Inventory Considerations Research Articles

Comment on “Salesforce Compensation with Inventory Considerations”: A Rejoinder

In this rejoinder, we address the concern about equilibrium existence in our previous work published in 2013. By making the simple adjustment of discretizing inventory ordering amounts to prevent inventory orders in arbitrarily small increments, we show that the results from that paper hold, thus maintaining the robustness of the insights obtained. This paper was accepted by Jeannette Song, operations management.

Comment on “Salesforce Compensation with Inventory Considerations”

This note critically analyzes the conditions for equilibrium existence in a model involving incentive schemes and inventory management. It identifies a technical flaw where equilibrium is asserted to exist under certain parameter values when, in fact, it does not. However, by introducing a reasonable assumption that inventory can only be ordered in sufficiently large increments, the qualitative outcomes and key insights remain intact. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.03914 .

On Salesforce Compensation with Inventory Considerations

On Salesforce Compensation with Inventory Considerations

An Overview of Hospital Capacity Planning and Optimisation.

Health care is uncertain, dynamic, and fast growing. With digital technologies set to revolutionise the industry, hospital capacity optimisation and planning have never been more relevant. The purposes of this article are threefold. The first is to identify the current state of the art, to summarise/analyse the key achievements, and to identify gaps in the body of research. The second is to synthesise and evaluate that literature to create a holistic framework for understanding hospital capacity planning and optimisation, in terms of physical elements, process, and governance. Third, avenues for future research are sought to inform researchers and practitioners where they should best concentrate their efforts. In conclusion, we find that prior research has typically focussed on individual parts, but the hospital is one body that is made up of many interdependent parts. It is also evident that past attempts considering entire hospitals fail to incorporate all the detail that is necessary to provide solutions that can be implemented in the real world, across strategic, tactical and operational planning horizons. A holistic approach is needed that includes ancillary services, equipment medicines, utilities, instrument trays, supply chain and inventory considerations.

Salesforce Compensation and Two‐Sided Ambiguity: Robust Moral Hazard with Moment Information

We analyze a salesforce principal‐agent model where both the firm and sales agent have limited information on the effort‐dependent demand distribution, creating two‐sided ambiguity. Under the max–min decision criteria, the firm offers a contract to the agent who exerts unobservable effort to influence the demand distribution. We formulate the problem as a semi‐infinite program and use the agent's shadow prices to construct the least expensive contract. Next, we use the least expensive contract to create a non‐linear optimization model, which provides the firm's optimal robust contract. Due to the problem's complexity, we focus our attention on the class of distribution‐free contracts. We show that using a distribution‐free contract is a necessary condition for achieving the first‐best outcome. Our analysis reveals that the index of dispersion determines whether the optimal distribution‐free contract is linear or quadratic. Finally, we extend our model to incorporate quota‐bonus contracts and inventory considerations. Overall, our results demonstrate that variance information plays a critical role in designing contracts under distributional ambiguity and provides justification for the application of quadratic contracts in practice.

Appreciating how your bread is buttered: improving online order allocation for cross-channel retailers

Existing research has considered how pure online retailers can optimise the allocation of fulfilment responsibilities for online sales. However, the fulfilment policies used by pure online retailers may be suboptimal for their dual-channel counterparts. This paper considers online order allocation policies for cross-channel retail/e-tail organisations which utilise buy online, ship from store (BOSS) fulfilment. Specifically, we develop and evaluate a policy for allocating fulfilment responsibilities for a set of multiproduct online orders across a retailer’s store locations when the retailer also faces in-store demand for the same products. Further, our model incorporates carrier rate schedules into the allocation decision and adjusts allocations based on current and forecasted inventory positions at the stores. As such, the model determines fulfilment responsibilities by balancing quantity discounted carrier shipment costs, fixed costs of handling/packing orders, and expected inventory positions. Computational results highlight the extent to which our policy outperforms single-channel policies without in-store inventory considerations. We find that undertaking BOSS without tailoring online order allocation to the cross-channel setting accommodates online fulfilment at the expense of in-store performance. Conversely, consideration of in-store inventories in the order allocation decision enables a cross-channel retailer utilising BOSS to simultaneously improve service levels and reduce inventory.

