As a key driver of innovation and entrepreneurship, venture capital firms play an important role in building innovation cities. Based on the listed companies in ChiNext market of Shenzhen Stock Exchange in China between 2009 and 2011, this paper examines the impact of venture capital firms’ co-investment behavior on investees’ fundraising capacity in IPO Markets. Empirical results show that co-investment behavior of venture capital firms indeed enhance the investee’s fundraising capacity, however, the number of venture capital firms introduced by it is not the more, the better, if the entrepreneur wants to raise more capital in IPO markets. The optimal choice for the company is to introduce four venture capital firms in order to raise larger amounts of capital in the IPO process. This paper also reveals the reason for the phenomenon of excess fundraising of IPO companies in the ChiNext market from the perspective of co-investment behavior of venture capital firms.
as open-pit mines consist in the accumulation of ore layers of different characteristics, their extraction process results from the iteration of a sequence of elementary operations performed on parcels by specialized or multipurpose machines. Medium-term extraction programming consists in deciding the assignment of the available machines over time. This problem is addressed through Discrete Event Simulation implemented in a phosphate mine, taking into account all the extraction constraints to drive the extraction process. This combines an effectiveness criterion (degree of urgency of obtaining certain ore qualities) and an efficiency criterion (machine displacements). While the parameterization used to control simulation quickly produces many technically feasible scenarios, making the final optimal choice is extremely difficult, particularly as one needs to take into account time considerations in extracted ore progressive provisioning and machine use. Our paper describes the bases of a visual decision-support system (DSS), currently under development, which enables the processing of results to compare multiple scenarios and adjust parameterization in order to come up with a good solution. This web-based DSS is coupled to the simulator via a relational database.
Capacitated location routing problem (CLRP) considers the plant location problem of tactical level and the vehicle routing problem of operational level simultaneously. Most existing works on CLRP investigate the deterministic problem settings, while it is usually hard to exactly estimate the customer demands due to some factors. This paper studies a sustainable CLRP with customers and suppliers matching under stochastic demands, where each vehicle starts from a plant and distribute product to customers and load material at a supplier then drive back to the original plant. The problem is to determine the candidate locations to open and the route of each vehicle, in order to minimize the weighted sum of construction cost and the expectation of fuel consumption. For the problem, a two-stage stochastic programming formulation is first proposed, and then a sample average approximation (SAA) method is developed. A case study shows the applicability of the proposed solution method.
We consider an online car-sharing problem between two locations with advance bookings. Each customer submits a pair of requests, where each request specifies the pick-up time and the pick-up location: one request from A to B , and the other request from B to A , not necessary in this order. The scheduler aims to maximize the number of satisfied customers, where the schedule has to decide whether or not to accept a request pair immediately at the time when the request pair is submitted. This problem is called OnlineTransfersForCommuting . We present lower bounds on the competitive ratio for this problem with both fixed booking times and variable booking times, and propose two algorithms, greedy algorithm and balanced greedy algorithm, that achieve the best possible competitive ratios.
With the popularity of electric vehicles (EVs), the charging problem becomes more and more serious due to the lack of sufficient public charging facilities in many big cities. Especially, for the electric taxis (ETs) on an e-hailing platform, the relatively long waiting and charging time, compared to fuel vehicles, has nontrivial effects on drivers' incomes and the whole service level of an e-hailing platform. Thus, it is significant to develop an efficient approach to arrange and coordinate the ETs' charging scheme. However, most previous studies focus on the coordination of personal EVs' charging demands or operations aiming to reduce the charging costs of independent drivers and the load of the city's power grid or maximize the profits of the charging stations. In this paper, we address the optimal charging problems of ETs to minimize the total out-of-service times of all ETs on an e-hailing platform. In particular, we formulate the problem as a mixed integer programming (MIP) model involving not only the optimal assignment between ETs and available charging piles but also the optimal charging sequence of ETs assigned to the same pile, based on the real-time charging demands of ETs and the statues of charging piles. The MIP model is efficiently solved by the art-of-state MIP resolver, Gurobi tool. A simulation experiment setting is established based on the real data from Didi e-hailing platform operating in Chengdu city, China. The computational results demonstrate that our MIP approach can observably reduce the total out-offtime of all ETs within acceptable computational time, comparing the existing approach in the literature, which apply first-comefirst-service rule to determine the charging sequences of ETs.
