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The purpose of this paper is to present a method for generating a set of feasible, optimized production schedules for the erection process of compact shipyards building complex ship types.

A bi-objective mathematical model is developed based on the process constraints. A Pareto front of possible erection schedules is created using a the Non-dominated Sorting Genetic Algorithm II with a custom heuristic fitness function and constraint violation.

It was possible to consistently generate a wide variety of production schedules with superior performance to those manually created by shipyard planner in negligible computational time.

The set of optimized production schedules generated by the developed methodology can be used as a starting point by existing shipyard planners when drafting the initial erection planning for a new project. This allows the planners to consider wider variety of options in less time.

No other published approach for the automatic generation of optimized production schedules of the erection process is specifically tailored to the construction of complex ships.

A scheduling method for determining the critical path in linear projects is presented, that takes into account maximum time and distance constraints in addition to the commonly used minimum time and distance constraints. The maximum constraints, though often present in the specifications of a project, are not considered during the planning procedure, since no method existed to enable scheduling with them. The proposed method builds on the concept of the maximum constraints and expands on the necessary background for their implementation into the schedule. The introduced critical path algorithm allows for grouping linear activities into four categories regarding their critical status and their ability to influence project duration. The method is applied to a low‐pressure pipeline construction project and the results are presented.

Given the increasing number of tourists exposed to commercially available cannabis, it is important to understand visitors' perceived constraints to cannabis consumption while travelling. This study aims to compare cannabis tourists' perceived constraints between first-time and repeat tourists to gain comprehensive understanding.

This study involved 32 conversational field interviews (5–10 min) with Chinese tourists who had smoked cannabis in coffee shops in Amsterdam to identify perceived constraints to engaging in cannabis tourism. The hierarchical constraint model (HCM) informed qualitative data coding using a deductive approach to compare first-time and repeat tourists. Content analysis was conducted manually.

Results showed that both tourist groups faced intrapersonal, interpersonal and structural perceived constraints to cannabis consumption. Differences in first-time and repeat cannabis tourists' perceived constraints were explained using the neutralization technique framework and psychological tactics, such as the defence of necessity, claims of entitlement, normal practice and claims of relative acceptability. In addition, social exchange theory was employed to explain why repeat cannabis tourists perceived fewer constraints than first-time tourists.

Although the current study did not intend to address the importance of cannabis tourism in places where cannabis is commercially available, findings offer empirical guidance for industry practitioners and policymakers regarding cannabis use education, prevention and policy in tourism contexts; related efforts can promote the sustainable development of this tourism market while protecting cannabis tourists' physical and psychological well-being.

This study makes important theoretical contributions regarding travel constraints in the unique context of cannabis tourism between first-time and repeat tourists. Findings will also enable academics, industry operators, policymakers and local residents of cannabis tourism destinations to better understand how these tourists decide whether to consume cannabis overseas. Differences in these perceived constraints between first-time and repeat tourists are discussed to highlight the dynamic nature of travel constraints.

The purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs).

The model contains a cost objective function, generalized precedence relationship constraints, activity duration and start time constraints, lag/lead time constraints, execution mode (EM) constraints, project duration constraints, working time unit assignment constraints and resource constraints. The mixed-integer nonlinear programming (MINLP) superstructure of discrete solutions covers time–cost–resource options related to various EMs for project activities as well as variants for production process implementation.

The proposed model provides the exact optimal output data for project management, such as network diagrams, Gantt charts, histograms and S-curves. In contrast to classic scheduling approaches, here the optimal project structure is obtained as a model-endogenous decision. The project planner is thus enabled to achieve optimization of the production process simultaneously with resource-constrained scheduling of activities in discrete time units and at a minimum total cost.

A set of application examples are addressed on an actual construction project to display the advantages of proposed model.

The unique value this paper contributes to the body of knowledge reflects through the proposed MINLP model, which is capable of performing the exact cost optimization of production process (where presence and number of activities including their mutual relations are dealt as feasible alternatives, meaning not as fixed parameters) simultaneously with the associated resource-constrained project scheduling, whereby that is achieved within a uniform procedure.

