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In several earlier issues of this journal, a multi‐criteria warehouse location problem was discussed with the focus of these articles on the solution methodology employed. The first article presented the needs of AJ Equipment Company to resolve a multiple objective warehouse location decision and to demonstrate the practical application of a multiple criteria model to the warehouse location problem. The solution methodology employed by Green et al. was to use a zero‐one goal programming model. This approach modelled the goals to which management had given priority and generated a single optimal solution.

Quality function deployment (QFD) is a structured approach to seek out customers, understand their needs, and ensure that their needs are met. QFD is probably the most important management tool developed to assure quality in new or improved products and services. As with any other tool, the quantum of benefits obtained from the use of QFD is proportional to the effectiveness of its use. To enhance the effectiveness and efficiency of QFD as a means to transfer the “voice of the customer” into design and production, a new comprehensive hierarchical framework for QFD planning process and a zero‐one goal programming model for the selection of design requirements are proposed. The hierarchical framework contributes to the strategic guidance and provides clear direction for QFD teams during the construction of the house of quality. The decision model assists in determining a set of design requirements that most effectively meet customer needs subject to limited resources and other organizational restrictions. An illustrative example is also provided to demonstrate the practical usage of the design selection model.

The purpose of this paper is to develop a framework for route selection in multimodal transportation which can reduce cost, lead time, risk and CO₂ emission in multimodal transportation systems.

This research proposes the development of a framework for route selection in multimodal transportation that includes a six-phase framework to select an optimal multimodal transportation route. The first phase is to collect the data of each route and select the origin and destination. The second phase is to calculate time and cost of each route by using a multimodal transport cost-model. In the third phase, the CO₂ emissions are calculated based upon the 2006 guidelines of Intergovernmental Panel on Climate Change. The fourth phase proposes an integrated quantitative risk assessment, analytic hierarchy process (AHP) and data envelopment analysis methodology to evaluate the multimodal transportation risk. The fifth phase is to prioritize criteria by using the AHP which can be used in the objective function. The final phase is to calculate the optimal route by using the zero-one goal programming.

The aims of the model are to minimize transportation costs, transportation time, risk and CO₂ emission.

The approach has been tested on a realistic multimodal transportation service, originating from Bangkok in Thailand to a destination at Da Nang port in Vietnam. The results have shown that the approach can provide guidance in choosing the lowest cost route in accordance with other criteria, and to minimize the CO₂ emission effectively.

The contribution of this research lies in the development of a new decision support approach that is flexible and applicable to logistics service providers, in selecting multimodal transportation route under the multi-criteria in term of cost, time, risk and importantly the environmental impact.

Project selection management is a matter of challenge for project-oriented organizations, particularly, if the decision-makers are confronted with limited resources. One of the main concerns is selecting an optimal subset that can successfully satisfy the requirements of the organization providing enough resources to the best subset of the project. The projects for which there are not enough resources or those requiring whole resources of the organization will collapse soon after failed to success. Therefore, the issue is in the risk of choosing a set of projects so that can make a balance in investment versus on collective benefit.

A model is presented for project selection and has been tested on the 37 available projects. This model could increase the efficiency of the whole subset of the project significantly in comparison to the other model and it was because of choosing a diverse subset of projects.

Provides a general framework for project selection and a diverse and balanced subset of projects to increase the efficiency of the selected subset. Also, reduces the impact of uncertainty risk on the project selection process.

For the purposes of project selection, any project whose results are uncertain is a risky project because, if the project fails, it will reduce combined project value. For example, a pharmaceutical company’s R&D project is affected by the uncertain results of a specific compound. If the company invests in different compounds, a failure with one will be offset by a good result on another. Therefore, with selecting a diverse set of projects, this paper will have a different set of risks.

This paper discusses the risk of selecting or being responsible for selecting a project under uncertainty. Most of the projects in the field of project selection generally consider the risks facing the projects or existing models that do not take into account the risk.

The author aims to review the fundamental concept of quality function deployment and to discuss the facts that the road to success for new product development is the identification of customers' requirements and their conversion into engineering design requirements. Thereafter, the author seeks to present an in‐depth review of the subject and to study five new cases on the topic of quality function deployment.

The paper discusses the key elements of quality function deployment and the fact that the vision for the development of a comprehensive quality system can be built on the principles of quality function deployment taking customer requirements into consideration and relating them to design requirements.

To make the product development task successful and bring competitive advantages to the core business, management must be committed to the needs of customers through marketing surveys and implementing these in the process of product development by converting them into engineering design requirements.

This article reviews quality function deployment and its extensions such as fuzzy QFD, AHP and QFD, statistically extended QFD, dynamic QFD, and other extensions. In addition, cases covering the topics of a ship of quality, cost‐design parameter modeling, an enhanced version of quality function development, financial factors and uncertainties in the product design process with fuzzy formulation, and a model for prioritizing and designing rule changes for the game of soccer, are also reviewed.