We develop a dynamic game model of a supply chain in which the manufacturer sells products both through direct and retail channels when there exists consumer returns. We investigate and analyze the effects of consumer returns on the equilibrium outcomes and channel strategy of supply chain members. It reveals that there does not always exist Pareto range about customer acceptance of direct channel that both the manufacturer and the retailer can be better from dual channel mode, because of consumer returns, if consumer visiting cost is zero; otherwise, under certain conditions, there exists dual channel mode with positive direct channel demand and the optimal wholesale price under dual channel mode is larger than that under single retail channel mode, and the manufacturer can be still better from charging high wholesale price, but the retailer is worse on the opposite.
It is known that the workers' fatigue can greatly affect both industrial performances and the workers' wellbeing at work. Consequently, when designing a manufacturing system, managers are interested in facility layouts that favor performance objectives, while avoiding excessive fatigue. Unfortunately, most existing studies related to layout design focus on technical aspects of the considered system (e.g. flow costs, distance between machines, etc.) so that human factors, in particular fatigue, are insufficiently taken into consideration. Therefore, we are interested in how the workers' fatigue can be taken into account when evaluating possible layout designs. We analyze the factors that induce fatigue, which are mainly concerned with the work arduousness, and depend on the layout. We explain how they can be considered in order to compare possible solutions of a layout problem. In such a context, emphasis is put on the role of simulation. We illustrate our purpose and highlight the importance of taking fatigue into consideration through a comparison, using simulation, of two different layouts of a job-shop system. The comparison is based both on the mean flowtime of jobs and how the workers' fatigue evolves over time.
The assembly line worker assignment and balancing problem (ALWABP) is a hot and interesting topic. In an assembly line, persons with disabilities can be employed and work better due to the specialized work. In this paper, we investigate the ALWABP with heterogeneous workforce considering stochastic worker availability. The problem has been addressed in the previous literature. However, the workers are sometimes insufficient to meet all stations due to high level of workforce absenteeism and this situation has not been fully considered in the literature. Therefore, we propose an improved two-stage stochastic programming model allowing inadequate workers to minimize the weighted sum of cycle time and penalty cost caused by insufficient workers. The first stage is to assign fixed tasks to stations, and the assignment of flexible tasks and workers is regarded as the second-stage decision. Sample average approximation (SAA) is applied to solve the model within feasible time. Finally, we conduct computational experiments to test the model compared to the model with the assumption that the number of workers is adequate to meet the number of stations (AS model). We test two instances including 8 and 10 tasks respectively and different numbers of scenarios are also taken into account to demonstrate the improvement of our model.
Humanitarian organisations need to be efficient and effective and to do so, rely on their supply chain and logistics efforts. However, the urgency context, the destabilised environment as well as large uncertainties often affect the performance of the relief. Symptoms such as redundancies or operation lack, mismanagement and high costs call the methods of humanitarian organisations into question. This research work examines the potentiality that the evolution directly to a hyperconnected world could benefit to humanitarian organisations. To this end, Physical Internet a new concept of global logistics networks based on the hyperconnection notion has been studied. Designed to address most of the stakes of tomorrow multiplication of flows, environmental emergency, performance and speed pressures it might present various interests for the humanitarian sector. In a first approach, some Physical Internet principles have been projected on typical humanitarian logistics operations related to transportation, storage allocation and inventory management. It appears interests in terms of savings, efficiency, sustainability and responsiveness. However, such reengineering comes with a cost and would require significant change such as a large reshape of the current logistics model and legal framework as well as further reflections regarding information management and technology development.
