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This article aims to explore whether and how rural entrepreneurs (REs) navigate the challenges they face to support rural development, with a particular focus on sustainable entrepreneurial development in Jordan.

Drawing on structuration theory (Giddens, 1984), the article is underpinned by the position-practice perspective and its four interrelated elements: praxis, positioning, capabilities and trust. A qualitative methodology is applied to explore the case study of Deserttulip in Jordan and its utilisation of the innovative agricultural Groasis Waterboxx-Technology (GroasisWBT).

Findings indicate that the position-practice of rural entrepreneurs becomes contextualised between enablers and constraints that shape entrepreneurs' praxis, positioning, capabilities and trust. Pervasive constraints are evidenced by limiting external structures and resources. Yet, these are challenged by enablers observed through the agentic roles of REs and the utilisation of innovative tools in ways that minimise the limitations of constrained contexts.

The article has practical implications within countries with extreme constraints – including environmental (drought), financial and institutional constraints – where collective sustainable initiatives, alongside the utilisation of innovative tools, are called for in order to minimise the impact of limited resources and institutional support whilst promoting empowerment, cohesion and sustainable entrepreneurial development.

The article highlights the specificity of constrained entrepreneurship in ways that allow re-interpreting position-practice of rural entrepreneurs around collective social systems rather than individual entrepreneurs. A sustainable ripple is conceptualised, whereby expanding sustainable entrepreneurial development initiatives are observed around collective acts that stress the accountability and mutual dispositions of rural entrepreneurs towards developing and sustaining their contexts.

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.

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).

A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.

Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.

It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.

An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

A survey of project scheduling problems since 1973 limited to work done specifically in the project scheduling area (although several techniques developed for assembly line balancing and job‐shop scheduling can be applicable to project scheduling): the survey includes the work done on fundamental problems such as the resource‐constrained project scheduling problem (RCPSP); time/cost trade‐off problem (TCTP); and payment scheduling problem (PSP). Also discusses some recent research that integrates RCPSP with either TCTP or PSP, and PSP with TCTP. In spite of their practical relevance, very little work has been done on these combined problems to date. The future of the project scheduling literature appears to be developing in the direction of combining the fundamental problems and developing efficient exact and heuristic methods for the resulting problems.

Effective management of resources in a dynamic, multiproject environment requires consideration of two key issues: the availability of each of the multiple resources and the method of scheduling these resources to complete activities and, subsequently, projects. Identifies the trade‐offs between performance and the availability of multiple resources, when some resources are more costly than others. Finds that there are significant effects when the level of either the costly resources or the cheaper resources are varied, that trade‐offs can be made by reducing the availability of the costly resources and increasing the availability of the cheaper resources and that the improvement in completion time performance is reasonably linear over the tested ranges and the rates of improvement differ over the ranges. Describes the resource allocation factors and treatments as well as the scheduling heuristics. Uses a finite scheduling algorithm along with the prioritization heuristics to schedule the constrained multiple resources simultaneously and a simulation to replicate the environment. Develops linear regressions to provide further insight.

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

Considers the resource‐constrained project scheduling problem where cash inflows and outflows are tied to the occurrence of events. The objective is the maximization of the project net present value (NPV) as well as the minimization of project tardiness in the presence of a project due date. Develops hybrid scheduling rules with both NPV and tardiness considerations to enhance both objectives. Experiments extensively with a set of benchmark problems originally designed for the objective of minimizing the project duration. Demonstrates that thje hybrid rules developed here are superior in performance with respect to both objectives when compared with well known rules which are developed for the two objective of minimizing the project duration. Demonstrates that the hybrid rules developed here are superior in performance with respect to both objectives when compared with well‐known rules which are developed for the two objectives taken individually. Furthermore, the iterative algorithm improves the performance of all tested rules significantly.

Research on entrepreneurial ecosystems has advanced over recent years and has become a popular topic. Despite the interest, previous work has focused on entrepreneurial ecosystems in large cities in the United States. Ecosystems in small cities, underpopulated rural areas, university towns and outside the USA have not been considered much. This paper begins to address this deficit by reviewing three groups of literature.

From the review, the paper builds a conceptual framework to consider entrepreneurial ecosystems led by universities. After summarizing the literature on entrepreneurial ecosystems, entrepreneurial universities and entrepreneurship education, the paper suggests a conceptual framework outlying the structure, components and mechanisms that enable universities to operate as catalysts in the creation of entrepreneurial ecosystems.

It is evident that on many of the “ingredients” of a successful entrepreneurial ecosystem, a resource-constrained environment would have many gaps. Building an entrepreneurship ecosystem in such contexts would be inherently challenging. The model presented suggests that the presence of a university in such locations should enhance the prospects of progress but that the nature of the university itself would impact any outcomes. Universities that make concerted efforts to be entrepreneurial and that have entrepreneurship programmes have strategies available to them that can enhance entrepreneurship ecosystems over time.

The contribution of this paper is to show “how” a university and its entrepreneurship programme can operationally address deficits in a local ecosystem and how it might bring about positive change. The paper also opens new avenues for entrepreneurship education researchers.

Historically, scheduling of production activities has been one of the most important management problems. Scheduling involves the planning and the co‐ordination of the various activities to achieve the optimum utilisation of resources over a given time period. Production scheduling differs with the typology of the production systems. Various production systems that are encountered in practice are: continuous production, mass production, batch production, job shop production and projects. In this article, we attempt only to discuss the project scheduling problems.

Construction resources are often limited, and there is a need to ensure resources are carefully allocated to avoid resource wastage and/or project delay. While mathematical and heuristic models were developed to resolve the conflicts between activities, none of them has been proven to be totally satisfactory. The aim of this paper is to explore this.

In this paper, a novel heuristic method known as a pull‐driven approach (PDA) is proposed; and this approach encourages a lean consideration of resource supply and partnering between activities. Two case studies are presented to illustrate how PDA can be applied. A comparison with another heuristic approach, namely the ranked positional weight method is conducted to illustrate the efficiency of the proposed approach.

Using the PDA approach, all the activities strive to find matching partners to pull resources to their sides. The resources are, therefore, no longer passively pushed to a single activity with high ranking but a combination of activities that can demonstrate their superiority in resource utilisation will actively win the necessary resources.

This approach has an ability to deal with intermittent activities and locate the lean (no‐waste) supply level of each resource.