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Regulators can adjust penalties to compensate for incomplete monitoring of regulated parties that are subject to legal rules, but compensating penalty adjustments often are unavailable when regulated parties are subject to legal standards. Incomplete monitoring consequently invites greater noncompliance under standards than under rules. This chapter develops a model that quantifies some of the specific tradeoffs that regulators face in designing standards regimes under incomplete monitoring. The model also considers the extent to which suboptimal compliance due to incomplete monitoring is likely to result in deadweight loss in different settings.

The pandemic has caused severe disruptions and significant losses in various industries. In particular, the nursing service industry has been greatly affected, leading to increased service costs and attrition of nursing service provider (NSP) residents. Although prior studies suggest that outsourcing may mitigate losses from disruptions, there still lacks a detailed analysis of whether and when to adopt such a disruption solution.

This study develops a two-period game-theoretical model to explore the impacts of demand and cost disruptions caused by the pandemic on NSPs’ operational strategies, suppliers’ strategy choices and equilibrium prices and demand.

The results present several novel managerial insights. First, we suggest that higher demand and cost disruptions decrease service demand, but do not necessarily prompt an NSP to outsource nursing services. Interestingly, we find that even when the service cost of the outsourcing strategy is low, the NSP may still insist on the in-house strategy. Additionally, the equilibrium strategy does not always result in lower prices and higher demand.

Our findings provide insightful takeaways for NSPs to cope with the pandemic in the nursing service industry. The results also offer theoretical support for other industries to recover from demand and cost disruptions.

Low utilisation is observed in many buildings and space‐sharing is often identified as a facilities management response, but uncertainty about demand makes it difficult to decide how much shared accommodation to provide. The purpose of this paper is to analyse similar problems in the discipline of yield management, a branch of operations research.

The “newsvendor problem” in yield management is adapted and applied the to the space‐sharing problem. The mathematical model identifies the optimum capacity for specified values of input variables. The model is illustrated with worked examples for systematic variation in three factors: the average demand (three values), the penalty cost ratio (six values), and demand uncertainty (three values).

The optimum capacity for shared accommodation can be mathematically determined. It varies considerably with the case‐specific values given to input variables. Three “principles of optimality” are defined that apply to optimum capacity for a given demand, or alternatively to optimal loading for a given capacity.

The variation between different cases shows that optimal capacity must be assessed for specific contexts. The mathematical model makes simplifying assumptions that have not yet been tested in real‐world situations. A comparison between optimal and actual performance would reveal whether there are opportunities for significant enhancement in facilities management performance.

Applications of yield management ideas to the space sharing problem have not been found in the literature.

Inventory pledge financing (IPF) serves as an effective means to address the financial constraints faced by supply chains. This study develops an IPF system involving a bank, SMEs and a third-party logistics provider (3PL) to explore the impact of varying cost structures and regulatory environments, specifically the strategic interactions within IPF system before and after the blockchain implementation. Also, provides theoretical foundations for improving the overall efficiency of financing and advancing the application of blockchain technology.

An evolutionary game framework is employed to analyze the dynamics of financing behaviors before and after the blockchain implementation. Simulation methods are utilized to examine how different factors, including concealing costs, penalty structures and disposal prices, influence decision-making processes within IPF system.

Under IPF, the interactions of participants are shaped by asset management capabilities, reinvestment returns and penalties for fraud. As concealing costs increase, the likelihood of reaching a (loose regulation, compliant pledge) equilibrium rises. Post-blockchain implementation (IPFB), the equilibrium is influenced by default losses and compliance gains. Blockchain technology enhances regulation, effectively reducing fraud risks.

This study bridges significant gaps by offering a dynamic and behavioral perspective on IPF in the context of blockchain technology. Using an evolutionary game framework, the study uncovers how blockchain reshapes decision-making processes, mitigates fraud risks and enhances regulatory efficiency. By integrating cost structures and compliance incentives, it offers novel insights into behavioral shifts and systemic improvements in financing ecosystems.

The service sector comprises a dominant segment of the economy. Customer satisfaction, a measure of quality, is based on the degree of difference between expected quality and the actual level of quality experienced. Expected level of quality is influenced by customer perception of quality, which in turn is impacted by external and internal factors. In service industries, the interaction between the service provider and the customer may also influence quality. Thus quality may consist of tangible and intangible factors. In this chapter we consider the measurable attributes associated with quality in the service sector. Based on a specified guarantee level associated with the attribute, for example, service time, a penalty function is used to determine the impact of deviating from the guarantee level. With service time being a stochastic random variable, expected penalty costs to the service provider are found under a variety of conditions.

With the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience strategies.

The stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the decision-maker.

Firstly, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover conveniently.

The findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among distributors.

This study establishes blockchain as a pro-resilience technology. It advocates that organisations focus on investing in blockchain to enhance their visibility and recovery, as it effectively reduces absolute shortages and feelings of shortage unfairness while improving recovery and visibility.

To the best of the authors’ knowledge, this is a unique supply chain model study that integrates a technology such as blockchain directly as a binary variable in the model constraint equations while also focusing on resilience strategies, costs, risk aversion and shortage unfairness.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

The purpose of this paper is to explore different aggregate production planning (APP) strategies (inventory levelling, validation of the workforce and flexible production alternatives: overtime and/or outsourcing) by using a system dynamics model in a two-level, multi-product, multi-period manpower intensive supply chain (SC). Therefore, the appropriateness of using systems dynamics as a research method, by focusing on managerial applications, to analyse APP policies is proven. From the combination of systems dynamics and APP, recommendations and action strategies are considered for each scenario to understand how the system performs and to improve decision making on APP in the SC context.

The research design analyses a typical factory setting with representative parameter settings for five different conventional APP policies – inventory levelling, workforce variation, overtime, outsourcing and a combination of overtime and outsourcing – through deterministic systems dynamics-based simulation. In order to validate the simulation model, the results from published APP models were replicated. Then, optimisation is conducted for this deterministic setting to determine the performance of all these typical policies with optimal parameter settings. Next, a Monte Carlo stochastic simulation is used to assess the robustness of such performances in a variety of demand settings. Different aggregate plans are tested and the effect that events like demand variability and production times have on the SC performance results is analysed.

The results support the assertion that the greater the demand variability, the higher the flexibility costs (overtime, outsourcing, inventory levelling, and contracts and firings). As greater inter-month oscillations appear, which must be covered with additional alternatives, the optimum number of employees must be determined by analysing the interchanges and marginal costs between capacity oversizing costs (wages, idle time, storage) and the costs to undersize it (penalties for lowering safety stocks, delayed demand, greater use of overtime and outsourcing). Accordingly, controlling the times to avoid increased costs and penalties incurred by delayed demand becomes an essential important task, but one that also depends on the characteristics of this variability.

This paper has developed a modelling approach for APP in a manpower intensive SC by applying system dynamics. It includes a simulation model, the analysis of several scenarios, the impact on performance caused by variability events in the parameters, and some recommendations and action strategies to be subsequently applied. The modelling methodology proposed can be employed to design-specific models for each SC.

This paper proposes an APP system dynamics approach in a two-level, multi-product, multi-period manpower intensive SC for the first time. This model bridges the gap in the literature relating to simulation, specifically system dynamics and its application for APP. The paper also provides a qualitative description of the various pros and cons of each analysed policy and how they can be combined.

The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs).

The problem is a four‐parameter optimization with two parameters being k‐dimensional. The optimal solution is obtained by using a four‐stage approach where at each stage a one‐parameter optimization is solved.

Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF.

There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment.

Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.