In a complex project, an organization is often not able to manage all aspects alone, since it does not have all the required competences, skills or resources. In this case, alliance formation can be a solution for project development. Apart from simply managing complex projects, firms also find it important to increase innovativeness by sharing knowledge between partners in alliances. However, in alliances one of the difficulties is achieving effective collaboration: mis-communication, missing skills or missing resources create a high risk that the project fails to achieve its goals. In order to decrease the risk of failure, and to overcome potential collaboration inefficiency, partner selection takes place among firms that are able to communicate well while at the same time having the required knowledge to achieve their objectives. The important role of partner selection in alliances justifies the increased attention given to substantial criteria in alliance formation. Proposing a knowledge-based framework aimed at increasing the understanding of partner selection in alliances is the contribution of this paper. This knowledge can be gained by evaluating projects from a technological point of view to estimate their challenging degrees, and studying the partners background in past projects or partnerships. This paper structured to propose hypotheses based on a systematic literature review. At the heart of the hypotheses is a consideration of the needs of the project, and starting there allows us to characterize alliance formation and partner selection using a new typology. Finally, a novel framework is proposed that could help decision-makers in the managerial aspects of partner selection in alliance formation. The framework also presents considerable potential for future studies.
BIM interoperability has been recognized as a strong brake to BIM collaboration and is a very active research field. However, no application takes the challenge of easing the collaboration processes itself yet. This paper suggests a framework to smooth the collaboration: a BIM data management tool that allows distinct project teams to co-manipulate BIM shared data outside the borders of files, formats and software tools.
The paper presents a preliminary version of a decision-making tool for tactical planning in a Make-To-Order (MTO) multi-product, multiple-assembly line SME (Small and Medium-Sized Enterprises) environment with heterogeneous workers. A single objective mixed-integer mathematical model is proposed to identify quantities of finished products to be produced in different planning horizons while respecting capacity availability. The main objective of the model is to minimize the tardiness in delivery of customer orders. The proposed model was tested with nine instances. The computational results prove the capability of the proposed model to reduce delay on deliveries.
Dual resource constrained job shop problem (DRCJSP) is an extension of the job shop scheduling problem such that each job has two available processing resources like machines and workers, each of which is of a non-identical number of operations. In many real manufacturing systems, machines often process in different processing speeds for some special demands. Note that the processing time of each job not only depends on the assigned worker but also on deployed machine. This undoubtedly leads to non-deterministic job processing time, thus resulting in different electricity consumption. We investigate the bi-objective DRCJSP problem with multi-processing speed for minimizing makespan and total electricity consumption in this work. We establish a bi-objective integer programming model and devise an epsilon constraint algorithm together with a non-dominated sorting genetic algorithm II (NSGA-II). The epsilon constraint method produces exact solutions for small job instances while NSGA-II can efficiently solve large job instances. Numerical experiments validate the proposed model and algorithms.
Research has indicated that consumers who possess a purchase intention have a greater exploratory buying behavior tendency. However, the background introduction of new technology products such as wearable devices has not received sufficient research attention. Accordingly, the present study proposes that there is a positive relationship between purchase intention and exploratory buying behavior tendency, using sport involvement as a moderator. A total of 302 valid questionnaires were collected in Taiwan with an effective recovery rate of 60.4%. We found that there was a significant positive correlation among exploratory buying behavior tendency, exploratory acquisition of products, exploratory information seeking, sport involvement and purchase intention. Moreover, purchase intention significantly predicted the change in exploratory buying behavior tendency. In addition, purchase intention and sport involvement have a significant interaction effect in predicting the change in exploratory buying behavior tendency. Specifically, the positive relationship between purchase intention and the change in exploratory buying behavior tendency increased especially for consumers who felt lower sport involvement. Implications and applications of the study findings are discussed.