The purpose of this paper is to establish a mechanism for partner selection via adapting relative weights of criteria according to the priority of motivations for establishing strategic alliance.

The analytic network process (ANP) approach derived from the idea of the Markov chain is employed to deal with this dynamic situation and to establish a partner selection mechanism. With this approach, the priority of motivations and the relative importance of criteria are determined simultaneously.

Although choosing an appropriate partner is an important variable influencing success of alliance, attempts to identify a universal list of criteria and their corresponding relative importance which enterprises should employ when seeking a proper partner would be futile since the objectives of forging alliances vary depending on specific motivations. Based on this iterative review approach proposed in this paper, a proper weight setting for these criteria is available and will comply with the original motivation for establishing the strategic alliance. This is essential for selecting an appropriate partner for establishing an alliance that matches the original motivation.

The limitation of this research is the neglect of the possible inner dependence among criteria and sub‐criteria, although that can be coped with by choosing them properly.

The content of motivations and criteria as well as their priority and weightings may vary with different kinds of alliances or situations. The partner evaluation and selection mechanism proposed in this paper can meet different situations by adapting the relative weights of criteria and attributes according to the relationship between the criteria and motivations for every particular situation, thus enabling decision‐makers to think more comprehensively before conducting a selection process. If the priority of the motivations obtained from the mechanism is consistent with that set initially, the relative weights of these criteria can then be employed to evaluate the candidate partners in the selection mechanism. If it is not, the decision maker should reconsider the weighting process or measure again the relative weights for the criteria before conducting the evaluation and selection processes to avoid selecting an inappropriate partner that runs contrary to the original motivations.

The emphasis is on the interdependence between motivations and criteria for partner selection. This paper systematically deals with the interdependence of these two factors. Based on this iterative review approach proposed in this paper, a proper weight setting for these criteria is available and will comply with the original motivation for establishing the strategic alliance.

Addresses an actual problem of assigning customers to employees in a mail order firm. The management of the firm groups customers based on the first letter of the last name. To make assigning responsibility for handling customer orders easier, management specifies that no letter group can be broken up. In other words, all customers with last names beginning with the same letter must be assigned to the same employee. Management also desires a fair assignment among employees. A perfectly fair assignment entails assigning the same number of customers to every employee. A lower bound on the solution to the problem is established and two approaches to the problem are developed. First, it is formulated as a zero‐one goal programming problem and thus commercially available computer codes can be used to solve it. Second, a heuristic approach which assigns customers to employees based on rules similar to assembly line balancing is developed. The zero‐one goal programming approach yields slightly better results. However, it requires longer computer running time.

Construction companies typically allocate limited resources, i.e. human resources and funds, for marketing activities; so, they commonly face the problem of deciding in which marketing activities they should primarily invest and how much resource they should allocate for the selected marketing activities. Indeed, the problem of selecting the best set of marketing activities should be treated as a multi‐objective optimisation problem with multiple obligatory and flexible goals with different priorities, several interdependencies, and multiple constraints on resources. The main objective of this paper is to solve the problem of optimal allocation of scarce marketing resources using the combination of analytic network process (ANP) and zero‐one‐goal‐programming (ZOGP) models.

The research methodology of this paper mainly involves: reviewing the relevant literature on marketing and construction marketing; identifying the marketing activity alternatives in which construction companies may consider to invest; constructing an ANP model in order to calculate the importance weights of the different marketing activities; formulating a ZOGP model, which uses the weights obtained from the ANP model and considers the obligatory and flexible goals with different priorities, interdependencies and constraints on resources, that enables construction companies to optimize their scarce marketing resources; and conducting two case studies in order to illustrate how the proposed methodology works.

This paper provides construction companies with an integrated decision‐support methodology, which will assist them in allocating their limited marketing resources for different marketing activity alternatives in a more efficient way. The proposed methodology does not only consider the constraints on marketing resources and priorities of the goals that need to be satisfied but also show the deviations from the desired goals.

This paper is of benefit to construction companies as it offers an efficient and convenient tool that allows construction companies to optimise scarce marketing resources.

The purpose of this paper is to describe a method that has been set up to schedule preventive maintenance (PM) tasks for power and water plants with all constraints such as production and maintenance.

The proposed methodology relies on the zero-one integer programming model that finds the maximum number of power and water units available in separate generating units. To verify this, the model was implemented and tested as a case study in Kuwait for the Cogeneration Station.

An effective solution can be achieved for scheduling the PM tasks and production at the power and water cogeneration plant.

The proposed model offers a practical method to schedule PM of power and water units, which are expensive equipment.

This proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for power and water units in a cogeneration plant, effectively and complies with all constraints.