Customer Demand Rates Research Articles

The Impact of Wall Structure with Photovoltaic Function on Prefabricated Buildings

The wall structure is the main load-bearing component of the prefabricated building, which plays an important role in the stability and aesthetics of the building. With the deepening of the application of new energy in the construction field, the application of photovoltaic wall with photovoltaic function is becoming more and more extensive. However, there is still controversy about whether the photovoltaic function can meet the requirements of the building. On this basis, this paper discusses the wall structure with photovoltaic function in depth, and analyzes its energy consumption, heat preservation, ventilation and cost indicators. The results show that the wall structure of photovoltaic function can improve the aesthetics of prefabricated buildings, meet customer demand rate of more than 75%, save energy consumption by 25%~30%, increase indoor temperature by 1~3 degrees, and ventilate at 1.2 levels. It can be inferred that the load-bearing wall of photovoltaic function is suitable for prefabricated buildings and has the potential and role of vigorous development.

A state-dependent M/M/1 queueing location-allocation model for vaccine distribution using metaheuristic algorithms

Abstract Controlling and maintaining public health in the face of diseases necessitates the effective implementation of response strategies, including the distribution of vaccines. By distributing vaccines, vulnerable populations can be targeted, individuals can be protected, and the spread of diseases can be minimized. However, managing vaccine distribution poses challenges that require careful consideration of various factors, including the location of distribution facilities. This paper proposes a novel model that combines location-allocation problems with queueing systems methodologies to optimize the efficiency of vaccine distribution. The proposed model considers factors such as uncertain demand, varying service rates, depending on the system state. Its primary objective is to minimize total costs, which encompass the establishment and adjustment of the service mechanism, travel times, and customer waiting time. To forecast customer demand rates, the model utilizes time-series techniques, specifically the seasonal Autoregressive Integrated Moving Average model. In order to tackle large-scale problems, a total of 16 newly developed metaheuristic algorithms are employed, and their performance is thoroughly evaluated. This approach facilitates the generation of solutions that are nearly optimal within a reasonable timeframe. The effectiveness of the model is evaluated through a real-life case study focused on vaccination distribution in Iran. Furthermore, a comprehensive sensitivity analysis is conducted to demonstrate the practical applicability of the proposed model. The study contributes to the advancement of robust decision-making frameworks and provides valuable insights for addressing location-related challenges in health systems.

Integrating Customer Order Decoupling Point with Operation Planning in Deteriorating Supply Chain: General Dynamic Model and Applications

Considering the real dilemma of deteriorating items, we explore how to find a balance between the conflicting achievements of enhancing flexibility and reducing costs. We propose a general dynamic model to penetrate customer orders by decoupling point with objective to minimize the cost. The closed forms of optimal solutions are obtained for companies to deal with the integrated problem of customer order decoupling point (CODP) decision and production–inventory plan simultaneously. Applications and numerical experiments are performed to illustrate practical insights for managers. The results show that the truth of zero-inventory policy is to avoid unnecessary inventory instead of absolutely no inventory. The CODP will shift forward with the increasing of customer demand rate and shift backward with the increasing of deterioration rate under the production smoothing policy. However, although it encounters the same changes of demand, the CODP moves with a smaller extent in high deterioration rate than in low. In addition, we find that during the growth phase of product life cycle, the increase of time-sensitive degree of demand is coupled with forward shifting of CODP; while during the decline phase, it is coupled with CODP backward shifting.

Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Abstract Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.

Enhanced sourcing strategy for centralized multitier multiple suppliers network with failure risks

PurposeThis paper attempts to address three key objectives. The primary aim is to enhance sourcing strategy for a centralized and coordinated multitier multiple suppliers networks with uncertain demand and supplier failure risks. The second objective is to enumerate all possible practical supplier(s) failure scenarios and quantify expected loss of demand cost. Finally, the work illustrates statistical experimentation to identify “influential” variables that can significantly impact the expected supply network and loss costs.Design/methodology/approachA seven-step solution framework is proposed to derive an optimal sourcing strategy for the specific network configuration with varied supplier failure scenarios. Five distinct models are formulated to address all possible scenarios of supplier failure events. Mixed-integer nonlinear programming technique is used to derive expected supply network cost and loss cost. The solution framework is verified using a real-life case.FindingsA cross-case analysis indicates that an increase in suppliers' failure risk (SFR) probabilities or customer demand rate increases the expected loss of demand costs for a multitier supply network. Besides, an increase in unit component prices increases the expected supply network cost.Research limitations/implicationsA two-tier automotive supply network for a single product is considered for all case studies.Practical implicationsThe enhanced strategy can facilitate practitioners enumerate different supply network failure scenarios and implement the best solution.Originality/valueThere is no evidence of earlier research to derive optimal sourcing strategy for a centralized, coordinated multitier multiple supplier's network, considering demand uncertainties and SFR.

