficiently and its distribution depends on the preparedness and prevailing conditions in the post disaster period. This study focused on approaching these shortcomings by adopting an integrated approach which starts with the characterisation of inventory management challenges unique to disaster settings. Gaps within developed models are identified, and an inventory prepositioning and aid distribution model is developed and applied to bridge some gaps. Therefore, this study presents two models (deterministic and stochastic programming with recourse) for prepositioning modelling. The models are implemented as multi-objective mixed-integer linear programming relief inventory prepositioning models for the Democratic Republic of Congo (DRC) and Central African Republic (CAR). The models minimise shortages and enhance equitability while minimising the total response time in areas with poor road network in a cross-border distribution setting. The model is solved using a pre-emptive optimisation approach, and a sensitivity analysis is conducted to evaluate the influence of the budget, priority items proportion, and capacity variation in the model input. Results indicate that the models are sensitive to changing parameters. Of the two models, the stochastic model was determined to have higher reliability but required a higher budget to match the performance of the deterministic model. Results analyses confirm that the models can add value to humanitarian organisations when planning facility locations, inventory prepositioning, and conflict area-distribution centre assignments in the DRC and CAR. This study, therefore, contributes to the body of knowledge and humanitarian organisations in Africa.Industrial and Systems EngineeringMEng (Industrial Engineering)Unrestricte