A production and distribution framework: Manufacturer dominates

This paper presents a two-stage supply chain problem at the operational level involving a manufacturer and a third-party logistics provider (3PL provider). The integrated problem consists of two subproblems: An m-machine permutation flow shop with inventory considerations for the manufacturer and a routing problem for the 3PL provider. Both agents face tardiness penalty costs in case of late delivery to the customer locations. Assuming imperfect information sharing between the two agents and independence in their own decisions, we investigate the scenario in which the manufacturer dominates the negotiation.In this scenario, the manufacturer imposes the number and composition of vehicles as well as the vehicle departure dates on the 3PL provider. However, lacking the information regarding the actual delivery times he is forced to estimate delivery times at the customer locations in the context of his planning. Following the planning of the manufacturer, the 3PL provider decides on the optimal routing of the vehicles. Both agents aim to minimise their total costs. After formulating the problems as a mixed integer linear program, two metaheuristic algorithms are proposed to solve the scenario. The performance of the heuristics is evaluated on a set of randomly generated instances. The results show that the genetic algorithm is the best performing method.

An intelligent method for dynamic distribution of electric taxi batteries between charging and swapping stations

Taxis are often motivated to drive in crowded areas to earn more money by serving customers. Clean taxicab fleets in cities will have a significant impact on reducing air pollution and cutting emissions. Use of electric taxis is a highly efficient solution to address the issue of greenhouse effects, because electric cars are cleaner and cheaper than gasoline-powered cars. Battery swapping is an efficient and fast recharging method enabling taxi drivers to go to a battery swapping station (BSS) and replace their empty batteries with full ones. We study a new dynamic optimization model that helps the planning process in joint consideration of inventory, routing, and dynamic pricing to distribute electric taxi batteries between a BSS and a battery charging station (BCS). A novel dynamic programming (DP) model is proposed to incorporate a Markov decision process (MDP) with an actual demand function, operator cost, customer delay, and a dynamic pricing strategy using a social optimization function through a look-ahead policy. The results indicate that the average response time to transfer batteries from BCS nodes to BSS nodes under the look-ahead policy is reduced by up to 9% compared to the myopic case. We also observe that the average social welfare under the look-ahead policy increases by 22 % compared to a policy without look-ahead. The numerical analyses highlighted the benefits of supplying batteries at a socially efficient price instead of the standard monopoly price.

An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier’s reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

Using transaction data and product margins to optimise weekly flyers

Customers increasingly expect companies to understand their wants and needs and to market to those desires. Unfortunately, such levels of personalisation can be difficult to accomplish for traditional brick-and-mortar retailers, particularly when the fixed cost of personalised marketing is significant. This paper considers methods for providing customised promotions to customers in the form of weekly flyers. We consider how multiple versions of a weekly flyer can be used by a retailer, which products should be included in each flyer version, and how customer preferences, markup, and inventory considerations impact these decisions. Specifically, transaction histories are used to estimate customer preferences for various product offerings using market basket analysis. These probabilities are then incorporated into an optimisation model for grouping customers into market segments and presenting option sets within each segment that maximise expected marginal profits across the flyers. A heuristic is proposed for solving larger problems and evaluated against lower and upper bounds on the optimal profit. Computational results indicate that leveraging customer transaction data to optimise the product selection and assignment of four unique flyers can increase profit by 7.7% over the optimal single-flyer solution.

A Bi-Level Bi-Objective optimization model for the integrated storage classes and dual shuttle cranes scheduling in AS/RS with energy consumption, workload balance and time windows

Dual shuttle cranes scheduling problem is an option of Automated Storage and Retrieval System (AS/RS), which has paid marginal academic regard, so it could be extended to advance novel challenges that should be investigated. In this paper, an integrated class-based storage location assignment and dual shuttle cranes scheduling problem is addressed by introducing a novel bi-level optimization model. In the upper-level, the locations of storing and retrieving products under class-based storage policy, multi-period planning and inventory considerations are determined, which its objective functions are included minimization of total cost and energy consumption. The solution of this level is utilized by the lower-level, which is related to the scheduling of dual shuttle cranes. In the lower-level, a broad range of necessities of rewarding scheduling, ranging from workload balance to time windows under multi-period planning is reflected. Since the proposed model is a bi-level and bi-objective, a Modified Cooperative Coevolutionary Algorithm for Bi-Level Optimization (MCoBRA) is presented with the intention of solving the proposed model. Finally, in order to illustrate the validation of the proposed model and solution approach, a broad range of numerical experiments are sketched out, and some comparative analyses concerning two existing algorithms in the literature are provided. Sensitivity analyses show that considering class-based storage policy in the upper-level has significant influences on the whole of the objective functions, which could be offered better planning of operations.