Ride-Sourcing Modeling and Pricing in Non-Equilibrium Two-Sided Markets

Abstract Ride-sourcing is a prominent transport mode because of its cost-effectiveness and convenience. It provides an on-demand mobility platform that acts as a two-sided market by matching riders with drivers. The conventional models of ride-sourcing systems are based on equilibrium assumption, discrete, and suitable for strategic decisions. This steady-state approach is not suitable for operational decision-making where there is noticeable variation in the state of the system, denying the market enough time to balance back into equilibrium. We introduce a dynamic non-equilibrium ride-sourcing model that tracks the time-varying number of riders, vacant ride-sourcing vehicles, and occupied ride-sourcing vehicles. The drivers are modeled as earning-sensitive, independent contractor, and self-scheduling and the riders are considered price- and quality of service-sensitive such that the supply and demand of the ride-sourcing market are endogenously dependent on (i) the fare requested from the riders and the wage paid to the drivers and (ii) the rider’s waiting time and driver’s cruising time. The model enables to investigate how dynamic wage and fare set by the ride-sourcing service provider affect supply, demand, and states of the market such as average waiting and search time especially when drivers can freely choose when to start and finish working. Furthermore, we propose a controller based on the model predictive control approach to maximize the service provider’s profit by controlling the fare requested from riders and the wage offered to drivers to satisfy a certain quality of market performance. We assess three pricing strategies where the fare and wage are (i) time-varying and unconstrained, (ii) time-varying and constrained so that the fare is higher than the wage such that the instantaneous profit is positive, and (iii) time-invariant and fixed. The proposed model and controller enable the ride-sourcing service provider to offer a wage to the drivers that is higher than the charged fare from the riders. The result demonstrates that this myopic loss can potentially lead to higher overall profit when customer demand rate who may opt to use the ride-sourcing system increases while the demand of ride-sourcing vehicles decreases simultaneously.

Joint Dynamic Pricing and Inventory Control for Perishable Products Taking into Account Partial Backlogging and Inflation

A joint dynamic pricing and inventory model by considering the inflation and time value of money where the product has a limited lifetime is developed. The selling price depends on product lifetime and customer demand rate depends on the selling price and time. Also, the shortages are considered and partially backlogged. The main goal is to determine the optimal initial selling price, the economic order quantity, and the optimal replenishment period simultaneously in order to maximize the total net present value of profit. We represent that for given any number of replenishment the function of profit is concave. In order to find the optimal solution, the presented heuristic algorithm is applied. To represent the algorithm and procedure of solution, a numerical example is solved. The results show that applying the proposed dynamic pricing approach leads to a significant improvement in profit’s retailer. Finally, sensitivity analysis of key parameters model is performed and some managerial insights are presented.

Buyer’s Pricing and Lot-sizing Policy with Price Dependent Demand under Day terms Supplier Credit in a Two-Stage Supply Chain

As an effective means of price discrimination, some suppliers offer credit periods to the buyers in order to stimulate the demand for the product they produce. The availability of the credit period from the supplier enables the buyer to choose his selling price from a wider range of price option. Since the buyer's lot-size is affected by the customer's demand rate of the product, the problems of determining the selling price and the lot-size are interdependent and must be solved simultaneously. In this regard, this paper deals with the problem of determining the buyer’s optimal selling price and lot-size simultaneously when the supplier permits delay in payments for an order of a product. The positive effects of credit period on the product demand can be integrated into the inventory model through the consideration of retailing situations, where the demand rate is a function of the selling price the buyer sets for the product. Thus, it is also assumed that the customer’s demand rate is represented by a downward slopping linear function of the selling price. Investigation of the properties of an optimal solution allows us to develop an algorithm whose validity is illustrated using an example problem.

