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This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels. [ABSTRACT FROM AUTHOR]

This paper addresses the relevant topic of supply chain agility. Our work proposes a quantitative approach to evaluate and improve agility, in the context of a serial end-to-end supply chain. We define a metric to assess the agility of a supply chain. Then, we develop a supply chain flow model that integrates various parameters related to physical and information/decision flows such as production and logistics/transportation capacities and lead times, frozen planning horizons, target stocks ... This flow model is then used to simulate the supply chain information and physical flows in the nominal mode and when a situation needing agility happens. The model enables to calculate the agility metric when no action is done as well as in situations where the use of some agility levers is considered. A numerical study is presented allowing to illustrate the model developed in this work. We also discuss some interesting insights we have obtained from real-life applications of our model. [ABSTRACT FROM AUTHOR]

Emergency Medical Service (EMS) managers continuously strive to improve the coverage performance, i.e., the percentage of calls responded to within a specific target time, to save lives in case of life-threatening emergencies. This goal can be achieved by dynamically adjusting the location of rescue teams during a day in response to some temporal or geographical fluctuations such as demand patterns, traffic conditions, or the number of teams on duty. This relocation is known as the multi-period redeployment problem. In this study, we propose a discrete simulation-based optimization model to adress the multi-period redeployment problem in the French EMS of the Val-de-Marne department (France), named SAMU 94. The proposed model uses an iterative method that combines the use of a mathematical model to find the optimal locations of rescue teams with the use of the SAMU 94 simulation model implemented in Arena software, to evaluate the busy fraction parameters required to solve the mathematical model. The model performance was compared with that of the simulation-based optimization software, OptQuest. The experimental results demonstrated that the iterative method could produce solutions with better coverage performance, 20 times faster than OptQuest. [ABSTRACT FROM AUTHOR]

We calculate optimal safety stock in a periodic review (T,S) assemble-to-order system having multiple components and multiple finished goods (FGs). Customer orders for FGs arrive according to independent Poisson processes, and cannot be neither backlogged nor lost. In case of potential component stock-out, the studied system uses rush deliveries from suppliers. For this setting, approximate expressions of the optimal safety stock that minimise the sum of inventory holding and rush ordering costs are developed. Exact optimal safety stocks are calculated using Discrete Event Simulation, and compared numerically to the approximate expressions. The model is applied to a first-tier automotive supplier and yields to a significant reduction in terms of inventory holding and rush ordering costs. A sensitivity analysis on relevant system parameters such as components demand, assembly coefficients and unit rush ordering cost is conducted. [ABSTRACT FROM AUTHOR]

Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as when faced with unforeseen constraints. A multimodal optimization task aims to find multiple global optima as well as high-quality local optima of an optimization problem. Evolutionary algorithms with niching techniques are commonly used for such problems, where a rough estimate of the optima number is required to determine the population size. In this paper, a partition-based random search method is proposed, in which the entire feasible domain is partitioned into smaller and smaller subregions iteratively. Promising regions are partitioned faster than unpromising regions, thus, promising areas will be exploited earlier than unpromising areas. All promising areas are exploited in parallel, which allows multiple good solutions to be found in a single run. The proposed method does not require prior knowledge about the optima number and it is not sensitive to the distance parameter. By cooperating with local search to refine the obtained solutions, the proposed method demonstrates good performance in many benchmark functions with multiple global optima. In addition, in problems with numerous local optima, high-quality local optima are captured earlier than low-quality local optima. [ABSTRACT FROM AUTHOR]

With increasing components’ variety in mixed-model assembly lines, industrials show interest in kitting operations using robots. This paper deals with a hybrid kitting system that consists of a robot and an operator working in series to deliver parts to a Just-In-Time mixed-model assembly line. We develop a mathematical model that optimally assigns stock keeping units to either the robot or the operator so that the cycle time of the overall system is optimised. To test the model, a case study from the automotive sector is presented and a sensitivity analysis is carried out on relevant system parameters. [ABSTRACT FROM AUTHOR]

As e-commerce expands, more and more products are offered online to attract internet consumers' interest. These products are then shipped to consumers' home by a drop-shipper. Drop-shipping seems to be a good option to sell products in addition to physical stores. Furthermore, both types of products, either sold in store or on Internet can be returned by consumers, with often a higher return ratio for those purchased on Internet. To model these two sales channel and the interactions between them, we consider a News-Vendor (NV) managing both a physical store and an online sale channel that can be fulfilled by a drop-shipping option. We also consider the possibility of reselling products that are returned by consumers during the selling season. The concavity of the expected profit is proven and the optimality condition is obtained. Promising results are obtained from a numerical analysis. In particular, we show that the expected profit can be 14.4% less than the optimal expected profit if the return effect is ignored. Using drop-shipping option can reduce the optimal store inventory by 31.2% and if the NV has no drop-shipping option, the expected profit can be 9% less. [ABSTRACT FROM AUTHOR]

In high diversity, Just In Time production environments, mixed-model assembly lines aim at producing a large amount of end products that use alternative variants of components used in the assembly process. Therefore, the availability of parts required at the border of the assembly line is critical for the production process. To ensure the availability of parts needed on the line, three line feeding modes are commonly used in practice: line stocking, kitting and sequencing. The relative performance of each mode, in terms of average total operating cost, depends on various factors such as the diversity of components, parts’ physical features (i.e. weight and volume), to name a few. The impact of such parameters on the performance of line feeding modes remains still weakly studied in the literature. Through an empirical approach based on a real case, our study aims at evaluating the average total cost pertaining to each mode. The total cost includes parts preparation before assembly, picking, in-plant transportation and storage costs. A thorough comparative study enables then to characterise situations which make one mode less costly compared with the others. Each situation corresponds to specific values taken by parameters that are considered in the cost formulation. [ABSTRACT FROM PUBLISHER]