Benjamin Lev

A multi-objective bi-level location planning problem for stone industrial parks

This paper focuses on a stone industrial park location problem with a hierarchical structure consisting of a local government and several stone enterprises under a random environment. In contrast to previous studies, conflicts between the local authority and the stone enterprises are considered. The local government, being the leader in the hierarchy, aims to minimize both total pollution emissions and total development and operating costs. The stone enterprises, as the followers in the hierarchy, only aim to minimize total costs. In addition, unit production cost and unit transportation cost are considered random variables. This complicated multi-objective bi-level optimization problem poses several challenges, including randomness, two-level decision making, conflicting objectives, and difficulty in searching for the optimal solutions. Various approaches are employed to tackle these challenges. In order to make the model trackable, expected value operator is used to deal with the random variables in the objective functions and a chance constraint-checking method is employed to deal with such variables in the constraints. The problem is solved using a bi-level interactive method based on a satisfactory solution and Adaptive Chaotic Particle Swarm Optimization (ACPSO). Finally, a case study is conducted to demonstrate the practicality and efficiency of the proposed model and solution algorithm. The performance of the proposed bi-level model and ACPSO algorithm was highlighted by comparing to a single-level model and basic PSO and GA algorithms.

Optimal decision-making via binary decision diagrams for investments under a risky environment

This paper presents two methods for supporting investments and resource allocation in a constrained risky environment. These methods are based on the application of logical decision trees and binary decision diagrams as an approach that allows quantitative analysis of a qualitative study. The scenario considered in this paper is a decision-making process under risk environment, where stochastic variables are considered. The two novel procedures are introduced to facilitate the resource allocation as the objective of the decision-making process. The first procedure uses the analytic expression provided by binary decision diagrams as an objective function of a non-linear programing model. The second procedure introduces an importance measure that takes into account some external constraints, unlike the classical importance measures that only consider the topology of the tree. The first technique will optimise the outcomes and the second will provide a good approximation of the outcomes using simpler calculations.

Multi-attribute comprehensive evaluation of individual research output based on published research papers

This paper proposes a multi-attribute comprehensive evaluation method of individual research output (IRO). It highlights the fact that a single index can never give more than a rough approximation to IRO, and the evaluation of IRO is a multi-attribute complex problem. Firstly, an evaluation index system is established by determining evaluation attributes and choosing the appropriate bibliometric indicators. To address the multiple authorship problem, this paper develops an improved number-of-papers-published indicator. Following this, TOPSIS method is used to conduct a comprehensive IRO evaluation. Then this paper uses a case study to test the feasibility of the methodology. Finally, this paper discusses the effectiveness of the proposed method. Compared with traditional single-indicator evaluation approaches, the proposed multi-attribute evaluation takes more aspects into consideration, therefore it is able to effectively overcome the one-sidedness of a single indicator. The proposed method also has significant advantages compared with other comprehensive IRO evaluation methods.

Advanced Business Analytics

Consider consumers. In economic theory, they make choices to maximize their total utility. That’s all fine, but to truly optimize one’s choices to discover what’s best among the vast array of alternatives available is hard. Doing it well requires a lot of information and the ability to sort through that information to decide effectively. The result is that consumer decision-making is highly imperfect in practice and a great deal of potential value goes unrealized as a result. Business analytics helps capture this latent value by improving our choices as consumers: Google’s search engine provides fast, highly relevant web content, enhancing the value of your time online.

Exploring Determinants of Knowledge Sharing in a Social Network of Practice

Network of practice (NoP) operating on social media have been rapidly grown in recent years since the social media allows users not only to create contents, but also to interact with each other. A new type of NoP using social networking services (SNS) is defined as a social network of practice (sNoP). SNoP involves a collection of individuals who communicate, collaborate, and exchange knowledge openly with others sharing a common practice. Relatively little has been published focusing on the factors that influence the participation in knowledge sharing within the sNoP. This study focuses on the determinants of knowledge sharing in sNoP whose inquiry requires not only social theories, but also socio-technical views. Building on the social cognitive theory, the social capital theory, and the technology acceptance theory, this research-in-progress paper aims to explore how personal cognition, social capital, and technology acceptance attitude affect knowledge sharing in sNoP.

Hierarchical Supplier Selection Optimization with Multiple Items in Large-Scale Construction Projects

AbstractManagers of modern large-scale construction projects are under pressure to meet higher customer expectations with tighter budgets. Although they deal with numerous issues in the purchasing ...

Bi-level plant selection and production allocation model under type-2 fuzzy demand

Abstract This study is concerned with the plant selection and production allocation problem under the background of Original Equipment Manufacturing (OEM), which consists of a single controlling company regarded as the leader and multiple candidate plants as the followers. A bi-level programming model is proposed, where the plant selection optimization problem located at the upper level contains nested production allocation optimization problems positioned at the lower level. In this model, demands are described in terms of type-2 triangular fuzzy numbers. In order to handle the type-2 fuzziness, a general expectation reduction method which incorporates an attitude parameter is developed. This method produces different reduced fuzzy numbers based on varying optimistic-pessimistic degrees of decision makers. Then a parametric model based on cut sets of the reduced fuzzy numbers is put forward to make the original problem solvable. An interactive satisfaction degree method is employed to transform the bi-level model into a single level model and produce solutions. Finally, an illustrative example is presented to demonstrate the feasibility of the proposed model and the developed approach. Detailed sensitivity analysis is provided as well. The results show that the attitude of a decision maker has an affect on the objective values at both levels: if the decision maker is more optimistic about the demand, then larger objective values can be obtained. We also find that different settings of satisfaction degree will result in different strategies of plant selection and order allocation. If we want to increase the upper level satisfaction degree, then the lower level satisfaction degree need to be sacrificed.

Supply Chain Coordination with Group Buying Through Buyback Contract

Under group buying strategy, a retailer offers buyers price discount based on buyers’ aggregate purchase quantity. This paper studies the supply chain coordination issue with a supplier and a retailer that uses group buying mechanism when selling to customers. We demonstrate that a buyback contract can coordinate the supply chain under group buying, and how its contract terms critically depend on the quantity threshold above which group buying deal is activated.

A Class of Chance Constrained Linear Bi-Level Programming with Random Fuzzy Coefficients

In this paper, we consider a class of linear bi-level programming with random fuzzy coefficients, which has no mathematical meaning because of the uncertain factors. So in order to make it solvable, we introduced the linear chance constrained bi-level model. And some theorems are proposed to obtain the equivalent model. Then we employ the interactive programming technique to deal with the bi-level equivalent model. At last an illustrative example is present to show the efficiency.

Stochastic Decentralized Bi-Level Programming for Supplier Selection and Order Allocation

The aim of this paper is to present an integrated and practical optimization method for solving a supplier selection and order allocation problem by considering a hierarchical structure under randomness. To accomplish this, a stochastic expected linear bi-level programming model is developed based on the supplier selection and order allocation in the context of the home decoration company. In this model, the demand, price, defect rate and late delivery rate are considered as stochastic variables. The corporation managers, the leader in the hierarchy, seeks to optimize the total cost. The project teams, the followers in the hierarchy, mainly concern the factors which may affect the project schedule and quality. As it is very difficult to solve bi-level model even it is linear with single objective in each level, an interactive fuzzy programming is applied to transform the bi-level to a single level model which can be easily solved using LINGO. Finally, a case study is presented to demonstrate the applicability and efficiency of this method.