A new management scheme to support reverse logistics processes in the agrifood distribution sector

During the last decades, reverse logistics and reuse of products have received growing attention as profitable and sustainable business strategies. Looking at the agrifood distribution sector, every day thousands of agrifood stores throw away large quantities of food product no longer suitable for sale. This "waste product", in the majority of cases, could still find new uses as animal feed or fertilizer. The return flow of food product is a typical problem of reverse logistics. This study proposes a new bi-modular scheme for managing the process of collection of "food waste" resulting from the agribusiness distribution sector and its subsequent distribution to livestock farms and collection centers located in the area of interest. The proposed management scheme consists of two modules: - module 1: to cluster the observed area into convenient collection sectors by means of clustering algorithms; - module 2: to identify optimal retrieval routes within each cluster by using Vehicle Routing models. The province of Cagliari in Sardinia (Italy) has been identified as test area. An extensive data collection process has been performed in order to collect the information necessary to portray the existing scenario. The following businesses have been recorded: grocery stores and supermarkets with at least 400 sqm of retail area, livestock farms with at least 200 heads of cattle, feed mills. A number of variables concerning location, type, size and demand data have been collected for each recorded unit.The management scheme has been implemented in a software platform and successfully applied in the test area. The outcome provides useful insights to stakeholders and suggests avenues for further research in the area in order to develop a more general and intuitive tool for managing reverse logistics processes in agrifood chains

Supply chain management: An opportunity for metaheuristics

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

Product Return Handling

In this article we focus on product return handling and warehousingissues. In some businesses return rates can be well over 20% andreturns can be especially costly when not handled properly. In spiteof this, many managers have handled returns extemporarily. The factthat quantitative methods barely exist to support return handlingdecisions adds to this. In this article we bridge those issues by 1)going over the key decisions related with return handling; 2)identifying quantitative models to support those decisions.Furthermore, we provide insights on directions for future research.reverse logistics;decision-making;quantitative models;retailing and warehousing

Optimization of Location-Routing for the Waste Household Appliances Recycling Logistics under the Uncertain Condition

Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. In recent years, due to the poor management of waste household appliances recycling logistics system, safety accidents occur frequently, which seriously harm the health and life safety of the society. This paper studies the risk management of recycling waste household appliances under uncertain conditions and establishes a risk measurement model under fuzzy population density. Considering the multi-stage and classification diversity of waste household appliances recycling logistics, the multi-objective location routing model and location - routing model are established respectively. Based on the model complexity analysis, the solution method of multi-objective model is designed. Finally, the validity of the model and algorithm is verified by examples and tests

Resolving forward-reverse logistics multi-period model using evolutionary algorithms

© 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

SIMULTANEOUS ROUTING AND LOADING METHOD FOR MILK-RUN USING HYBRID GENETIC SEARCH ALGORITHM

Milk-run methodology is proposed to manage the procurement of orders from suppliers. The heuristic solution methods in the literature generally apply stepwise approach to route and load the vehicles. In this study we propose a hybrid genetic local search algorithm which simultaneous solves vehicle routing and order loading problems. This is the main contribution of the study. We consider volume and weight capacities (multi capacitated) of different types of transportation vehicles (heterogeneous fleet). Because of high adaptability and easy utilization, genetic algorithms are the most preferred approach of meta-heuristics. The chromosome structure of the proposed genetic algorithm is constituted by random numbers to eliminate infeasibility. The best chromosome of each generation is improved using local search method during the algorithm runs. We applied the algorithm to a real manufacturing company that produces welding robots and other process automation equipment. The results showed the effectiveness of the algorithm

Operations Research for Green Logistics – An Overview of Aspects, Issues, Contributions and Challenges

The worldwide economic growth of the last century has given rise to a vast consumption of goods while globalization has led to large streams of goods all over the world. The production, transportation, storage and consumption of all these goods, however, have created large environmental problems. Today, global warming, created by large scale emissions of greenhouse gasses, is a top environmental concern. Governments, action groups and companies are asking for measures to counter this threat. Operations research has a long tradition in improving operations and especially in reducing costs. In this paper, we present a review that highlights the contribution of operations research to green logistics, which involves the integration of environmental aspects in logistics. We give a sketch of the present and possible developments, focussing on design, planning and control in a supply chain for transportation, inventory of products and facility decisions. While doing this, we also indicate several areas where environmental aspects could be included in OR models for logistics

Design Principles for Closed Loop Supply Chains

In this paper we study design principles for closed loop supply chains. Closed loop supply chains aim at closing material flows thereby limiting emission and residual waste, but also providing customer service at low cost. We study 'traditional' and 'new' design principles known in the literature. It appears that setting up closed loop supply chains requires some additional design principles because of sustainability requirements. At the same time however, we see that traditional principles also apply. Subsequently we look at a business situation at Honeywell. Here, only a subset of the relevant design principles is applied. The apparent low status of reverse logistics may provide an explanation for this. To some extent, the same mistakes are made again as were 20 years ago in, for instance, inbound logistics. Thus, obvious improvements can be made by applying traditional principles. Also new principles, which require a life cycle driven approach, need to be applied. This can be supported by advanced management tools such as LCA and LCC.reverse logistics;case-study;closed loop supply chains