An integrated system for capacity planning and facility layout

In order to achieve efficient facility design for service type activities, operating under dynamic conditions and a large number of constraints, the use of a traditional approach has proved to be tedious and time consuming. Development of an efficient decision support system for such a situation calls for the consideration of the complex nature of interaction between the system parameters and the relationship between the working environment and the resources within the system. Mathematical programming techniques, e.g. linear and integer programming as well as queuing models, though useful in handling combinatorial optimization problems, are incapable of dealing with stochastic utilization problems normally encountered in the design of facilities of a fast changing environment. This paper makes use of a pattern search algorithm for the optimal allocation of service facility resources. The layout of the facilities has then been optimized by the use of the CLASS algorithm. The two separate algorithms have suitably been integrated together into a single simulation-based system. The effectiveness of the proposed methodology has been demonstrated by means of a real case study pertaining to design and layout optimization of a multi-functional gasoline service station in Bangkok.     

Integrating MRP and finite capacity planning

Traditional production control systems based on the manufacturing resource planning concept do not sufficiently support the planner in solving capacity problems, ignore capacity constraints and assume that lead times are fixed. This leads to problems on the shop floor, that cannot be resolved in the short term. This paper focuses on solving these capacity problems by improving capacity planning at the material requirements planning MRP level through integration of MRP and finite capacity planning. This results in a planning method for simultaneous capacity and material planning. The planning method is based on a new and more accurate primary process model, giving the planning algorithm more flexibility in solving capacity problems. The algorithm is based on advanced scheduling techniques and uses aggregated information, thus combining speed and accuracy. The algorithm is designed to use the available flexibility: alternative routeings, safety stock, and replanning of production orders and requirements. This paper also discusses such related issues as robustness, memory and the role of the human planner.     

Maximizing customer equity subject to capacity constraints

This paper considers a firm that wants to optimally allocate limited capacity to heterogeneous customer segments in order to maximize its customer equity. The decision whether to accept or to reject a customer׳s request in a current period influences his repurchase behavior in later periods. The allocation process becomes complex, when demand exceeds capacity, because the isolated determination and optimization of a single customer׳s lifetime value is no longer feasible. Using a Markov decision process formulation, we study how to trade off short-term attainable revenues and long-term customer relationships. Furthermore, we analyze when and how intertemporal customer behavior influences capacity allocation. Finally, we investigate the impact of limited capacity on the customer lifetime value by introducing an opportunity cost-based approach that understands customer profitability as a customer׳s contribution to customer equity.     

A mixed inventory model with variable lead time and random supplier capacity

In this paper, we have studied analytically the implication of a controllable lead-time and a random supplier capacity on the continuous review inventory policy, in which the order quantity, reorder point and lead-time are decision variables. Two models are considered: the normal lead-time demand and lead-time demand is distributed free. For both cases, after formulating the general model, some properties of the optimal ordering policy have been developed. Particularly, we have shown that the expected annual total cost is a unimodal function and quasi-convex in the order quantity. When the variable capacity distribution is exponential, we develop effective procedures for finding the optimal solutions. Furthermore, the effects of parameters are also performed.     

Modelling and analysis of dynamic capacity complexity in multi-stage production

The uncertainty associated with managing dynamic capacity problem is the main source of its complexity. This article presents a system dynamics approach to model and analyse operational complexity of dynamic capacity in multi-stage production. The unique feature of this approach is that it captures the stochastic nature of three main sources of complexity associated with dynamic capacity. These are the demand, internal manufacturing delay and capacity scalability delay. The developed model was demonstrated by an industrial case study of multi-stage printed circuit board assembly line. The analysis of simulation experiments showed that ignoring complexity sources can lead to wrong decisions concerning both scaling levels and backlog management decisions. In addition, a general trade-off between the controllability and complexity of the dynamic capacity was illustrated. Finally, comparative analysis of the effect of each of these sources on the complexity level revealed that internal delay has the highest impact on dynamic capacity efficiency. Guidelines and recommendations for better capacity management and reduction of its complexity are presented.     

Static and Dynamic Pricing of Excess Capacity in a Make‐to‐Order Environment

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.     

The effect of capacity management strategies on employees' well-being: A quantitative investigation into the long-term healthcare industry

The aim of this study is to understand the effects of different capacity management strategies on the well-being of employees in long-term healthcare organizations. Such strategies may produce psychological effects in terms of job satisfaction and well-being among employees, namely frontline employees, thus affecting service quality. We collected 2158 observations from 42 nursing homes in Italy. Our results show that all capacity management strategies addressed in this study can influence the perceived degree of fatigue or of job hazard, and some of them can influence both. Moreover, a better perception of job hazard and fatigue leads to a higher degree of reported well-being from employees, although with the former, it is only through the mediation of job satisfaction. We conclude our paper by discussing theoretical contributions and policy implications.     