Dynamic Personalized Pricing with Inventory Considerations

Due to the availability of data-driven customer profiling, many firms (e.g., Amazon) have adopted personalized pricing in practice. We consider a joint dynamic personalized pricing-inventory control problem for a firm that sells a single product to multiple customer segments over a finite selling season. In each period, the demand of each customer segment is stochastic and price-dependent, and any unsatisfied demand is backordered. The firm's objective is to decide on the personalized pricing and ordering policies in order to maximize the expected profit over the planning horizon. We find that, under the mixture of additive and multiplicative demand models, a base-stock personalized price (BSPP) policy is optimal. For the special case with only the additive (resp. multiplicative) demand model, we derive a sequential property that is determined by the (resp. normalized) marginal revenues of different customer segments. Moreover, when the demands are linearly price-dependent under the additive demand model, a uniform adjustment policy for personalized pricing is optimal. Exploiting the BSPP policy, we further provide sufficient conditions (i.e., stationary parameter settings) under which a myopic policy is optimal. Our results provide guidelines for firms on how to coordinate personalized pricing and inventory in practice. We also numerically investigate the benefits of personalized pricing and compare it with uniform pricing. We find that personalized pricing can reap significant benefits (e.g., increasing profit up to 30%). Finally, we show that our results are robust to the settings with correlated demands and limited capacities.

Tabu-search-based order seat planning for engineer-to-order manufacturing

This paper focuses on engineer-to-order (ETO) products, where the combination of components varies according to the customer requirements. However, components are not specialised for each product; rather, multiple components can be used for different products ('shared component' hereinafter). An issue faced by target ETO companies is that although they always have a huge component inventory, the order-receiving rate is low. One reason for this is that the components are allocated to orders sequentially; therefore, orders arriving later may have a higher chance of refusal, particularly under conditions of component shortages. To solve this problem, an order seat booking system is proposed with consideration of short-term demand information, component inventory, and component combinations for different product specifications. A seat planning algorithm based on tabu search is developed to increase the seat count by distributing the shared components among the order seats. The effect of increased order intake by using the planned order-receiving system is confirmed by numerical experiments.

Simulation-Based Optimization for the Integrated Control of Production and Logistics: A Performance Comparison

Manufacturing systems’ efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve the overall manufacturing performance. However, the integration can result in high complexity and stochasticity. Thus, the three areas are rarely addressed together. Thereof, this paper proposes an integrated simulation-based optimization method to cope with uncertainty and complexity. The proposed approach was compared to a benchmark approach and the obtained results show that the first is able to handle the complexity and stochasticity of real-world manufacturing systems, surpassing the performance of the latter.

Tabu-search-based order seat planning for engineer-to-order manufacturing

This paper focuses on engineer-to-order (ETO) products, where the combination of components varies according to the customer requirements. However, components are not specialised for each product; rather, multiple components can be used for different products ('shared component' hereinafter). An issue faced by target ETO companies is that although they always have a huge component inventory, the order-receiving rate is low. One reason for this is that the components are allocated to orders sequentially; therefore, orders arriving later may have a higher chance of refusal, particularly under conditions of component shortages. To solve this problem, an order seat booking system is proposed with consideration of short-term demand information, component inventory, and component combinations for different product specifications. A seat planning algorithm based on tabu search is developed to increase the seat count by distributing the shared components among the order seats. The effect of increased order intake by using the planned order-receiving system is confirmed by numerical experiments.

Ripple effect quantification by supplier risk exposure assessment

Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis.