Algorithmic Methods for Concave Optimization Problems

In this thesis, we reformulate the original non-linear model for the LMRP. Firstly, we introduced a set of parameters to represent the non-linear part of the cost increase for a facility space allocated potential additional costs and new set of decision variables, indicating how many customers each equipment distribution. The algorithms are tested on problems with 5 to 500 potential facilities and randomly generated locations. Then using actual data to validate this new method is better. Our work was motivated by the modeling approach used in the Maximum Expected Covering Location Problem (MEXCLP). We compare new method and Lagrangian relaxation method to solve LMRP with constant customer demand rate and equal standard deviation of daily demand.

A Statistical Comparison of Two Safety Stock Replenishment Mechanisms in a Cyclic Stochastic IRP

Preventing stock-out in a replenishment system, in which customer demand rates are stochastic with constant averages, can be accomplished via safety stocks hold at each customer. These safety stocks should be replenished back to their initial levels each time they are used. However, sometimes it occurs that a truck does not have enough capacity to carry the amount of the product the visited customers need to restore their stocks and safety stocks to their required levels. Therefore, the carried extra amount of the product, intended to replenish safety stocks, should be divided amongst the customers in some optimal or fair manner. To achieve this fair allocation, we propose and analyze two policies, the first called Fair-share and the second Ratio methods. Details on how these two methods are implemented, to achieve the level of service expected at each customer, are discussed and illustrated. In addition a simulation model is developed and used to compare the performance of each policy in the long run.

Cyclic Versus Reactive Planning for Inventory Routing

For the Inventory Routing Problem (IRP), two research streams are found in the literature. One stream assumes stochastic demands at the customers and adopts a reactive, dynamic approach in which new routes are designed every day to replenish customers that are almost out of stock. The second research stream assumes constant demand rates and adopts a static, cyclic approach in which customers are partitioned into subsets and then periodically replenished in routes with optimized cycle times. In this paper, we link these two research streams by investigating the validity of the cyclic approach for stochastic customer demand rates. We show that a cyclic, static approach is still valid under fluctuating customer demands, by carefully providing sufficient slack through a combination of safety stock at the customers and spare capacity in the replenishment routes. The advantage of the cyclic approach is that replenishment frequencies and vehicle routes remain the same over time. As such, the cyclic approach takes better advantage of the variability pooling effect, and the demand variability is buffered in the distribution stage, resulting in a reduced bullwhip effect in the upstream stages. Illustrative examples comparing the cyclic and reactive approaches confirm that the cyclic approach indeed offers cost efficient solutions with limited variability dissipation to upstream stages.

An Integrated Model with Variable Production and Demand Rate under Inflation

In this article, an integrated model is developed in which a manufacturer purchases raw materials from a supplier, and then produces finished products/goods, after that delivers them to a buyer. In the intended model production rate is assumed as a function of demand rate and customer demand rate is time dependent. To make the model more realistic the effect of inflation and time value of money is also taken into consideration. The concept of the model is illustrated through the numerical example and sensitivity analysis with respect to the system parameters is also performed.

A just-in-time three-level integrated manufacturing system for linearly time-varying demand process

This paper considers a just-in-time (JIT) manufacturing system in which a single manufacturer procures raw materials from a single supplier, process them to produce finished products, and then deliver the products to a single-buyer. The customer demand rate is assumed to be linearly decreasing time-varying. In the JIT system, in order to minimize the suppliers as well as the buyers holding costs, the supply of raw materials and the delivery of finished products are made in small quantities. In this case, both the supply and the delivery may require multiple installments for a single production lot. We develop a mathematical model for this problem, propose a simple methodology for solving the model, and illustrate the effectiveness of the method with numerical examples.

A practical solution approach for the cyclic inventory routing problem

Abstract Vendor managed inventory (VMI) is an example of effective cooperation and partnering practices between up- and downstream stages in a supply chain. In VMI, the supplier takes the responsibility for replenishing his customers’ inventories based on their consumption data, with the aim of optimizing the over all distribution and inventory costs throughout the supply chain. This paper discusses the challenging optimization problem that arises in this context, known as the inventory routing problem (IRP). The objective of this IRP problem is to determine a distribution plan that minimizes average distribution and inventory costs without causing any stock-out at the customers. Deterministic constant customer demand rates are assumed and therefore, a long-term cyclical approach is adopted, integrating fleet sizing, vehicle routing, and inventory management. Further, realistic side-constraints such as limited storage capacities, driving time restrictions and constant replenishment intervals are taken into account. A heuristic solution approach is proposed, analyzed and evaluated against a comparable state-of-the-art heuristic.

