Supply Chain Management Problems Research Articles

Supply chain risk analysis with mean-variance models: a technical review

Pioneered by Nobel laureate Harry Markowitz in the 1950s, the mean-variance (MV) formulation is a fundamental theory for risk management in finance. Over the past decades, there is a growing popularity of applying this ground breaking theory in analyzing stochastic supply chain management problems. Nowadays, there is no doubt that the mean-variance (MV) theory is a well-proven approach for conducting risk analysis in stochastic supply chain operational models. In view of the growing importance of MV approach in supply chain management, we review a selection of related papers in the literature that focus on MV analytical models. By classifying the literature into three major areas, namely, single-echelon problems, multi-echelon supply chain problems, and supply chain problems with information updating, we derive insights into the current state of knowledge in each area and identify some associated challenges with a discussion of some specific models. We also suggest future research directions on topics such as information asymmetry, supply networks, and boundedly rational agents, etc. In conclusion, this paper provides up-to-date information which helps both academicians and practitioners to better understand the development of MV models for supply chain risk analysis.

Warehouse location with production, inventory, and distribution decisions: a case study in the lube oil industry

In this paper, a supply chain management problem from a real case study is modeled and solved. A company in Pakistan wanted to outsource part of its warehousing activity to a third party logistics (3PL) provider. Consequently, the company had to decide on where to rent space in the 3PL warehouses. Knowing that such a strategic decision is affected by tactical and operational decisions, the problem is presented as a facility location problem integrating production, inventory, and distribution decisions. The problem is formulated as a mixed integer linear programming model which minimizes the total cost composed of location, distribution, production, and inventory costs. Several constraints specific to the situation and policy of the company were considered. A thorough analysis was done on the results obtained with respect to formulation efficiency, sensitivity analysis, and distribution of costs. In addition to the solution of the company problem, a set of 1215 problem instances was generated by varying five types of relevant costs in a full factorial manner. The solution of the generated problems always suggests to open in the same two locations and the integrality gaps averaged 0.062 % with a maximum of 0.102 %. On average, the major components of the total cost are production cost (96.6 %), transportation costs (2.7 %), and inventory holding costs (0.38 %). The total warehouse opening cost accounted for less than 0.05 % of the total costs.

Genetic Algorithm for Inventory Levels and Routing Structure Optimization in Two Stage Supply Chain

Several analytical models have been developed to solve the integrated production distribution problems in Supply Chain Management (SCM). In certain multi-stage service supply chain like blood banks, the term ‘production’ is referred as collection. It is often crucial to consider the inventory and distribution costs for successful decision making in multi-stage service supply chain. In this paper, the authors have explored this problem by considering a Two - Stage Collection - Distribution (TSCD) Model for blood collection and distribution that faces a deterministic stream of external demands for blood product. A finite supply and collection of blood at stage one Central Blood Bank (CBB) has been assumed. Blood is collected at stage one CBB and distributed to stage two Regional Blood Bank (RBB), where the storage capacity of the RBB is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the packed blood bags are stored which is used to meet the final demand of customer zone. During each period, the optimal collection rate at CBB, distribution rate between CBB and RBB and routing structure from the CBB to RBB and then to customer zone, must be determined. This TSCD model with capacity constraints at both stages is optimized using Genetic Algorithms (GA) and compared with the standard operations research software LINDO for small problems.

An Efficient Analytic Solution to a Supply Chain Management Problem

An Efficient Analytic Solution to a Supply Chain Management Problem

The System Dynamics Analyses of Bullwhip Effect in China Processed Oil Supply Chain

Bullwhip effect is a serious problem in supply chain management, it brings about production disorder, inventory imbalance, business process obstruction, waste of resources and market chaos. Our processed oil supply chain is so a large complicated system that bullwhip effect of it is more serious. To deal this problem, this paper discussed China processed oil supply chain, aim at maintaining processed oil supply, analysed the strength of bullwhip effect in the chain, proved delay is the basic reason of bullwhip effect and pointed out reducing delay is the key point to weaken bullwhip effect. We used system dynamics analysis and Anylogic simulation software to get the conclusion. Finally, we put forward some effective strategies to deal with bullwhip effect in China processed oil supply chain for improving the chain's operating efficiency.

Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands

This paper introduces a new hybrid algorithmic approach based on Particle Swarm Optimization (PSO) for successfully solving one of the most popular supply chain management problems, the Vehicle Routing Problem with Stochastic Demands (VRPSD). The VRPSD is a well known NP-hard problem in which a vehicle with finite capacity leaves from the depot with full load and has to serve a set of customers whose demands are known only when the vehicle arrives to them. A number of different variants of the PSO are tested and the one that performs better is used for solving benchmark instances from the literature.

A Centralized Model Predictive Control Strategy for Dynamic Supply Chain Management

Reducing the Bullwhip effect is one of several crucial problems in supply chain management. In this paper, a centralized Model Predictive Control (MPC) strategy is applied to control inventories in a 4 echelons supply chain. The single MPC controller used in this strategy optimizes globally and finds an optimal ordering policy for each node. The controller relies on a linear discrete-time state-space model to predict process output and the prediction can be approached by two multi-step predictors, which depend on measurability of the controller states. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming. Simulation results show that centralized MPC strategy can track customer demand and maintain a proper inventory position level with reduced Bullwhip effect.

The IOT Technologies based Medical Equipment SCM in the Hospital

The management of medical equipment supply chain is the important part of process management of the diagnosis in hospital. At present, our enterprise supply chain management of medical equipment is still in its infancy with poor operational efficiency, backward information system and low-level management. The technology of the Internet of Things based bar code technology and mobile terminal technology is the feasible and economical solution on hospital supply chain management. In this paper, it discusses problems of the medical equipment supply chain management and analyzes the possibility of medical equipment supply chain management by the Internet of Things, it constructs the model of supply chain management system based on Internet of things and introduces its application process and implementation.

Knowledge Flow in Supply Chain Manufacturing: Case Study in Food Manufacturing Firm

Abstract This paper discusses the knowledge acquisition problems faced in the Supply Chain Management (SCM) when acquiring knowledge among Supply Chain (SC) members, and it subsequently shows how modelling of knowledge types can be used as knowledge base to assist in solving SCM problems. This paper justifies the types of knowledge are useful in order to build knowledge bases to help decision makers and more importantly to help making decisions at the right time. The aim of this research is to explore knowledge types in the supply chain of food manufacturing firms. Our research method emphasizes content analysis and case study based on food manufacturing firm. As a result, we developed a model for the knowledge flow in SCM that advocates the knowledge that are relevant among SCM members. This model can dramatically save knowledge and, more fundamentally, allows for the knowledge engineers to build a knowledge based system based on this model.

Out-of-stock problem: possible classification schemes

An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We present available PCG data and discuss how to determine the importance of some features (fields), their interconnections and compare them with standard data fields used in other publicly accessible studies and recommendations from Efficient Consumer Response (ECR). We propose several models and algorithms to predict and solve Out of stock problem and at the end the computational results of these models are presented.

Large neighbourhood search applied to the efficient solution of spatially explicit strategic supply chain management problems

Supply chain management (SCM) has recently gained wider interest in both academia and industry given its potential to improve the benefits of a company through an integrated coordination of all its entities. Optimization problems in SCM are commonly cast as large scale mixed-integer linear programs (MILPs) that are hard to solve in short CPU times. This limitation is critical in spatially explicit SCM models since they require a large number of discrete variables to represent the geographical configuration of the network, which leads to complex MILPs. We present herein a novel solution method for this type of problems that combines the strengths of standard branch and cut techniques with the efficiency of large neighbourhood search (LNS). We illustrate the capabilities of this novel approach through its application to two case studies arising in energy applications: the design of supply chains (SCs) for bioethanol production and the strategic planning of hydrogen infrastructures for vehicle use.

Hybrid simulation based optimization approach for supply chain management

In this work, we propose a hybrid simulation optimization approach that addresses the problem of supply chain management. We formulated the problem as a mathematical model which minimizes the summation of production cost, transportation cost, inventory holding and shortage costs, subject to capacity and inventory balance constraints and propose a hybrid approach combining mathematical programming and simulation model to solve this problem. The main objective of this approach is to overcome the computational complexity associated with solving the underlying large-scale mixed integer linear problem and to provide a better representation of supply chain reality. The simulation-based optimization strategy uses an agent-based system to model the supply chain network. Each entity in the supply chain is represented as an agent whose activity is described by a collection of behavioral rules. The overall system is coupled with an optimization algorithm that is designed to address planning and scheduling level decisions.