Toxic Chemical Release Hazard Distance Determination Using Chemical Exposure Index (CEI) in a Gas Refinery

Events leading up to the release of toxic chemicals in the processing plants are one of the main hazards of chemical industries that can endanger employees and also people in neighborhood. In this study, DOW's Chemical Exposure Index (CEI) is used to determine hazard distances of possible toxic chemical releases in one of the South Pars gas refineries. To do this, 318 considerable release scenarios were identified and by process parameters and CEI equations, airborne quantity, chemical exposure index and hazard distances were calculated. In the worst case of a toxic chemical release, hazard distance of the studied refinery is 10000 meters. The Sludge Catcher unit is the most dangerous unit in terms of toxic chemical release. In addition to the advantages of the CEI observed in this study also some limitations were observed including sensitivity to process parameters, no consideration of the material inventory and the concentration in the CEI calculations

Technical Note—Multiproduct Pricing Under the Generalized Extreme Value Models with Homogeneous Price Sensitivity Parameters

We consider unconstrained and constrained multiproduct pricing problems when customers choose according to an arbitrary generalized extreme value (GEV) model and the products have the same price sensitivity parameter. In the unconstrained problem, there is a unit cost associated with the sale of each product. The goal is to choose the prices for the products to maximize the expected profit obtained from each customer. We show that the optimal prices of the different products have a constant markup over their unit costs. We provide an explicit formula for the optimal markup in terms of the Lambert-W function. In the constrained problem, motivated by the applications with inventory considerations, the expected sales of the products are constrained to lie in a convex set. The goal is to choose the prices for the products to maximize the expected revenue obtained from each customer, while making sure that the constraints for the expected sales are satisfied. If we formulate the constrained problem by using the prices of the products as the decision variables, then we end up with a nonconvex program. We give an equivalent market-share-based formulation, where the purchase probabilities of the products are the decision variables. We show that the market-share-based formulation is a convex program, the gradient of its objective function can be computed efficiently, and we can recover the optimal prices for the products by using the optimal purchase probabilities from the market-share-based formulation. Our results for both unconstrained and constrained problems hold for any arbitrary GEV model. The e-companion is available at https://doi.org/10.1287/opre.2018.1740 .

Optimization of Th-U fuel breeding based on a single-fluid double-zone thorium molten salt reactor

Molten salt reactor (MSR) shows great promise of high thermal-electric conversion efficiency, inherent safety, and on-line reprocessing. Furthermore, fuel breeding can be realized in both thermal and fast spectrum reactors with Th-U fuel cycle, which can make use of the abundant resource of thorium.In order to combine the advantages of high breeding ratio (BR) in fast spectrum and low fissile inventory for criticality in thermal spectrum, a single-fluid double-zone thorium-based molten salt reactor (SD-TMSR) core configuration is proposed. First, assemblies in both the inner and the outer zones are optimized by adjusting the ratios of molten salt and graphite with consideration of BR, 233U inventory, double time (DT), and temperature coefficient of reactivity (TCR) at the startup time, aiming to have a minimal DT for fuel breeding and a negative TCR for security. The results show that DT has the optimum value when the ratios of molten salt and graphite in the inner zone and the outer zone are 0.357 and 1.162, respectively. And the TCR can be improved to −2 pcm/K when the side length of the graphite hexagonal prism is 7.5 cm. Then, based on the optimized geometry, the burn-up calculations with a series of on-line reprocessing rates are carried out with a self-developed MSR reprocessing sequence (MSR-RS). The results show that on-line reprocessing is beneficial to the Th-U fuel breeding. When the reprocessing rate is 200 l/d, iso-breeding can be achieved. When the reprocessing rate is 5 m3/d, the performance of Th-U breeding is improved significantly, and only 16 years is needed for the 233U doubling. The results also show that TCR remains negative and inherent safety is satisfied during all the operation time.

A New Mathematical Model in Cell Formation Problem with Consideration of Inventory and Backorder: Genetic and Particle Swarm Optimization Algorithms

Cell Formation (CF) is the initial step in the configuration of cell assembling frameworks. This paper proposes a new mathematical model for the CF problem considering aspects of production planning, namely inventory, backorder, and subcontracting. In this paper, for the first time, backorder is considered in cell formation problem. The main objective is to minimize the total fixed and variable costs, including the machine related costs, intercellular movements, deviation between the levels of cell utilizations, inventory, backorder, and sub-contracting costs. The presented mathematical model is validated using GAMS software, and various test problems are solved by Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) algorithm. The performance of the algorithms is compared with the results obtained by the GAMS. The results demonstrate, there is no significant difference between the results of algorithms. Finally, some sensitive analyses are carried out to analyze the effects of backorder and inventory holding costs.