Cyclicality of capital-intensive industries: a system dynamics simulation study of the paper industry

This paper provides a view on the cyclicality of capital-intensive industries that could add considerably to our understanding of how cycles in prices, profits and capacity come about. Previous studies of business cycles focus on macro-economic systems or on the agricultural sector. Causes for fluctuations are typically believed to be mainly exogenous in nature. We seek to extend the existing literature on industrial cycles by developing a model that incorporates endogenously generated cyclicality. A simulation model of the paper industry is developed, and validated on the basis of data for the US paper industry.     

Service Systems with Experience‐Based Anecdotal Reasoning Customers

The existing queueing literature typically assumes that customers either perfectly know the expected waiting time or are able to form rational expectations about it. In contrast, in this article, we study canonical service models where customers do not have such full information or capability. We assume that customers lack full capability or ample opportunities to perfectly infer the service rate or estimate the expected waiting time, and thus can only rely on past experiences and anecdotal reasoning to make their joining decisions. We fully characterize the steady‐state equilibrium in this service system. Compared with the fully rational benchmark, we find that customers with anecdotal reasoning are less price‐sensitive. Consequently, with a higher market potential (higher arrival rate), a revenue‐maximizing firm may increase the price if the service rate is exogenous, and it may decrease the price if the service rate is at the firm's discretion. Both results go against the commonly accepted pricing recommendations in the fully rational benchmark. We also show that revenue maximization and welfare maximization lead to fundamentally different pricing strategies with anecdotal reasoning, whereas they are equivalent in the fully rational benchmark.     

Dynamic Pricing with Constant Demand Elasticity

The celebrated model of Gallego and van Ryzin is specialized to the case of constant elasticity of demand. A closed form is developed, which has an even simpler form than that arising with exponential demand and which possesses an excellent approximation. In this environment, monopoly is efficient, which means that all the behavior usually attributed to monopoly pricing is actually a consequence of efficient pricing and would arise even in a perfectly competitive environment. If the initial supply is not too large, consumers have no incentive to delay their purchases to get a lower price at the average inventory prevailing at any time.     

Scrutinising the interplay between governance and resilience in supply chain management: A systems thinking framework

Supply chain disruptions recurrently challenge end-to-end operations owing to the ambiguous understanding of the role of governance in impacting supply network resilience. This paper scrutinises the relevant literature to understand the plethora of interpretations in supply chain governance and resilience while further providing a new perspective on the representation of the interplay between governance and resilience in supply chains. In this regard, the Systems Thinking lens is adopted to pull together the typologies and constructs of supply chain governance and resilience from the literature. Methodologically, System Dynamics modelling principles are leveraged to capture the underpinning structural interdependencies in a causal loop diagram. The study reveals that endogenous and exogenous supply chain governance processes and mechanisms support the intrinsic and extrinsic resilience in networks. Overall, this research contributes to the supply chain risk management domain by synthesising the interplay between governance and resilience, identifying pertinent typologies, and articulating research propositions that can inform decision-making at policy and management levels.     

Advance selling with freebies and limited production capacity

We consider a two-period pricing model in which a seller offers freebies along with the product when making advance sales, and production is constrained by capacity. The seller can offer freebies to increase both market base and customer׳s valuation toward the product in advance. The customers strategically determine whether to purchase the product in advance and gain freebies when their valuation on the product is uncertain, or delay their purchase decision until the regular selling period. We characterize the optimal pricing, quality level of the freebie and production quantity decisions that maximize the expected profits of the seller over the two periods.     

Ordering behavior in a supply chain with customers that respond to changes in service level

The presence of service-level-dependent demand has been empirically observed in industry and is well-documented in the literature. How does the implicit contractual existence of such service dependency impact the ordering decisions of upstream suppliers? We conduct three controlled laboratory experiments to study the impact a service-reward mechanism may have on the upstream ordering decisions of those charged with inventory ordering decisions. The multistudy approach provides representations of decision dynamics across a variety of scenarios, from one-shot buys to long-term supply-chain relationships. Our combined empirical results consistently suggest that the service-reward mechanism significantly and systematically elevates order levels and order variability in a manner that increases departure from optimal ordering. This effect is observed even when decision-makers have incentives to maintain a steady ordering pattern with their suppliers. Our findings shed much-needed light on individual ordering responses to, and the associated risks of, service-reward mechanisms.     