Vehicle routing problem with time windows considering overtime and outsourcing vehicles

The vehicle routing problem with time windows (VRPTW) is an important problem in third-party logistics and supply chain management. We extend the VRPTW to the VRPTW with overtime and outsourcing vehicles (VRPTWOV), which allows overtime for drivers and the possibility of using outsourced vehicles. This problem can be applied to third-party logistics companies for managing central distributor-local distributors, local distributor-retailers (or customers), and manufacturers. We developed a mixed integer programming model, a genetic algorithm (GA), and a hybrid algorithm based on simulated annealing. The computational results demonstrate the efficiency of the developed algorithms. We also develop a decision support system for the VRPTWOV that is equipped with a vehicle route rescheduling function for realistic situations based on the GA.

Contributors

Contributors

The best vendor selection for conducting the recycled material based on a hybrid MCDM model combining DANP with VIKOR

Environmentally conscious manufacturing and product recovery (ECMPRO) has become an obligation of manufacturers, and it has been extended to be the policy and strategy of businesses. Producing recyclable products and using recycled materials are optimal strategies for ECMPRO. Vendor selection (VS) is one of the multiple criteria decision-making (MCDM) problems in strategic supply chain management. The purpose of this article is to propose how the best selection to conduct the recycled materials can be implemented for enhancing and increasing the efficiency of using resources in the manufacturing process through recycled materials VS. Aluminum composite panel (ACP) is a global product, and ACP companies in Taiwan use recycled materials in more than 80% for their products on a quantity basis. Therefore, we selected the ACP industry of Taiwan as an empirical model to study VS and to reveal methods of improving gaps in each criterion for achieving the aspired levels of performance. We use the MCDM model combining DEMATEL-based on ANP (called DANP) with VIKOR to solve the recycled materials VS problems of multiple dimensions and criteria that are interdependent, instead of the independent assumption of an analytic hierarchy process, for mimicking the real-world scenario.

Contributors

Contributors

An Efficient Genetic Algorithm for Solving the Order Allocation Problem

This paper aims to solve an order allocation problem in supply chain management (SCM). In this problem, the price fluctuation over consecutive time periods is known beforehand. Based on that assumption, clients can arrange their orders in advance to minimize the costs incurred in SCM, e.g., purchasing cost or inventory holding host. However, order allocations are subject to demand volumes, time-varying costs, suppliers’ capacities, inventory levels, and inventory costs, etc. It is difficult to make a correct decision immediately. With so many factors affecting the decision-making, traditional methods (e.g., dynamic programming) is very time-consuming. Therefore, a genetic algorithm cooperated with a local search is proposed. The experimental results show that the problem can be solved efficiently and near optimally.

A supply chain network design considering transportation cost discounts

This study addresses an integrated facility location and inventory allocation problem considering transportation cost discounts. Specifically, this article considers two types of transportation discounts simultaneously: quantity discounts for inbound transportation cost and distance discounts for outbound transportation cost. This study uses an approximation procedure to simplify DC distance calculation details, and develops an algorithm to solve the aforementioned supply chain management (SCM) problems using nonlinear optimization techniques. Numerical studies illustrate the solution procedures and the effects of the model parameters on the SCM decisions and total costs. Results of this study serve as a reference for business managers and administrators.

Managing perishability in production-distribution planning: a discussion and review

Managing perishability may represent a remarkable problem in supply chain management of a varied set of industries. In fact, perishability can influence, for example, productivity or customer service and it may happen to occur in one or more processes throughout the supply chain. In this paper a review on planning models that handle perishability issues in production and distribution is conducted. The contribution of this paper is three-fold. First, a new framework for classifying perishability models based on multiple process features is presented. Second, it draws the community attention to the importance of managing perishability in many different industries’ supply chains by showing its relevance and by reviewing the literature related to production and distribution planning. Finally, it points towards research opportunities so far not addressed by the research community in this challenging field.

Contributors

Contributors