We propose a data structure called the “cooling box” which can be used to efficiently process the energetic reasoning [1], [4] for the parallel machine scheduling problem and the Cumulative Scheduling Problem (CuSP) [2]. In this context, each task to schedule can be represented by an object whose resource demands vary according to the time. To efficiently process some adjustments of the release dates of the tasks, it is useful to identify the highest-priority task, i.e. the one using the highest resource quantity at a given time. The aim of the proposed data structure is to obtain a time complexity of O(n 2 ln C) [3] for these adjustments instead of O(n 2 C) [1], where C is the capacity of the cumulative resource and n is the number of tasks.
Aviation or air transportation refers to the activities surrounding mechanical flights in the airlines and the aircraft industries. In this paper, we present a recent literature survey on aviation management. The literature review is classified into the following main categories: Airline Capacity Analysis; Air Traffic Flow Management; Airline Fleet Assignment; Tail Assignment with Aircraft Maintenance Routing; Airline Crew Pairing; Airline Recovery and Rescheduling; Airline Revenue Management; Collaborative Decision Making; Aircraft Scheduling. This classification aims to motivate the researchers and practitioners in aviation management to develop more applicable, realistic and wide-ranging optimization methodologies for meeting the current needs of aviation industry.
Nowadays, service-oriented manufacturing systems (e.g., cloud manufacturing, product service systems, etc.) have attracted more and more interesting and attention of researchers from many different fields. However, because of the complex and dynamic environment, one of the most important issues that need to be addressed for the promotion and application of cloud manufacturing system is the dynamic supply-demand matching of manufacturing resource services. In this paper, the strategy problems of matching efforts are investigated for a supply chain with resource sharing, where the considered supply chain under cost sharing contract consists of two independent and competing manufacturers and a resource service platform. Firstly, we use a differential equation to model the evolution of manufacturing resources’ sharing level and depict the effect of the matching efforts on market demand. By applying the two-stage differential game, the optimal matching strategies are obtained based on the presented optimal control model. Subsequently, the cost sharing contract is designed to coordinate and improve the performance of the supply chain. Finally, a numerical example is provided to illustrate the impacts of the platform transaction fee and the purchasing cost on the feasible region of the corresponding contract.
We consider a fixed charge capacitated multimodal transportation problem which has wide application but not been extensively studied in the literature. The problem aims at determining the route for each demand, the transportation mode on the route and the number of containers for each transportation mode, such that all transportation requests are satisfied with the minimum logistic costs. We develop a column generation framework to provide a lower bound. In order to evaluate the quality of the lower bound generated, we compare the lower bound obtained with optimal objective function value. The average gap is 19.6%.
The parking problem has become one of the major issues in urban transportation planning and traffic management research. The present paper deals with the dynamic assignment problem of the parking slots (places). The objectives are to provide a global satisfaction of all customers and maximize parking occupancy. A dynamic assignment problem consists in solving a sequence of assignment problems over time. To cope with all these aspects, we offer in this paper, a new approach based on a learning strategy: an Estimation of Distribution Algorithm (EDA) where a reinforcement learning method is used to support the assignment algorithm. We tested our approach with simulations for over 120 days, using a set of up to 10 parking lots (i.e. with up to 7,000 parking slots) and 13,000 requests distributed with different patterns over a day. The comparative study between an assignment algorithm with and without reinforcement learning algorithm has proven the relevance of our approach: the saving is up to 80%. The results also showed the effects and the benefits of the learning strategy.
We consider multitasking scheduling on a single machine involving two competing agents, in which the due date of each job of the first agent is a decision variable, which is to be determined by the decision maker using unrestricted due date method, whereas the due date of the jobs in the second agent is exogenously given. By multitasking, we mean that the processing of a selected job on the machine may be interrupted by other jobs that are available but unfinished. The first agent's objective value is to minimize an integrated scheduling criterion including the due date assignment cost and the weighted number of its tardy jobs, while the second agent's objective value is to minimize the maximum value of a regular scheduling function of its jobs. The overall objective is to minimize the objective value of the first agent, subject to the objective value of the second agent not exceeding a given threshold. For each of the problems considered, we show that it is NP-hard in the ordinary sense and admits pseudo-polynomial time algorithm.
