Sohail S. Chaudhry

Vendor selection with price breaks

Abstract We study the problem of vendor selection where a buyer must choose order quantities to place with vendors in a multiple-sourcing network. The selection process is influenced by the price, delivery, and quality objectives of the buyer, as well as by the production or rationing constraints of the vendors. Vendors are assumed to offer price breaks which depend on the sizes of the order quantities. Each proce break schedule is characterized by two attributes: it represents either quantity discounts or surcharges, and is either cumulative (all-units) or noncumulative (incremental). We present linear and mixed binary integer programming models that provide unifying frameworks for models of vendor performance measures from the purchasing literature and models of price breaks from the Operations Research literature. We argue that our models offer viable approaches to vendor selection with price breaks because of the availability of commercial software packages for personal computers that provide flexible and efficient computational tools for solving the models. This implies that vendor selection with price breaks can be performed at the fingertips of purchasing managers.

Semantic Inference on Heterogeneous E-Marketplace Activities

An electronic marketplace (e-marketplace) is a common business information space populated with many entities of different system types. Each of them has its own context of how to process activities. This leads to heterogeneous e-marketplace activities, which are difficult to make interoperable and inferred from one entity to another. This study solves this problem by proposing a concept of separation strategy and implementing it through providing a semantic inference engine with a novel inference algorithm. The solution, called the RuleXPM approach, enables one to semantically infer a next e-marketplace activity across multiple contexts/domains. Experiments show that the cross-context/cross-domain semantic inference is achievable. This paper is an understanding of many aspects related to heterogeneous activity inference.

A model of a decision support system based on case-based reasoning for third-party logistics evaluation

: The social demands for third-party logistics (3PL) are further developing the model of supply chain management. The analysis of an effective approach for 3PL service supplier evaluation is given in terms of its direct relation to the operational efficiency and benefit of the service-demanding enterprise as well as its supply chain management. An analysis of the traditional academic theoretical results and practical methods for 3PL supplier selection indicates the deficiencies of this approach. In this paper, a case-based reasoning model framework is postulated for a 3PL evaluation and selection system. This work further expands upon the theoretical basis of this system and its reasoning process by discussing the advantages and practical value of this framework.

Stochastic vendor selection problem: chance-constrained model and genetic algorithms

We study a vendor selection problem in which the buyer allocates an order quantity for an item among a set of suppliers such that the required aggregate quality, service, and lead time requirements are achieved at minimum cost. Some or all of these characteristics can be stochastic and hence, we treat the aggregate quality and service as uncertain. We develop a class of special chance-constrained programming models and a genetic algorithm is designed for the vendor selection problem. The solution procedure is tested on randomly generated problems and our computational experience is reported. The results demonstrate that the suggested approach could provide managers a promising way for studying the stochastic vendor selection problem.

Piracy in cyber space: consumer complicity, pirates and enterprise enforcement

This article presents an overview of the growth of internet piracy in the global marketplace. The ethical perceptions (or lack of) of the younger generation is addressed, in terms of their willingness to consume counterfeit goods on the web. Firms face the task of educating the consumer that downloading music, software, movies and the like, without compensation, is unethical. This awareness is critical for decreasing the demand for counterfeit goods in the virtual marketplace, where a consumer can exhibit a rogue behaviour with a limited fear of prosecution. We address the pyramid of internet piracy, which encompasses sophisticated suppliers/facilitators, such as the Warez group. Recent sting operations, such as Operation Buccaneer, are also depicted to highlight successful tactical manoeuvres of enforcement agencies. An overview of the Digital Millennium Copyright Act and the No Electronic Theft Act is included to debate the controversy surrounding this legislation. A discussion of enterprise enforcement mechanisms and novel anti-piracy technology for cyberspace is provided to reveal some of the tools used to fight the pirates, such as innovations in digital watermarking and NECs recently announced video content identification technology. Enterprise information systems and its interdependence on the internet are also demanding new technologies that enable internet investigators to rapidly search, verify and potentially remove pirated content using web services. The quality of service of web services designed to efficiently detect pirated content is a growing consideration for new anti-piracy technology.

Solving a class of facility location problems using genetic algorithms

Locating p facilities to serve a number of customers is a problem in many areas of business. The problem is to determine p facility locations such that the weighted average distance traveled from all the demand points to their nearest facility sites is minimized. A variant of the p-median problem is one in which a maximum distance constraint is imposed between the demand point and its nearest facility location, also known as the p-median problem with maximum distance constraint. In this paper, we apply a fairly new methodology known as genetic algorithms to solve a relatively large sized constrained version of the p-median problem. We present our computational experience on the use of genetic algorithms for solving the constrained version of the p-median problem using two different data sets. Our comparative experimental experience shows that this solution procedure performs quite well compared with the results obtained from existing techniques.

Group decision-making in an unconventional emergency situation using agile Delphi approach

This paper presents a group decision-making approach for managing unconventional emergency situations using the internet. In unconventional emergency events, the expediency of developing a response strategy is of utmost importance in group decision-making environment. The research of this paper aims to rapidly develop and execute the response approach to the unconventional emergency events. We develop an agile Delphi decision-making platform based on ASP.NET. The platform proposed in the paper also provides the function of identifying risk preference and cognitive style of the decision-making experts, which allows experts to conduct appropriate alternative portfolio and enables researchers to find a new combination of experts to make the decision-making more efficient. This paper concludes with an empirical analysis in which the sedimentation event that occurred at Wuhan subway is illustrated using the proposed methodology.

Complex service design: A virtual enterprise architecture for logistics service

This paper presents an interdisciplinary systems approach to service modeling, design and deployment. The study is based on a longitudinal case study of the development process of a complex logistic service system based on an advanced logistics model. The case examines the development of a Virtual Enterprise Architecture (VEA) for an automatic high-speed transport and sorting system applied in airports for baggage handling. The study traces the evolution of the system from the early conceptual phases to a successful commercial service deployed at Changi, Singapore Airport. The study is conducted using a retrospective analysis of the case using a design science research approach. The paper evaluates and discusses the issues of creating and designing a new complex logistics service, distinct from the physical product, based on an advanced discrete event-based simulation model. The paper concludes by presenting a generalized and validated conceptual framework for a VEA based on an advanced simulation model. The paper thus contributes to the field of service systems and service management by identifying a novel approach to effective design of a new service.

The impact of team task and job engagement on the transfer of tacit knowledge in e-business virtual teams

This research focuses on the initiative to integrate team task and job engagement as related to the transfer of tacit knowledge. The aim of this study is to investigate if team task and job involvement enhances the overall work efficiency through transfer of tacit knowledge between e-business virtual teams. We develop a model that is based on the relationship between tacit degree of knowledge, job engagement, team tasks, and their effect on knowledge transfer. The conceptual model is validated with the data collected from a survey. The results indicate that the tacit knowledge does not have a positive effect on team tasks and job engagement, and the team tasks and job engagement have a significant positive effect on knowledge transfer. In addition, the study found that team tasks and job engagement have mediating effects in the process of tacit knowledge transfer. Also, we discuss the theoretical and practical implications and contributions of this study.

A genetic algorithm approach to solving the anti‐covering location problem

: In this paper we address the problem of locating a maximum weighted number of facilities such that no two are within a specified distance from each other. A natural process of evolution approach, more specifically a genetic algorithm, is proposed to solve this problem. It is shown that through the use of a commercially available spreadsheet-based genetic algorithm software package, the decision-maker with a fundamental knowledge of spreadsheets can easily set up and solve this optimization problem. Also, we report on our extensive computational experience using three different data sets.