A decision support model for handling customer orders in business chain
Introduction
The concept of chain stores originated in the United Kingdom but developed in the United States at the beginning of the twentieth century as a new form of retail. The largest companies that introduced this way of trading were Woolworths, American Stores, and United Cigar Stores [1]. Today, companies that use this form are Walmart, Tesco, McDonalds, KFC, Pizza Hut, Starbucks, etc. In general, in the basic form, a chain store is a group of retail shops or branches that sell the same range of products or services on equal terms, share the same logo and decor, and implement the same management and marketing. Individual branches are located in different parts of the city or urban agglomeration, but may also cover an entire country or many countries. The development of the Internet and the popularization of mobile devices resulted in further development and redefinition of this form of business in the field of, among others, introducing new distribution channels, new ordering methods, new forms of marketing and shortening supply chains. Another reason for the changes is the emergence of restrictions resulting from the SARS-CoV-2 pandemic, which has resulted in a significant increase in on-line sales with delivery to the customer as well as new chain store projects related to the delivery of food, new types of services, etc. All this has resulted in a greater demand for, e.g., transport, storage, and digital and communication resources. Hence, the rational (optimal) use of resources in the conditions of their temporary unavailability, subjecting them to additional sanitary procedures as well as the optimization of the customer order handling process have become crucial in this situation. Taking into account the “network” nature of this way of doing business and the above-mentioned conditions, managers making the right decisions in a short time becomes practically impossible without appropriate support for this process. Therefore, the main motivation for the research described in this article was to build a decision support model for handling customer orders based on the resources and structure of chain stores (Section 3), which would combine the problems of planning, resource allocation and routing. As a result of these studies, a model for handling customer orders was proposed, which enables decision support in the allocation of customer orders to selected production/distribution points, determining routes between production/distribution points and customers, and the selection of couriers for deliveries according to these routes. Also presented are two alternative ways of implementing the model based on the mechanisms of mathematical programming (verification of the correctness) and a proprietary approach integrating constraint logic programming and genetic algorithms (solving problems with real sizes). The model was tested using the example of a case study of a pizza chain [2].
Section snippets
Literature review
The considered problem of handling customer orders in chain stores/restaurants can be generally classified into the group of problems involving the simultaneous production and distribution of goods and services. This group includes problems related to transport, supply chain management, etc. A discussion of these types of problems and, more importantly, a review of their mathematical models, is presented in [3]. Most of these models are derived from operations research (OR) or simulation [4].
Illustrative example: pizza chain
Let us consider an exemplary pizza chain [2], which has its facilities/branches in various locations in the city or urban agglomeration. Each facility (pizza parlor) offers w products (pizzas, pasta, burgers, cakes, ice creams, etc.) from the same menu, which is prepared and served on site or delivered to the customer if desired. In this case, customers place orders via a mobile app, a central Internet application, or via a hotline, choosing the assortment, quantity and time and providing the
Mathematical model
To find answers to the questions posed, among others, in Section 3, automatically and in an acceptable time, a decision support model was proposed for the problem of handling orders in a chain store/restaurant. The model was formulated in the form of a set of constraints and a set of questions, the scope of which was extended (Table 1 and Table 2). The structure of the model allows for the future addition of further questions with the same set of constraints. When building the model, the
Implementation methods
Based on the set of constraints (1)…(16), the set of questions Q1…Q4, the parameters of the modeled problem (Table 3), and the values which were taken from the NoSQL database, a decision support model for handling customer orders was built. The developed model has been implemented in two ways; the methods of implementing the model in a simplified manner are presented in Fig. 2.
The first way (A) was to implement the model directly using AMPL (A Mathematical Programming Language). AMPL is an
Computational examples
In order to verify the proposed model (Section 5) and evaluation of both implementation methods, a number of computational experiments were carried out. All of them used a computer with the following parameters: Intel(R) Core(TM) i5-4200 M CPU @ 2.50 GHz 2.50 GHz; 8.00 GB RAM.
