Arvind Mohais

Combining vehicle routing and packing for optimal delivery schedules of water tanks

This article describes a decision-support system that was developed in 2011 and is currently in production use. The purpose of the system is to assist planners in constructing delivery schedules of water tanks to often remote areas in Australia. A delivery schedule consists of a number of delivery trips by trucks. An optimal delivery schedule minimises cost to deliver a given total sales value of delivered products. To construct an optimal delivery schedule, trucks need to be optimally packed with water tanks and accessories to be delivered to a set of delivery locations. This packing problem, which involves many packing and loading constraints, is intertwined with the transport problem of minimising distance travelled by road. Such a decision-support system that optimises multi-component operational problems is of great importance for an organisation; it supports what-if analysis for operational and strategic decisions and trade-off analysis to handle multi-objective optimisation problems; it is capable of handling and analysing variances; it is easy to modify – constraints, business rules, and various assumptions can be re-configured by a client. Construction of such decision-support systems requires the use of heuristic methods rather than linear/integer programming.

Evolutionary approaches for supply chain optimisation: Part I: single and two‐component supply chains

Purpose – The purpose of this paper and its companion (Part II: multi‐silo supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In tis part, the paper aims to devote attention to single silo and two‐silo supply chains. It also aims to discuss three models. The first model is based on the winebottling real‐world system and exposes complexities of a single operational component of the supply chain. The second model extends it to two components: production and distribution. The last system is a real‐world implementation of the two‐component supply chain.Design/methodology/approach – Evolutionary approach is proposed for a single component problem. The two‐component experimental supply chain is addressed by the algorithm based on cooperative coevolution. The final problem of steel sheet production is tackled with the evolutionary algorithm.Findings – The proposed systems produce solutions better than solutions proposed by human...

Evolutionary approaches for supply chain optimisation. Part II: multi‐silo supply chains

Purpose – The purpose of this paper and its companion (Part I: single and two‐component supply chains) is to investigate methods to tackle complexities, constraints (including time‐varying constraints) and other challenges. In this part, attention is devoted to multi‐silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one‐to‐many and many‐to‐one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real‐world supply chain network.Design/methodology/approach – Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi‐silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy‐evolutionary algorithm is proposed to address the problem.Findings – The proposed systems pro...

Intelligent techniques for forecasting multiple time series in real‐world systems

Purpose – The purpose of this paper is to describe a real‐world system developed for a large food distribution company which requires forecasting demand for thousands of products across multiple warehouses. The number of different time series that the system must model and predict is on the order of 105. The study details the systems forecasting algorithm which efficiently handles several difficult requirements including the prediction of multiple time series, the need for a continuously self‐updating model, and the desire to automatically identify and analyze various time series characteristics such as seasonal spikes and unprecedented events.Design/methodology/approach – The forecasting algorithm makes use of a hybrid model consisting of both statistical and heuristic techniques to fulfill these requirements and to satisfy a variety of business constraints/rules related to over‐ and under‐stocking.Findings – The robustness of the system has been proven by its heavy and sustained use since being adopted...

Global Optimization in Supply Chain Operations

This chapter discusses some optimization issues from a business perspective in the context of the supply chain operations. We note that the term “global optimization” may have different meanings in different communities and we look at it from the business and classical optimization points of view. We present two real-world optimization problems which differ in scope and use them for our discussion on global optimization issues. The differences between these two problems, experimental results, the main challenges they present and the algorithms used are discussed. Here, we claim neither uniqueness nor superiority of the algorithms used, rather the main goal of this chapter is to emphasize the importance of the global optimization concept.

Comparison of cooperative and classical evolutionary algorithms for global supply chain optimisation

This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and two approaches were used for global optimisation: a classical evolutionary approach and a cooperative coevolutionary approach. The latter approach produced higher quality solutions due to its use of communication between silos. Additionally, a second problem was presented involving an existing Australian multi-factory sheet steel business.

A Fuzzy-Evolutionary Approach to the Problem of Optimisation and Decision-Support in Supply Chain Networks

This chapter deals with the problem of balancing and optimising the multi-echelon supply chain network of an Australian ASX Top 50 company which specialises in the area of manufacturing agricultural chemicals. It takes into account sourcing of raw material, the processing of material, and the distribution of the final product. The difficulty of meeting order demand and balancing the plants’ utilisation while adhering to capacity constraints is addressed as well as the distribution and transportation of the intermediate and final products. The aim of the presented system is to minimise the time it takes to generate a factory plan while providing better accuracy and visibility of the material flow within the supply chain. The generation of factory plans within a short period of time allows for what-if-scenario analysis and strategic planning which would not have been possible otherwise. We present two approaches that drive a simulation to determine the quality of the generated solutions: an event-based approach and a fuzzy rule-based approach. While both of them are able to generate valid plans, the rule-based approach substantially outperforms the event-based one with respect to convergence time and quality of the solution.

An Evolutionary Approach to Practical Constraints in Scheduling: A Case-Study of the Wine Bottling Problem

Practical constraints associated with real-world problems are a key differentiator with respect to more artificially formulated problems. They create challenging variations on what might otherwise be considered as straightforward optimization problems from an evolutionary computation perspective. Through solving various commercial and industrial problems using evolutionary algorithms, we have gathered experience in dealing with practical dynamic constraints. Here, we present proven methods for dealing with these issues for scheduling problems. For use in real-world situations, an evolutionary algorithm must be designed to drive a software application that needs to be robust enough to deal with practical constraints in order to meet the demands and expectations of everyday use by domain specialists who are not necessarily optimization experts. In such situations, addressing these issues becomes critical to success. We show how these challenges can be dealt with by making adjustments to genotypic representation, phenotypic decoding, or the evaluation function itself. The ideas presented in this chapter are exemplified by the means of a case study of a real-world commercial problem, namely that of bottling wine in a mass-production environment. The methods described have the benefit of having been proven by a full-fledged implementation into a software application that undergoes continual and vigorous use in a live environment in which time-varying constraints, arising in multiple different combinations, are a routine occurrence.

Comparison of cooperative, multiobjective cooperative and classical evolutionary algorithms for global supply chain optimisation

This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and three approaches were used for global optimisation: a classical evolutionary approach, a cooperative coevolutionary approach and a cooperative coevolutionary approach with non-dominated partner selection. The second approach produced higher quality solutions due to its use of communication between silos.

Comparison of different evolutionary algorithms for global supply chain optimisation and parameter analysis

This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and three approaches were used for global optimisation: a classical evolutionary approach, a cooperative coevolutionary approach and a coevolutionary approach with on the fly partner generation where the solution from the second component of the supply chain is generated deterministically based on the first one. The second approach produced higher quality solutions due to its use of communication between silos. Additional experiment was conducted to choose optimal species sizes.