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The importance of service firms cooperating with digital platforms is widely acknowledged. The authors study three contractual relationships (fixed-cost, cost-sharing, and profit-sharing) between service firms (specifically hotels) and digital platforms in a highly fragmented service supply chain to examine which of these contract types optimizes profits.

The authors extend prior models analyzing the optimal expected total profit from the travel service firm (hotel)–digital platform relationship, providing new insights into each contract type’s ability to coordinate decentralized systems and optimize profits for both parties.

This study finds that fixed cost contracts cannot coordinate the decentralized system. Cost-sharing contracts can coordinate the decentralized system but only allow one channel profit split. In contrast, profit-sharing contracts may not always perfectly coordinate the decentralized system but support alternative profit allocations. Practically, both profit-sharing and cost-sharing contracts are preferable to fixed-cost contracts.

The paper includes implications for travel service firm managers to consider when structuring contracts with digital platforms to focus on profit optimization. Profit-sharing contracts are most preferable when cost and revenue data are fully shared between parties, while cost-sharing contracts are preferable over fixed-cost contracts.

This study extends prior investigations into the utility of different contract types on the optimal profit of a travel service firm (hotel)-digital platform provider relationship. The research fills a gap in the literature concerning the contracts used in these relationship types.

Fear grips stock markets when a pandemic like COVID-19 strikes, severely affecting stock prices. However, fundamental value drivers of companies do not change drastically during pandemics. The sensitivity of firms' cash flows to lockdowns during pandemics depends on their cost structure. This paper develops a financial model incorporating information about value drivers and lockdown sensitivity of companies to find the enterprise value.

The authors develop a financial model that estimates the effects of COVID-19 on enterprise value and helps to identify wrongly valued stocks. The authors apply the model to five Indian stocks from five different industries to study how firms belonging to various sectors get affected differently in this pandemic.

Companies belonging to civil aviation and retail sectors get more affected by COVID-19 compared to those in movie exhibition, automobile and hotel industries. The cost structure of the latter category of firms reduces their cash flow effect.

The model can be used by practitioners to understand any pandemic's effect on stock prices. Also, it explains how firms having different cost structures get affected by any crisis and help investors in taking appropriate buy/sell decisions.

The study has two contributions: first, the authors develop a financial model to estimate the effect of COVID-19 on the enterprise value. Second, contrary to popular perception, the authors find companies belonging to movie exhibition, hotel and automobile industries do not get that severely affected.

The COVID-19 pandemic has forced countries to consider how to reach vulnerable communities with extended outreach services to improve vaccination uptake. The authors created an optimization model to align with decision-makers' objective to maximize immunization coverage within constrained budgets and deploy resources considering empirical data and endogenous demand.

A mixed integer program (MIP) determines the location of outreach sites and the resource deployment across health centers and outreach sites. The authors validated the model and evaluated the approach in consultation with UNICEF using a case study from The Gambia.

Results in The Gambia showed that by opening new outreach sites and optimizing resource allocation and scheduling, the Ministry of Health could increase immunization coverage from 91.0 to 97.1% under the same budget. Case study solutions informed managerial insights to drive gains in vaccine coverage even without the application of sophisticated tools.

The research extended resource constrained LMIC vaccine distribution modeling literature in two ways: first, endogenous calculation of demand as a function of distance to health facility location enabled the effective design of the vaccine network around convenience to the community and second, the model's resource bundle concept more accurately and flexibly represented complex requirements and costs for specific resources, which facilitated buy-in from stakeholders responsible for managing health budgets. The paper also demonstrated how to leverage empirical research and spatial analysis of publicly available demographic and geographic data to effectively represent important contextual factors.

The construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.

This paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.

This paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.

This study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?

Several collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.

This study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

The earth is facing challenges to work for the survival of human life during domino effect disasters. The emergency resource storage locations should be selected considering the probability of domino effect disasters. The first purpose of this study is to select the storage locations where domino effect probability is less. And second, facility development cost and transportation costs and costs for unutilized capacity have been optimized.

The work is a multiobjective optimization problem and solved with weighted sum approach. At first, the probabilities of domino effect due to natural disasters are calculated based on the earthquake zones. Then with that result along with other necessary data, the location to set up storage facilities and the quantities of resources that need to be transported has been determined.

The work targeted a country, Bangladesh for example. The authors have noticed that Bangladesh is currently storing relief items at warehouse which is under the domino effect prone region. The authors are proposing to avoid this location and identified the optimized cost for setting up the facilities. In this work, the authors pointed out which location has high probability of domino effect and after avoiding this location whether cost can be optimized, and the result demonstrated that this decision can be economical.

Disaster response authorities should try to take necessary proactive steps during cascading disasters. The novelty of this work is determining the locations to select storage facilities if the authors consider the probability of the domino effect. Then a facility location optimization model has been developed to minimize the costs. This paper can support policymakers to assess the strategies for selecting the location of emergency resource facilities.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

As far as the authors know, no research has already been carried out on the multi-floor dynamic facility layout problem (MF-DFLP) in the continuous form regarding the flexible bay structure, the number and the variable location of the elevator. Therefore, the present paper models the given problem and attempts to find a sub-optimal solution for it using a meta-heuristic simulated annealing (SA) algorithm.

The efficient use of resources has always been a prominent matter for decision-makers. Many reasons including land use, construction considerations and proximity of departments have led to the design of multi-floor facilities. On the other hand, their fast-evolving environment calls for dynamic planning. Therefore, in this paper, a model and the SA algorithm for MF-DFLP are presented.

After presenting a mathematical model, the problem was solved precisely in a small size using the GAMS software. Also, a near-optimal solution method using a SA meta-heuristic algorithm is suggested and the proposed algorithm was run in the MATLAB software. To evaluate the presented model and the proposed solution, some test cases were considered in two aspects. The first aspect was the test cases that are newly generated in small, medium and large sizes to compare the exact optimal solution with the results of the meta-heuristic algorithm. Eight test cases with small sizes were solved using the GAMS software, the optimum solutions were obtained in a reasonable time, and the cost of their solutions was equal to that of the SA algorithm. Eight test cases with medium sizes were run in the GAMS software with the time limit of 80,000 s, and the SA algorithm had performed better for these test cases. Two test cases were also considered in large size that GAMS could not solve them, whereas the SA algorithm successfully found a proper solution for each. The second aspect included the test cases from the literature. The result showed that suggested algorithm is more capable of finding best solutions than compared algorithms.

In this paper, an unequal area MF-DFLP was studied in a continuous layout form in which the location and number of the elevators were considered to be variable, and the layouts were considered with flexible bay structure. These conditions were investigated for the first time.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.