The experiments were conducted in two stages. In the first one, the decision support model was implemented using two methods (A) and (B), using small data instances (P1…P4). Particular data instances differed in the number
Conclusions
The analysis of the properties of the proposed model and the obtained results of computational experiments (P1…P4, E1…E15) leads to the following conclusions:
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The proposed decision support model (Section 4), due to its structure and properties, is universal and allows one to find answers to many key questions regarding business chain management.
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For the same set of constraints of the proposed model, a number of further questions can be formulated beyond Q1…Q4, e.g., related to the unavailability
CRediT authorship contribution statement
Paweł Sitek: SupervisionJarosław Wikarek: Formal analysis. Izabela Nielsen: Writing – review & editing..
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Paweł Sitek is Associate Professor at the Department of Electrical Engineering, Automatic Control and Computer Science of Kielce University of Technology in Poland. He obtained M.Sc degree in Electrical Engineering from Kielce University of Technology, Poland, Ph.D. from Silesian University of Technology, Poland and D.Sc. degree in Computer Sciences from the Wroclaw University of Technology, Poland. His research includes operation research, constraints programming techniques, production
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Paweł Sitek is Associate Professor at the Department of Electrical Engineering, Automatic Control and Computer Science of Kielce University of Technology in Poland. He obtained M.Sc degree in Electrical Engineering from Kielce University of Technology, Poland, Ph.D. from Silesian University of Technology, Poland and D.Sc. degree in Computer Sciences from the Wroclaw University of Technology, Poland. His research includes operation research, constraints programming techniques, production planning and scheduling, discrete optimization, decision support systems and artificial intelligence. He is an author and co-author over 170 manuscripts including: international journals (JCR), chapters in books and conference proceedings. He is an editor and reviewer for a number of international journals and conferences.
Jarosław WIkarek is Assistant Professor at the Department of Electrical Engineering, Automatic Control and Computer Science of Kielce University of Technology in Poland. He obtained M.Sc degree in Electrical Engineering from Kielce University of Technology, Poland, Ph.D. from Silesian University of Technology, Poland. His research includes operation research, constraints programming techniques, production planning and scheduling, discrete optimization, and decision support systems. He is an author and co-author over 145 manuscripts including international journals (including on JCR), chapters in books and conference proceedings. He is a reviewer for a number of international journals and conferences.
Nielsen Izabela is Professor at the Department of Materials and Production, Aalborg University in Denmark. She was born December 22, 1977 in Poland. She gained an M.Sc., Eng. at the Faculty of Management and Production Engineering, Opole University of Technology in 2001. In 2005 she obtained her Ph.D. with honors in the application of constraint logic programming techniques in production flow planning from the Faculty of Production Engineering, Warsaw University of Technology. In 2006 she obtained an award for her research work from the Polish Ministry of Science and Higher Education. Her research is primarily in the areas of planning, scheduling and optimization problems. She has a special emphasis on automated manufacturing, transportation, and production systems. She has published over 140 articles in journals, books and conferences. She is an Associate Editor of the International Journal of Industrial Engineering: Theory, Applications and Practice and European Journal of Industrial Engineering. Furthermore, she is an editorial board member of several reputed journals including the International Journal of Advanced Logistics and International Journal: Production & Manufacturing Research.
Grzegorz Bocewicz is Associate Professor at the Department of Electronics and Computer Science of Koszalin University of Technology in Poland. He obtained M.Sc. degree in Telecommunications from the Koszalin University of Technology, Poland, and a Ph.D. and D.Sc. degrees in Computer Sciences from the Wrocław University of Technology, Poland in 2006, 2007 and 2014, respectively. His research interests are in the areas of the operational research, decision support systems, and constraints programming techniques. He is an author and co-author over 150 manuscripts including two books, international journals, and conference proceedings.