An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

Abstract

Featured Application: Supplier chain management, Industry production, Efficient industry management, global business management, smart manufacturing. Abstract: Modern day industries strive to obtain long-term supplier integrations (SI) with potentially stronger supplier groups, to achieve fast and reliable production. This paper studies the process of selecting vendors, while simultaneously considering the aspects of random factors, multiple criteria, and efficiently reaching optimal solutions to improve the SI. A framework was developed that consists of three layers of expert opinions, supplier requirements, and multi-objective bee colony optimization. The model factors affecting the SI decision were explored from the comprehensive relevant literature, and these factors were shortlisted and prioritized. Routines for the modeled framework were coded by using the proposed algorithms which were implemented for a real-world problem from a manufacturing small and medium enterprise (SME) in Pakistan. Optimization of SI was carried out on an archived artificial bee colony (AABC). Its effectiveness was also evaluated by comparison with simple artificial bee colony (ABC) and particle swarm algorithms. The methodologically calculated results, obtained from simulation of a mathematically reinforced optimization framework, are highly beneficial for the industry, as well as local and international suppliers. A detailed and in-depth evaluation of suppliers was provided by the sensitivity analysis, which presented a more rigorous authentication and elaboration of the results. The presented framework is the first of its kind for the SMEs of Pakistan and can be applied with little modification to other industries. competitive business climate, companies cannot afford undue delays in supply orders. From small industries to mega projects, the supply chain plays an important role in achieving production goals [2].A sound number of these challenges can be successfully handled if enterprises can develop long-term win-win relationships with well-matched international and national prospective suppliers. Therefore, the concept of supplier integration (SI) has been recently proposed by different researchers to enable enterprises to conveniently cope with these dynamic supply chain business issues [3][4][5][6]. SI is the concept for improving the part of the supply chain between manufacturers and their respective suppliers of ingredients, raw materials, packaging, etc. By sharing information between parties, the suppliers and buyers are able to exercise judgment on costs, quantities, timing of deliveries, production streamline product flow, and to create a collaborative relationship [7]. However, this strategy is coupled with a number of problems. The demographic location of countries, their economic status, product categories, and the capabilities of suppliers are some of the main factors affecting the decision regarding selection of suppliers and long-term integration with the manufacturer [8].The literature shows that different factors...

An Event-Based Supply Chain Partnership Integration Using a Hybrid Particle Swarm Optimization and Ant Colony Optimization Approach

“…Our approach adapts an objective method to determine the index weights and creates several alternatives for partnership integration based on an objective method which considers both the difference and correlation between indicators. This is different from the approach in the work of Faroop and Qazi [5] which depends on the experts' experience which results in high subjectivity. They choose alternatives for integration by using sensitivity analysis for each factor individually and try to determine the outcome.…”

“…By considering the cost, customer services and capacity utilization balance of the supply chain, Fulya [14] built a multi-objective supply chain network optimization model using genetic algorithm. Faroop and Qazi [5] developed a framework for supplier integration and took into account factors like quality, cost, lead time, delivery and technical capability. Luan and Yao [28] established a multiple objective optimization model and developed a hybrid algorithm of a genetic algorithm and ant swarm optimization for a supplier selection problem, considering minimizing cost and maximizing quality, delivery capability, innovation and development capability.…”

“…That is, enterprises should plan to replace separate business strategies with a cooperation strategy among partners [4]. Through successful supply chain partnership integration, enterprises can develop long-term relationships with appropriate prospective suppliers for mutual benefit [5]. The benefits of such partnership integration can be attained through efficient linkage among various events and activities in the supply chain process [6].…”

“…The benefits of such partnership integration can be attained through efficient linkage among various events and activities in the supply chain process [6]. By exercising judgement on events, such as costs, quantities, timing of deliveries, service quality and so on, the suppliers and buyers can decide to create a collaborative relationship or not [5]. For buyers in e-commerce, they usually have limited information about the suppliers and need to take more risks, because they can neither physically examine products nor verify the reliability of the sellers in an open, virtual environment [7].…”

“…Other approaches mainly included proposing multi-objective function model according to different scenario defined by rules for factors such as the total expenses for the manufacturers, quality of product, total profit for the distribution center, and supplier service etc. [5,[14][15][16]. However, these models ignored the dynamic nature of the supply chain and considered the acting factors discretely.…”