Supply Performance in Multi‐Echelon Inventory Systems with Intermediate Product Demand: A Perspective on Allocation

In this article, we study the performance of multi‐echelon inventory systems with intermediate, external product demand in one or more upper echelons. This type of problem is of general interest in inventory theory and of particular importance in supply chain systems with both end‐product demand and spare parts (subassemblies) demand. The multi‐echelon inventory system considered here is a combination of assembly and serial stages with direct demand from more than one node. The aspect of multiple sources of demands leads to interesting inventory allocation problems. The demand and capacity at each node are considered stochastic in nature. A fixed supply and manufacturing lead time is used between the stages. We develop mathematical models for these multi‐echelon systems, which describe the inventory dynamics and allow simulation of the system. A simulation‐based inventory optimization approach is developed to search for the best base‐stock levels for these systems. The gradient estimation technique of perturbation analysis is used to derive sample‐path estimators. We consider four allocation schemes: lexicographic with priority to intermediate demand, lexiographic with priority to downstream demand, predetermined proportional allocation, and proportional allocation. Based on the numerical results we find that no single allocation policy is appropriate under all conditions. Depending on the combinations of variability and utilization we identify conditions under which use of certain allocation polices across the supply chain result in lower costs. Further, we determine how selection of an inappropriate allocation policy in the presence of scarce on‐hand inventory could result in downstream nodes facing acute shortages. Consequently we provide insight on why good allocation policies work well under differing sets of operating conditions.     

有限供应的现货市场与期权合约下的采购策略

在随机现货价格与随机需求相独立的情况下,当现货市场供应量有限时,本文采用期权组合合约建立两阶段采购风险管理模型,以期最大化零售商的期望利润.文中提供了甄别有效合约的算法,得到零售商的最优采购策略,并进一步用算例分析了现货市场的供应量、现货价格和客户需求的波动性对最优采购策略的影响,发现当现货市场的供应量增加时,零售商应...     

基于需求更新的民航收入管理动态存量控制模型

本文对民航收入管理存量控制研究中具有里程碑意义的EMSR(Expected Marginal Seat Revenue)模型进行了评述,分析其存在的缺陷,提出将销售过程中获取的最新销售信息与需求的历史先验分布相结合,运用二维正态分布下的贝叶斯模型对需求分布进行更新,并将综合考虑新的需求预测、No-Show和取消订票等因素得到的新的需求限制与座位总数C相比较,给出更为通用的、市场反应更为灵敏的民航收入管理动态存量控制模型。     

An investigation into the vehicle routing problem with time windows and link capacity constraints

In this work, we investigate a new, yet practical, variant of the vehicle routing problem called the vehicle routing problem with time windows and link capacity constraints (VRPTWLC). The problem considers new constraints imposed on road links with regard to vehicle passing tonnage, which is motivated by a business project with a Hong Kong transportation company that transports hazardous materials (hazmats) across the city and between Hong Kong and mainland China. In order to solve this computationally challenging problem, we develop a tabu search heuristic with an adaptive penalty mechanism (TSAP) to help manage the company's vehicle fleet. A new data set and its generation scheme are also presented to help validate our solutions. Extensive computational experiments are conducted, showing the effectiveness of the proposed solution approach.     

基于需求转移的易逝性产品最优动态定价策略

本文针对易逝性产品中新产品对老产品需求的转移作用,应用收入管理方法得出老产品的最优动态定价策略,并应用最大凹向包络理论给出了简化算法的方法.数值算例表明在存在需求转移的情况下,应更早的提供较低的价格.最后作者给出了包含生产和定价的综合模型.     

Dynamic Pricing,Production, and Channel Coordination with Stochastic Learning

We consider a decentralized two‐period supply chain in which a manufacturer produces a product with benefits of cost learning, and sells it through a retailer facing a price‐dependent demand. The manufacturer's second‐period production cost declines linearly in the first‐period production, but with a random learning rate. The manufacturer may or may not have the inventory carryover option. We formulate the resulting problems as two‐period Stackelberg games and obtain their feedback equilibrium solutions explicitly. We then examine the impact of mean learning rate and learning rate variability on the pricing strategies of the channel members, on the manufacturer's production decisions, and on the retailer's procurement decisions. We show that as the mean learning rate or the learning rate variability increases, the traditional double marginalization problem becomes more severe, leading to greater efficiency loss in the channel. We obtain revenue sharing contracts that can coordinate the dynamic supply chain. In particular, when the manufacturer may hold inventory, we identify two major drivers for inventory carryover: market growth and learning rate variability. Finally, we demonstrate the robustness of our results by examining a model in which cost learning takes place continuously.     

竞争环境下基于顾客策略行为的易逝品动态定价研究

在两个提供相同易逝品的零售商折线竞争下,从顾客策略行为出发,研究竞争环境下两零售商的动态定价策略。首先建立供大于求的情况下两零售商动态定价模型,给出了均衡价格满足的条件,并探讨了在一些特殊情况下均衡价格的特性。然后将这一模型扩展到供小于求的情形,探讨模型的求解方法。算例分析了在折线竞争模式下零售商在面对顾客策略行为时,如何动态的决定价格。同时发现,在供大于求的情况下,顾客策略行为导致零售商的收益降低;在供小于求的情况下,一定程度的顾客策略行为可以使零售商获得更高的期望收益。