Buffers of work throughout a manufacturing facility enhance throughput. They protect a workstation against variations in processing times and against machine breakdowns of upstream workstations. However, buffer management is still thought to be an open problem: first there is no algebraic way of representing the relationship between buffer size and throughput, and second, the combinatorial nature inherent in the buffer design problem makes it difficult to develop an exact solution. These problems still exist today, as evidenced by the number of research papers that present sophisticated mathematics to solve this complex problem. Refutes all the above points. The buffer management method detailed does not use sophisticated mathematics impenetrable by the average production manager. Presents a heuristically‐based buffer management method effective at protecting throughput. The method will have advantageous effects on the size of buffers and the length of the production lead times, while still providing protection of the throughput rate.

This paper aims to minimize aircraft fuel consumption during the cruise phase when the flight is subjected to a specific time of arrival for different weights and distances.

The approach adopted herein uses sequential quadratic programming algorithm from MATLAB optimization toolbox, which includes a mathematical model of a jet airliner based on the Base of Aircraft Data as a function evaluator, to find out the impact of meet-time of arrival constraints on fuel consumption. The cruising speeds at predefined segments and the altitude are defined as the design variables.

The algorithm determines the optimum cruise altitudes and speeds for minimum fuel consumption in the case of no time constraints, also, for different time constraints where the flight time shall be reduced by increasing speed and lowering the altitude in most of the investigated cases.

The algorithm computes the optimum speed and the altitude according to different flight scenarios with the meet-time of arrival constraints for minimum fuel consumption which affects the direct operating cost of the flight. The algorithm might greatly help in decision-making for the meet-time of arrival operations.

Developing an algorithm to optimize the speed and the altitude of an aircraft based on weight and range for minimization of fuel consumption. It is a pioneer study in the literature that deals with the effect of meet-time constraints on fuel consumption.

Large-scale farm management in China has developed rapidly in recent years. Large-scale farmers face substantial operating risks, requiring extensive price risk management. However, the agricultural insurance and futures markets in China are incomplete. This study aims to analyze the price-risk-management behaviors of large-scale farmers under incomplete market conditions, with a focus on the interconnections between large scale farmers' subjective preferences (risk preferences, time preferences), liquidity constraints and their price risk management.

The authors construct an analysis framework to reveal the impact of large-scale farmers' risk preferences, time preferences and liquidity conditions on their price-risk-management behaviors under incomplete market conditions. Using data from field surveys and subjective preference experiments involving 409 large-scale grain farmers in China, an empirical analysis was conducted using the bivariate probit model.

The results show that risk-averse farmers will use risk transfer (such as contract farming) and risk diversification (such as multi-period sales) to avoid price risk. However, farmers subject to liquidity constraints and strong time preferences will not choose risk diversification, and the interaction between time preferences and liquidity constraints will strengthen this decision. The larger the farm-management scale, the greater the impact.

The authors focus on rapidly developed large-scale farm management in China. Appropriate price risk management is required by large-scale farmers due to their substantial operating risks. Considering the incomplete conditions of agricultural insurance and futures markets, the results of this study will help identify behavioral characteristics of large-scale farmers and optimize their price-risk-management strategies, further stabilizing large-scale farm management.

The purpose of this paper is to present a novel guidance law that is able to control the impact time while the seeker’s field of view (FOV) is constrained.

The new guidance law is derived from the framework of Lyapunov stability theory to ensure interception at the desired impact time. A time-varying guidance gain scheme is proposed based on the analysis of the convergence time of impact time error, where finite-time stability theory is used. The circular trajectory assumption is adopted for the derivation of accurate analytical estimation of time-to-go. The seeker’s FOV constraint, along with missile acceleration constraint, is considered during guidance law design, and a switching strategy to satisfy it is designed.

The proposed guidance law can drive missile to intercept stationary target at the desired impact time, as well as satisfies seeker’s FOV and missile acceleration constraints during engagement. Simulation results show that the proposed guidance law could provide robustness against different engagement scenarios and autopilot lag.

The presented guidance law lays a foundation for using cooperative strategies, such as simultaneous attack.

This paper presents further study on the impact time control problem considering the seeker’s FOV constraint, which conforms better to reality.

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.