Morteza Pourakbar

A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems

This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering quantities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series is presented by adding a Genetic Algorithm (GA) module for gaining of a window basis. Next, a back propagation neural network is used for defuzzification. The model can forecast the trends in fuzzy data. Then, an agent-based system is developed to minimize the total cost and to reduce the bullwhip effect in supply chains. The system can suggest the reasonable ordering policies. The results show that the propose system is superior than the previous analytical methods in terms of discovering the best available ordering policies.

Resilient supply chain network design under competition: A case study

This research, motivated by a real-life case study in a highly competitive automobile supply chain, experimentally studies the impact of disruption on the competitiveness of supply chains. The studied supply chain faces two major risks: disruption of suppliers and tough competition from competitors. Any disruption in upstream level of the supply chain leads to an inability to meet demand downstream and causes market share to be lost to the competitors. For such a setting, a resilient topology is redesigned that can recover from and react quickly to any disruptive incidents. To this aim, we speculate there are three policies that can be used to mitigate the disruption risk, namely keeping emergency stock at the retailers, reserving back-up capacity at the suppliers, and multiple-sourcing. The problem is addressed using a mixed integer non-linear model to find the most profitable network and mitigation policies. We design a piecewise linear method to solve the model. Based on the data extracted from an automotive supply chain, practical insights of the research are extracted in a controlled experiment. Our analysis suggests that implementing risk mitigation policies not only work to the advantage of the supply chain by sustaining and improving its market share but also benefit customers by stabilizing retail prices in the market. Using the case study, we analyze the contribution of each risk strategy in stabilizing the supply chains profit, market share, and retail price. Our analysis reveals that downstream “emergency stock” is the most preferable risk mitigation strategy if suppliers are unreliable.

A joint economic lot-size model for an integrated supply network using genetic algorithm

Competitive environment calls for cost reduction in tree-like inventory systems. One of the key elements to cost reduction of a supply chain is integration of inventory system. In this paper, we consider an integrated four-stage supply chain system, incorporating one supplier, multiple producers, multiple distributors multiple retailers. The aim of this model is to determine order quantity of each stage (from its upstream) and shortage level of each stage (for its downstream) such that the total cost of the supply chain to be minimized. Thus, for three possible situations of relation between supplier and producers replenishment intervals, an integrated inventory model, making joint economic lot-size of shortage, holding, ordering and setup costs in all stages is developed. Then a heuristic approach based on genetic algorithm for solving this problem is presented.

Developing exact and Tabu search algorithms for simultaneously determining AGV loop and P/D stations in single loop systems

There are some issues which have to be addressed when designing an automated guided vehicles system (AGVS) such as flow-path layout, traffic management, the number and the location of pick up and delivery points, vehicle routing and so on. One of the AGVS guide path configurations discussed in the previous researches includes a single-loop which is the subject of this paper. In unidirectional single loop systems, determining the loop for the motion of an AGV, and the location of pick up and delivery (P/D) stations in the cells, are prominent points which, when considered simultaneously, lead to better results than determining each one independently. However, in the literature it is proved that the problem of separately determining the shortest feasible loop is a NP-complete problem. In this paper, by considering a from-to chart and a block layout as the input of problem, we try to determine: (1) a single loop, with at least one shared edge with each cell, (2) the direction of the flow and (3) the location of P/D stations on the loop, all at the same time, in a way that the total travel distance on the loop be minimised. In this regard, first a new exact algorithm is presented and then three heuristic algorithms are developed utilising a Tabu search (TS) method. Solving randomly generated test problems shows that our exact algorithm is capable of solving small size problems; also all three TS algorithms work efficiently in solving problems that could not be solved by exact algorithms.

Customer differentiated end-of-life inventory problem

This paper deals with the service parts end-of-life inventory problem in a circumstance that demands for service parts are differentiated. Customer differentiation might be due to criticality of the demand or based on various service contracts. In both cases, we model the problem as a finite horizon stochastic dynamic program and characterize the structure of the optimal inventory policy. We show that when customers are differentiated based on the demand criticality then the optimal structure consists of time and state dependent threshold levels for inventory rationing. In case of differentiation based on service contracts, we show that in addition to rationing thresholds we also need contract extension thresholds by which the system decides whether to offer an extension to an expiring contract or not. By numerical experiments in both cases, we identify the value of incorporating such decisions in service parts end-of-life inventory management with customer differentiation. Moreover, we show that these decisions not only result in cost efficiency but also decrease the risk of part obsolescence drastically.

An Intelligent Agent-based System for Reduction of Bullwhip Effect in Supply Chains

This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering qualities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series, by adding a GA module for gaining of window basis, is presented. Next, a back propagation neural network is used for defuzzification. The model can forecast the trends of fuzzy data. To minimize the total cost and reduce the bullwhip effect, an agent-based system is developed. The system can propose the reasonable ordering policies. The results show that the proposed system is superior than the previous analytical methods in terms of discovering the best available ordering policies.

An exact static solution approach for the service parts end-of-life inventory problem

Abstract This paper studies the spare parts end-of-life inventory problem that happens after the discontinuation of part production. A final ordering quantity is set such that the service process is sustained until all service obligations expire. Also, the price erosion of substitutable or new generation products over time makes it economically justifiable to consider switching to an alternative service policy for repair such as swapping the old product with a new one. This requires the joint optimization of the final order quantity and the time to switch from repair to an alternative service policy. To the best of our knowledge, the problem has not been optimally solved yet either in its static or dynamic formulation. In the current paper, we solve its static version as a bi-level optimization problem. We investigate the convexity of the objective function and give a computationally efficient algorithm to find an exact optimal solution up to any given numerical error level ϵ > 0. We illustrate our approach on some numerical examples and compare our results with earlier works on this problem.

The Role of Customs in Securing Containerized Global Supply Chains

Customs has to deal with a massive number of containers arriving at ports. This massive flow of cargo provides an opportunity for organized crime infiltration. Risk management and the security of the supply chain has become a top priority for Customs administrations and for private firms. In this paper, we develop models that allow Customs to optimize its inspection process to target high-risk containers without hindering the flow of safe containers with extra delays at ports. The model characterizes optimal informational and physical inspection rates as a function of the risk factors attributed to containers. We use this model to analyze how an effective public–private partnership for risk and security management can be established between Customs and private firms.