Robust distribution planning for supplier-managed inventory agreements when demand rates and travel times are stationary

Supplier-managed inventory (SMI) is a partnering agreement between a supplier and his customers. Under this SMI agreement, inventory monitoring and ordering responsibilities are entirely transferred to the supplier. Subsequently, the supplier decides both the quantity and timing of his customer deliveries. The inventory routing problem is an underlying optimization model for SMI partnerships to cost-effectively coordinate and manage customer inventories and related replenishments logistics. This paper discusses the case where customer demand rates and travel times are stochastic but stationary, and proposes a version of the inventory routing optimization model that generates optimal robust distribution plans. The approach proposed to obtain and deploy these robust plans combines optimization and Monte Carlo simulation. Optimization is used to determine the robust distribution plan and simulation is used to fine-tune the plan's critical parameters such as replenishment cycle times and safety stock levels. Results of a simplified real-life case implementing the proposed optimization-simulation approach are shown and discussed in detail.

Designing distribution patterns for long-term inventory routing with constant demand rates

This paper proposes a practical solution approach for the challenging optimization problem of minimizing overall costs in an integrated distribution and inventory control system. Constant customer demand rates are assumed and therefore a long-term, cyclic planning approach is adopted. The concept of distribution patterns, consisting of vehicles performing multiple tours with possibly different frequencies, is used to extend the traditional concept of a single tour per vehicle. A heuristic is proposed that is capable of solving a cyclical distribution problem involving real-life features, such as customer capacity restrictions, loading and unloading extra times and prespecified minimum times between consecutive deliveries.

The effectiveness of investment in lead time reduction for a make-to-stock product

This paper focuses on a firm selling a make-to-stock product with a constant customer demand rate. The firm follows an exact (Q, r) policy for raw material inventory control and faces a random replenishment lead time. Through this research, we wish to gain a better understanding of the impact of investing in reducing supply lead time when the investment costs have to be borne, partly or fully, by the firm. This work is motivated by the recognition that lead time reduction is now of strategic importance in the successful operation of a firm. We examine different types of investment schemes in replenishment lead time reduction and the different cost models they generate. We present analytical and numerical results and insights for each type of model, compute the optimal (Q, r) policy and the associated investment levels. The work presents new results, and sheds light on some apparently counter-intuitive observations.

The effect of product variety on supply-chain performance

We present a stylized model for analyzing the effect of product variety on supply-chain performance for a supply chain with a single manufacturer and multiple retailers. The manufacturer produces multiple products on a shared resource with limited capacity and the effect of changeovers on supply-chain cost is due primarily to setup time rather than setup cost. We show that the expected replenishment lead time and the retailers' costs are concave increasing in product variety and that the increase is asymptotically linear. Thus, if setup times are significant, the effect of product variety on cost is substantially greater than that suggested by the risk-pooling literature for perfectly flexible manufacturing processes, where the cost increases proportionally to the square root of product variety. We demonstrate that disregarding the effect of product variety on lead time can lead to poor decisions and can lead companies to offer product variety that is greater than optimal. The results of our analysis enable decision-makers to quantify the effect of product variety on supply-chain performance and thus to determine the optimal product variety to offer. The results can also be used to evaluate how changes in the manufacturing process, the supply-chain structure, and the customer demand rate can improve the performance of supply chains with high product variety.

Determining optimal retail price and lot size under day-terms supplier credit

This paper deals with the problem of determining the retailer's optimal price and lot size simultaneously under the condition of permissible delay in payments. It is assumed that the customer's demand rate is represented by a constant price elasticity function which is a decreasing function of retail price. Investigation of the properties of an optimal solution allows us to develop an algorithm whose validity is illustrated using an example problem. Also, the effect of credit period on the retailer's pricing and lot sizing policy is examined through sensitivity analysis.